CN117410990A - Distributed energy distributed control method and system based on local calculation - Google Patents

Distributed energy distributed control method and system based on local calculation Download PDF

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CN117410990A
CN117410990A CN202311718732.XA CN202311718732A CN117410990A CN 117410990 A CN117410990 A CN 117410990A CN 202311718732 A CN202311718732 A CN 202311718732A CN 117410990 A CN117410990 A CN 117410990A
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CN117410990B (en
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韩震焘
王晶
杨国琛
张明理
宋卓然
梁毅
张泽宇
赫鑫
杨博
贾博
巨云涛
张晋奇
于宗民
叶鹏
王欢
王麒翔
张子信
尹婧娇
黄晓义
王贺蓉
金宇飞
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
North China University of Technology
Sichuan Energy Internet Research Institute EIRI Tsinghua University
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
North China University of Technology
Sichuan Energy Internet Research Institute EIRI Tsinghua University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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

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Abstract

The invention relates to the technical field of power distribution network optimal scheduling, and discloses a distributed energy distributed control method and system for a power distribution network based on local calculation, wherein the distributed energy distributed control method comprises the following steps: acquiring resource information of a power grid, and constructing a distributed control objective function and constraint conditions of the power distribution network; constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy; constructing a polyhedron internal approximation of a feasible domain of the model by a radiation function simplified control method, and solving an optimal solution upper bound of the decentralized control model; constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound. Reactive voltage control is achieved without communication. The method can effectively solve the defects of high-efficiency, reliable and safe distribution of electric energy and overhigh communication cost, can improve the robustness and reliability of a power grid, and can reduce the expected running cost to the maximum extent.

Description

Distributed energy distributed control method and system based on local calculation
Technical Field
The invention relates to the technical field of power distribution network optimal scheduling, in particular to a distributed energy distributed control method and system based on local calculation.
Background
The distribution network refers to a network system that distributes electric energy transmitted from a transmission network to end users. Most of the traditional power distribution network systems are centralized control modes, namely, a central control center monitors and controls the whole power grid. With rapid development and large-scale access of renewable energy sources such as wind energy and solar energy, the traditional centralized control mode cannot meet the requirements of an electric power system under the conditions of new energy source access and load side participation. Meanwhile, the complexity of the power system is continuously improved along with the increase of the accessed distributed power supply, and the traditional centralized control mode cannot meet the requirements of the control system on response speed, control precision and reliability. For these problems, distributed control techniques have evolved. The distributed control technology distributes control tasks to each distributed power supply and load, so that local coordination and global coordination are realized. The method can improve the energy utilization rate, reliability and stability of the power grid, reduce the dependence of the power grid on central control, and improve the robustness and the anti-interference capability of the power grid.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the method can effectively solve the defects that the traditional centralized control mode in the running and dispatching process of the power distribution network is difficult to meet the requirements on efficient, reliable and safe distribution of electric energy and the communication cost is too high, improve the robustness and reliability of the power distribution network, and simultaneously reduce the expected running cost to the maximum extent.
In order to solve the technical problems, the invention provides the following technical scheme: a distributed energy distributed control method based on local calculation comprises the following steps:
acquiring resource information of a power grid, and constructing a distributed control objective function and constraint conditions of the power distribution network;
constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy;
constructing a polyhedron internal approximation of a feasible domain of the model by a radiation function simplified control method, and solving an optimal solution upper bound of the decentralized control model;
constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound.
As a preferable scheme of the distributed energy source decentralized control method based on the local computing, the distributed energy source decentralized control method based on the local computing comprises the following steps: the resource information comprises line information, power information, energy storage information, current and voltage information of the power distribution network.
As a preferable scheme of the distributed energy source decentralized control method based on the local computing, the distributed energy source decentralized control method based on the local computing comprises the following steps: the distributed control objective function of the power distribution network comprises that the objective function of distributed control of the power distribution network is the sum of the minimum active network loss and the energy storage state;
；
wherein,representing the sum of the energy storage states +.>Representing active power loss of distribution network, < >>Representing the state of energy storage->Is a coefficient vector +_>Indicating line->Active loss of->Represents the line resistance +.>Representing the active power of the line,/->Representing reactive power of the line, +.>Representing the voltage amplitude at bus 0, +.>Indicating branch->Connected head-end node->Representing the total time period, +.>Indicating the total number of stored energy.
As a preferable scheme of the distributed energy source decentralized control method based on the local computing, the distributed energy source decentralized control method based on the local computing comprises the following steps: the constraint conditions comprise the following steps of network constraint of the power distribution network:
；
wherein,indicating line->Active power of transmission of +.>Indicating line->Reactive power of transmission, +.>Representation section->Voltage amplitude at>Representing node->Voltage amplitude of>Indicating line->Is (are) electric conduction>Indicating line->Susceptance of->Indicating line->Resistance of->Indicating line->Reactance of->Indicating line->Square of current amplitude>Indicating line->Middle node->And node->Phase angle difference between;
energy storage constraint:
；
；
wherein,is->State of charge of the energy storage device at the moment +.>Is the self-discharge coefficient of the energy storage device, +.>Representation->Charging power of the time-of-day energy storage device, < >>Representation->Discharge power of the time-of-day energy storage device, +.>Indicating the charging efficiency of the stored energy>Indicating the discharge efficiency of the stored energy>Indicating the rated capacity of the stored energy>Is indicated at +.>Energy storage->Is used for storing electric quantity;
；
wherein,representing the minimum value of the state of charge of the stored energy, < + >>Representing a maximum value of the stored state of charge;
distributed photovoltaic power generation output constraint:
；
wherein,representing the capacity of node i distributed photovoltaic, +.>Represents the photovoltaic utilization of node i of period T, < >>Representing the theoretical output of node i distributed photovoltaic at time t,/->Representing photovoltaic utilization limit,/->Representing the active output of the node i distributed photovoltaic; />Representing reactive power of the node i distributed photovoltaic;
load constraint;
；
wherein,representing node->Time->Active power demand of->Representing node->Time->Reactive power demand, < >>Representing node->、/>Active power injection->Representing node->、/>Reactive power injection is performed;
voltage amplitude constraint;
；
wherein,representing the upper limit of the node voltage amplitude,/->Representing a node voltage magnitude lower limit;
uncertainty model:
modeling active power injection and active and reactive power requirements of a photovoltaic inverter as discrete time random processes, perturbing the process with each busIn association, define perturbations as:
；
wherein,representing the set of perturbation functions at busbar i, +.>Representing load active disturbance, < >>Representing load reactive disturbance and photovoltaic active disturbance; />Representing a disturbance value;
the full disturbance trajectory is defined as:
；
wherein R represents a natural number,representing the total number of disturbances.
As a preferable scheme of the distributed energy source decentralized control method based on the local computing, the distributed energy source decentralized control method based on the local computing comprises the following steps: constructing a discrete time state space model describing collective dynamics of the power distribution network on the basis of the independent resource model; from the following componentsSubsystem composition, each subsystem->Indicating connection to busbar->Storing state for each subsystem>Input->：
；
The state equation of the whole system represents:
；
wherein the matrixExpressed as follows>Represents the Kronecker product;
；
definition at timeEach subsystem i determines the local control input based on the available information, proposes a fully decentralized disturbance feedback control strategy, which is +_ in time for each subsystem i>Is limited to:
；
wherein,a causal measurable function representing the local disturbance history, the local control strategy defining subsystem i is defined as:
；
wherein,for decentralized control strategy->Representing a family of all allowable decentralized control resources;
defining an optimal decentralized control problem:
；
wherein,representing a set of feasible states; />Representing a set of possible control inputs; />Representing a model objective function representing a minimization of expected active power loss versus storage state sum expectations, the expression:
；
wherein,representing the desire; />The method comprises the steps of inputting an equivalent coefficient matrix; />For perturbation equivalent coefficient matrix +.>Representing the disturbance.
Distributed energy distribution network based on local computingA preferred embodiment of the control method, wherein: the radiation function comprises the steps of simplifying a control problem through the radiation function, and constructing a polyhedron internal approximation of a feasible domain of the optimal decentralized control problem of the model; replacing the convex quadratic constraint with the linear constraint to derive a convex setIs a polyhedral approximation of:
；
deterministic constantExpressed as:
；
according toApproximating the optimal decentralized control problem as:
；
wherein the coefficient matrixDesignated by the underlying problem data, m represents the number of this inequality constraint,N u the total number of inputs is indicated,N x representing the total number of states +.>Respectively represent state variables +>Input variable->And random variable->Is a lower coefficient matrix of (a);
a subspace of a feasible decentralized affine control strategy is defined based on the following:
；
wherein S represents a constraint set of distributed control information,requiring a linear operator +.>Satisfying the information structure embedded in the feasible decentralized control strategy set Γ;
；
wherein,representation->Unit vector of>Representing optimal solution->As an auxiliary variable, +.>Representing a positive cone->Representing a semi-positive fixation cone->Representing the input of the system, M represents the second moment matrix of the system disturbance +.>Not only reversible but also positive, and->Indicating the desire.
As a preferable scheme of the distributed energy source decentralized control method based on the local computing, the distributed energy source decentralized control method based on the local computing comprises the following steps: the optimal solution further comprises the steps of constructing an outer polyhedron approximation of a feasible domain of a completely dispersed disturbance feedback control strategy, and solving an optimal value lower bound of the outer approximation;
for nodesThe convex quadratic constraint is equivalent to a linear inequality:
；
according toApproximating optimal decentralized control as:
；
wherein,a representative trace; />Representing an optimal solution, which is an optimal solution lower bound of optimal decentralized control; />Respectively represent state variables +>Input variable->And random variable->Is a coefficient matrix of the upper coefficient;
solving:
；
will beAs a choice of an optimal solution.
The system adopting the distributed energy distributed control method based on the local calculation is characterized in that: the model construction module is used for acquiring the resource information of the power grid and constructing a distributed control objective function and constraint conditions of the power distribution network; constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy; the analysis module is used for constructing a polyhedron internal approximation of a feasible domain of the model through a radiation function simplified control method and solving an optimal solution upper bound of the decentralized control model; constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound.
A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the method of any of the present invention.
A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the method of any of the present invention.
The invention has the beneficial effects that: the distributed energy distributed control method based on local calculation provided by the invention aims at the serious challenges brought to a power distribution system by increasingly increasing distributed resources: voltage amplitude at the bus rapidly fluctuates, substation tidal current foldback, and power quality problems due to the intermittence of renewable energy supplies. The control tasks are distributed to the distributed power supplies and the loads through a distributed control technology, and reactive voltage control is achieved without communication. The method can effectively solve the defects that the traditional centralized control mode in the running and dispatching process of the power distribution network is difficult to meet the requirements on efficient, reliable and safe distribution of electric energy and the communication cost is too high, improves the robustness and reliability of the power distribution network, and simultaneously reduces the expected running cost to the maximum extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for 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 an overall flowchart of a distributed energy distributed control method for a power distribution network based on local computing according to a first embodiment of the present invention;
fig. 2 is a local voltage decentralized control diagram in a distributed energy decentralized control method of a power distribution network based on local computation according to a first embodiment of the present invention;
fig. 3 is a three-node actual engineering topology diagram of a distributed energy distributed control method of a power distribution network based on local computation according to a second embodiment of the present invention;
fig. 4 is a graph of reactive voltage decentralized control effect of scenario 1 according to a distributed energy distributed control method based on local computation according to a second embodiment of the present invention, where a represents voltage control effect and b represents reactive adjustment condition;
fig. 5 is a graph of reactive voltage decentralized control effect of scenario 2 according to a distributed energy source decentralized control method based on local computing according to a second embodiment of the present invention, where a represents voltage control effect and b represents reactive adjustment condition.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1-2, for one embodiment of the present invention, a distributed energy source decentralized control method for a power distribution network based on local computing is provided, including:
s1: and acquiring the resource information of the power grid, and constructing a distributed control objective function and constraint conditions of the power distribution network.
Further resource information includes line information, power information, energy storage information, and current voltage information of the power distribution network.
The decentralized control objective function of the power distribution network is as follows:
；
the objective function of distributed control of the power distribution network is to minimize the sum of active network losses and energy storage states. Wherein the method comprises the steps ofAndrespectively representing the sum of the energy storage states and the active power loss of the power distribution network. Constraint (2)/(>Representing the state of energy storage->Is a coefficient vector. Constraint (3)/(x)>Indicating line->The specific functional expression is shown in formula (4). Constraint (4)/(A)>Represents the line resistance +.>Representing the active and reactive power of the line, +.>Representing the voltage amplitude at bus 0.
The distribution network decentralized control constraint conditions are as follows:
a. network constraints of the distribution network:
；
；
equation (5) and equation (6) are the result of linearization of the power flow constraint, whereIndicating line->Active power of transmission of +.>Indicating line->Reactive power of transmission, +.>Respectively represent node->Voltage amplitude at>Indicating line->Is/are>Representing node->Phase angle deviation of (2); formula (7) represents a square constraint of voltage, +.>Indicating line->Resistance and reactance of (a); formula (8) represents a line current constraint, +.>Indicating line->Is the square of the current amplitude of (a).
b. Energy storage constraint
；
In the method, in the process of the invention,is->The state of charge of the energy storage device at the moment; />Is->The state of charge of the energy storage device at time +1; />Is the self-discharge coefficient of the energy storage device; />And->Respectively indicate->Charging and discharging power of the energy storage device at any time;and->Respectively representing the charge and discharge efficiency of the stored energy; />Representing the rated capacity of the stored energy; />Is indicated at +.>Energy storage->Is used for storing electric quantity; />Indicating that the regulation step length is 1h.
In the method, in the process of the invention,and->Representing the minimum and maximum values of the stored state of charge.
c. Distributed photovoltaic power generation output constraint
；
In the method, in the process of the invention,the capacity of the distributed photovoltaic for node i; />Reactive power output at the moment of node i distributed photovoltaic t is provided;active output at the moment of node i distributed photovoltaic t; />Photovoltaic utilization for node i of period T, < >>The theoretical output force (the non-limit front output force) of the distributed photovoltaic of the node i at the moment t; />Is the minimum limit value of the photovoltaic utilization rate.
d. Load constraint
；
；
In the method, in the process of the invention,respectively represent node->Time->Active power and reactive power requirements of (a). />Respectively represent node->Active reactive power injection.
e. Voltage amplitude constraint
；
In the method, in the process of the invention,respectively representing upper and lower limits of node voltage amplitude. According to the specification of GB/T40427-2021, a node voltage constraint range is set to be 0.93UN-1.07 UN, wherein UN is rated voltage (per unit value is 1).
f. Uncertainty model
Active power injection and active and reactive power requirements of a photovoltaic inverter are modeled as discrete time random processes. The disturbance process is associated with each busIn association, define perturbations as:
；
the full disturbance trajectory is defined as:
；
wherein,representing the number of disturbance tracks as 1+3nT。
S2: and constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy.
On the basis of the established independent resource model, a discrete time state space model describing the collective dynamics of the power distribution network is constructed. The system consists ofSubsystem composition, whichIs>Indicating connection to busbar->Is a collection of resources. Storing local inputs for subsystems>The method comprises the steps of energy storage power generation condition and photovoltaic active power output:
；
the state equation of the whole system is expressed as follows:
；
wherein the matrixRepresenting disturbance factor, ++>Represents the Kronecker product, +.>Representing an n-order identity matrix:
；
definition at timeEach subsystem->Determining local control inputs based on available information, a fully decentralized disturbance feedback control strategy is proposed, i.e. for each subsystem +.>It is at time->Is limited to:
；
in the method, in the process of the invention,causal measurable function representing the history of local disturbances, defining subsystem +.>Is defined as +.>. Wherein->For decentralized control strategy->Is a family of all allowable decentralized control resources. Fig. 2 is a local voltage decentralized control.
The optimal decentralized control problem is defined as follows:
；
wherein,representing a set of feasible states; />Representing a set of possible control inputs; />Representing a model objective function representing a desire to minimize the sum of the expected active power loss and the stored state, the specific expression is as follows:
；
wherein,representing the desire; />The method comprises the steps of inputting an equivalent coefficient matrix; />Is a disturbance equivalent coefficient matrix.
S3: by means of a radiation function simplified control method, a polyhedral internal approximation of a feasible domain of the model is built, and an optimal solution upper bound of the decentralized control model is solved.
Considering that the model (23) is an infinite-dimensional convex problem, the calculation solution is difficult to carry out. The control problem is simplified by the radial function, and a polyhedral internal approximation of the feasible region of the model (23) is constructed.
The convex quadratic constraint (12) is replaced by the following linear constraint to derive a convex setIs a polyhedral approximation of:
；
thus, the deterministic constantCan be expressed as:
；
according toThe model (23) can be approximated as:
；
in the coefficient matrixMay be specified in terms of underlying issue data.
A subspace of a feasible decentralized affine control strategy is defined based on the following:
；
where S represents a constraint set of distributed control information,requiring a linear operator +.>The information structure embedded in the feasible set of decentralized control policies Γ is satisfied.
；
In the method, in the process of the invention,representation->The optimal value of the problem (29) is equal to the cost generated by the optimal affine control strategy of the problem (27). Adopts->Representing the optimal solution of problem (29), it is apparent that the optimal solution upper bound of problem (23), i.e.>Representing a positive cone->Representing a semi-positive fixation cone->Representing the input of the system, M represents the second moment matrix of the system disturbance, +.>Not only reversible but also positive, and->Indicating the desire.
S4: constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound.
Since the affine strategy calculated in S3 is suboptimal, the strategy induced suboptimal is constrained in this step by solving the lower bound of the optimal value of the problem (23). An outer polyhedral approximation of the feasible region of the problem (21) is constructed and the lower bound of the optimal value of the outer approximation is solved.
For nodesThe convex quadratic constraint (12) can be equivalent to the following linear inequality:
；
according toThe model (23) can be approximated as (31): />
；
Wherein,a representative trace; />Representing a suboptimal solution, which is an optimal solution lower bound for optimal decentralized control; />Respectively represent state variables +>Input variable->And random variable->Is a matrix of coefficients.
By usingRepresenting the optimal solution of problem (31), which is the lower boundary of the optimal solution of problem (23), the following solution can be obtained>Wherein->The solutions of (29) and (31), respectively, demonstrate that the affine strategy is a near-optimal solution choice.
A distributed energy distributed control system of a power distribution network based on local calculation is characterized in that: the model construction module is used for acquiring the resource information of the power grid and constructing a distributed control objective function and constraint conditions of the power distribution network; constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy; the analysis module is used for constructing a polyhedron internal approximation of a feasible domain of the model through a radiation function simplified control method and solving an optimal solution upper bound of the decentralized control model; constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include read only memory, magnetic tape, floppy disk, flash memory, optical memory, high density embedded nonvolatile memory, resistive memory, magnetic memory, ferroelectric memory, phase change memory, graphene memory, and the like. Volatile memory can include random access memory, external cache memory, or the like. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory or dynamic random access memory. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like.
The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
Example 2
Referring to fig. 3-5, for one embodiment of the present invention, a distributed energy distributed control method for a power distribution network based on local computing is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit computing and simulation experiments.
Firstly, the section selects a specific power system comprising 3 nodes for testing, and the test result and the actual condition are verified through an algorithm. The voltage control is carried out by using the local voltage and reactive power information of the nodes, the topological structure of the system is shown in the following figure 3, 1, 2 and 3 represent buses, wherein the node 1 is a balance node, the node 2 is provided with energy storage equipment, the node 3 is provided with distributed photovoltaic, a certain reactive power regulation capability can be provided for the system, and the voltage deviation is limited within a range of +/-5 percent, so that simulation verification is carried out.
The voltage of the node 3 is higher in the case of moderate load during the photovoltaic large-power period, and as shown in fig. 4a, the reactive power is correspondingly adjusted according to the voltage so as to control the voltage within the limit value. In case of a higher voltage, the voltage is gradually reduced by reducing the reactive output as shown in fig. 4b, and after 4 iterations, it is substantially stabilized at the rated voltage 1p.u, at which time the reactive output is no longer reduced, keeping the voltage stable. Wherein fig. 4a is the corresponding voltage amplitude after reactive power adjustment and fig. 4b is the amplitude of reactive power adjustment.
Under the condition that no photovoltaic output is generated but the load is in large demand, the voltage of the node 3 is lower, as shown in fig. 5a, under the condition that the voltage is lower, the reactive output simulation effect is increased, as shown in fig. 5b, under the condition that the reactive output is increased, the voltage gradually rises, and after 4 iterations, the voltage is basically stabilized at the rated voltage 1p. Wherein fig. 5a is the corresponding voltage amplitude after reactive power adjustment, and fig. 5b is the amplitude of reactive power adjustment.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (10)

1. The distributed energy distributed control method based on the local calculation is characterized by comprising the following steps of:
acquiring resource information of a power grid, and constructing a distributed control objective function and constraint conditions of the power distribution network;
constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy;
constructing a polyhedron internal approximation of a feasible domain of the model by a radiation function simplified control method, and solving an optimal solution upper bound of the decentralized control model;
constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound.
2. The distributed energy distribution network distributed energy source decentralized control method based on local computing according to claim 1, wherein: the resource information comprises line information, power information, energy storage information, current and voltage information of the power distribution network.
3. The distributed energy distributed control method based on local computing as claimed in claim 2, wherein: the distributed control objective function of the power distribution network comprises that the objective function of distributed control of the power distribution network is the sum of the minimum active network loss and the energy storage state;
；
wherein,representing the sum of the energy storage states +.>Representing active power loss of distribution network, < >>Representing the state of energy storage->Is a coefficient vector +_>Indicating line->Active loss of->Represents the line resistance +.>Representing the active power of the line,/->Representing reactive power of the line, +.>Representing the voltage amplitude at bus 0, i, j representing the branch +.>Connected head-end node->Representing the total time period, +.>Indicating the total number of stored energy.
4. A distributed energy distribution network distributed energy source decentralized control method based on local computing according to claim 3, wherein: the constraint conditions comprise the following steps of network constraint of the power distribution network:
；
；
wherein,indicating line->Active power of transmission of +.>Indicating line->Reactive power of transmission, +.>Representing node->Voltage amplitude at>Representing node->Voltage amplitude of>Indicating line->Is (are) electric conduction>Indicating line->Susceptance of->Indicating line->Resistance of->Indicating line->Reactance of->Indicating line->The square of the current amplitude value,indicating line->Middle node->And node->Phase angle difference between;
energy storage constraint:
；
wherein,is->State of charge of the energy storage device at the moment +.>Is the self-discharge coefficient of the energy storage device, +.>Representation->Charging power of the time-of-day energy storage device, < >>Representation->Discharge power of the time-of-day energy storage device, +.>Indicating the charging efficiency of the stored energy,indicating the discharge efficiency of the stored energy>Indicating the rated capacity of the stored energy>Is indicated at +.>Energy storage->Is used for storing electric quantity;
；
wherein,representing the minimum value of the state of charge of the stored energy, < + >>Representing a maximum value of the stored state of charge;
distributed photovoltaic power generation output constraint:
；
wherein,representing node i pointsCapacity of cloth type photovoltaic->Represents the photovoltaic utilization of node i of period T, < >>Representing the theoretical output of node i distributed photovoltaic at time t,/->Representing photovoltaic utilization limit,/->Representing the active output of the node i distributed photovoltaic; />Representing reactive power of the node i distributed photovoltaic;
load constraint;
；
wherein,representing node->Time->Active power demand of->Representing node->Time->Reactive power demand, < >>Representing node->、/>Active power injection->Representing node->、/>Reactive power injection is performed;
voltage amplitude constraint;
；
wherein,representing the upper limit of the node voltage amplitude,/->Representing a node voltage magnitude lower limit;
uncertainty model:
modeling active power injection and active and reactive power requirements of a photovoltaic inverter as discrete time random processes, perturbing the process with each busIn association, define perturbations as:
；
wherein,representing the set of perturbation functions at busbar i, +.>Representing load active disturbance, < >>Representing load reactive disturbance, +.>Representing an active disturbance of the photovoltaic; />Representing a disturbance value;
the full disturbance trajectory is defined as:
；
wherein R represents a natural number,representing the total number of disturbances.
5. The distributed energy distributed control method based on local computing as claimed in claim 4, wherein: constructing a discrete time state space model describing collective dynamics of the power distribution network on the basis of the independent resource model;
from the following componentsSubsystem composition, each subsystem->Indicating connection to busbar->Storing state for each subsystem>Input->：
；
The state equation of the whole system represents:
；
wherein the matrixExpressed as follows>Represents the Kronecker product;
；
definition at timeEach subsystem->Determining local control inputs based on available information, proposing a fully decentralized disturbance feedback control strategy +/for each subsystem>It is at time->Is limited to:
；
wherein,causal measurable function representing the history of local disturbances, defining subsystem +.>Is defined as:
；
wherein,for decentralized control strategy->Representing a family of all allowable decentralized control resources;
defining an optimal decentralized control problem:
；
wherein,representing a set of feasible states; />Representing a set of possible control inputs; />The object function of the model is represented,representing the minimization of the expected active power loss plus the storage state sum expectations, the expression:
；
wherein,representing the desire; />The method comprises the steps of inputting an equivalent coefficient matrix; />For perturbation equivalent coefficient matrix +.>Representing the disturbance.
6. The distributed energy distributed control method based on local computing as claimed in claim 5, wherein: the radiation function comprises the steps of simplifying a control problem through the radiation function, and constructing a polyhedron internal approximation of a feasible domain of the optimal decentralized control problem of the model;
replacing the convex quadratic constraint with the linear constraint to derive a convex setIs a polyhedral approximation of:
；
deterministic constantExpressed as:
；
according toApproximating the optimal decentralized control problem as:
；
wherein the coefficient matrixDesignated by the underlying problem data, m represents the number of this inequality constraint,N u the total number of inputs is indicated,N x representing the total number of states +.>Respectively represent state variables +>Input variable->And random variable->Is a lower coefficient matrix of (a);
a subspace of a feasible decentralized affine control strategy is defined based on the following:
；
wherein S represents a constraint set of distributed control information,requiring a linear operator +.>Satisfying the information structure embedded in the feasible decentralized control strategy set Γ;
；
wherein,representation->Unit vector of>Representing optimal solution->As an auxiliary variable, +.>Representing a positive cone->Representing a semi-positive fixed cone, v representing the input of the system, M representing the second moment matrix of the system disturbance
Not only reversible but also positive, and->Indicating the desire.
7. The distributed energy distributed control method based on local computing as claimed in claim 6, wherein: the optimal solution further comprises the steps of constructing an outer polyhedron approximation of a feasible domain of a completely dispersed disturbance feedback control strategy, and solving an optimal value lower bound of the outer approximation;
for nodesThe convex quadratic constraint is equivalent to a linear inequality:
；
according toApproximating optimal decentralized control as:
；
wherein,a representative trace; />Representing an optimal solution, which is an optimal solution lower bound of optimal decentralized control; />Respectively represent state variables +>Input variable->And random variable->Is a coefficient matrix of the upper coefficient;
solving to obtain:
；
will beAs a choice of an optimal solution.
8. A system employing the distributed energy distributed control method for a power distribution network based on local computing as claimed in any one of claims 1 to 7, characterized in that:
the model construction module is used for acquiring the resource information of the power grid and constructing a distributed control objective function and constraint conditions of the power distribution network; constructing an overall dynamic time state space model, and establishing a completely dispersed disturbance feedback control strategy;
the analysis module is used for constructing a polyhedron internal approximation of a feasible domain of the model through a radiation function simplified control method and solving an optimal solution upper bound of the decentralized control model; constructing an outer polyhedron approximation of a problem feasible domain, solving an optimal solution lower bound of a distributed control model, and acquiring an approximate optimal solution based on the upper bound and the lower bound.
9. A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the steps of the method of any of claims 1 to 7 when executed by a processor.
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