CN115333110A - Power distribution network-microgrid group collaborative distributed optimization scheduling method and system based on ADMM - Google Patents
Power distribution network-microgrid group collaborative distributed optimization scheduling method and system based on ADMM Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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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- 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
- H02J2300/28—The renewable source being wind energy
Abstract
The invention provides a power distribution network-microgrid group collaborative distributed optimization scheduling method and system based on ADMM, which comprises the following steps: dividing virtual areas based on different benefits of source load storage main bodies in a power distribution network-microgrid group, and constructing a distributed optimization overall framework; decomposing the total optimized dispatching objective function and the constraint condition by an alternating direction multiplier method, and constructing a distributed optimized dispatching model of the distribution network-micro power grid group considering multi-party benefit agents; and solving the distributed optimal scheduling model based on an alternating direction multiplier method. The invention provides a distribution network sub-area and micro-grid cluster controllable resource sub-area distributed optimal scheduling method based on different benefit subjects, which can keep better economy and has feasibility and comprehensive advantages.
Description
Technical Field
The invention belongs to the field of power distribution network-microgrid group collaborative optimization scheduling, and particularly relates to an ADMM-based power distribution network-microgrid group collaborative distributed optimization scheduling method and system.
Background
In recent years, with the gradual increase of energy and environmental problems, the rapid development of clean energy has become a consensus of the international society and an important energy strategy of China, and renewable energy and distributed power generation are more and more emphasized by people. Distributed power, while having significant advantages, presents its own problems. For example, a distributed power supply single machine is high in access cost and difficult to control, and as an effective consumption and management mode of the distributed power supply, a micro-grid as a micro energy system integrating 'source load storage' can deal with the safety and stability problems brought to a power distribution system after a large number of distributed power supplies are accessed through internal regulation, so that the receiving capacity and the utilization efficiency of the power distribution system to distributed renewable energy are improved, and the micro-grid is more and more widely applied to the power distribution system.
The micro-grid is a small power generation and distribution system formed by aggregation of a distributed power supply, an energy storage system, an energy conversion device, a load, a monitoring protection device and the like, and can be operated in a grid-connected mode with a superior grid and can also be operated in an isolated island mode. And the traditional single micro-grid has limited capability of consuming renewable energy sources and actively supporting the voltage/frequency of a power distribution network. As the number of micro-grids in the distribution network increases, if a plurality of geographically adjacent micro-grids have different investment subjects, different operation targets, or different renewable energy conditions; if the demand for interaction in aspects of electrical, control, information, capital and the like and the demand for realizing a common target through cooperation exist, a plurality of micro-grids can be connected through medium and low voltage distribution lines to form an integrated network, namely a micro-grid group, which is interconnected and mutually supplied.
At present, the problems of large calculation amount, difficulty in communication, difficulty in cooperation of benefit agents and the like exist in a power distribution network-microgrid cluster type scheduling mode. The distributed resources with the rapidly rising quantity can increase the computing pressure of the centralized computing cloud, so that the optimization center has higher hardware requirements on hardware devices such as computers and storage devices; a reliable communication path needs to be established between a large number of distributed resources and a centralized optimization center to realize interaction between mass cloud-end data and information, which leads to higher requirements of a power distribution system on communication band-pass, and in addition, the risk of errors is increased due to large-scale data transmission; a multi-party benefit agent often exists in a power distribution network-microgrid group related to source-load-storage resources, each benefit agent has a target aiming at self scheduling requirements, and the information of each agent does not have transparency due to privacy consideration, so that the traditional centralized regulation and control mode cannot meet the requirements. Aiming at the problems existing in the centralized scheduling, the optimization scheduling of the power distribution network-microgrid group by adopting a distributed algorithm is necessary.
Disclosure of Invention
The invention aims to overcome the defects of the microgrid cluster collaborative optimization scheduling and provides an ADMM-based power distribution network-microgrid cluster collaborative distributed optimization scheduling method. In order to achieve the above purpose, the invention adopts the following technical scheme:
the distribution network-microgrid group collaborative distributed optimization scheduling method based on the ADMM comprises the following steps:
dividing virtual areas based on different benefits of source load storage main bodies in a power distribution network-microgrid group to obtain a distributed optimization overall framework;
decomposing the objective function and the constraint condition of the total optimization scheduling by adopting an alternative direction multiplier method based on a distributed optimization overall framework, and constructing a distributed optimization scheduling model of the power distribution network-microgrid group based on a decomposition result;
and solving the distributed optimal scheduling model based on an alternating direction multiplier method, and optimizing and scheduling according to a solving result.
In the foregoing method, the distributed optimization overall framework includes a power distribution network region and a microgrid group controllable resource region, and the decomposing the total optimization scheduling objective function by using an alternating direction multiplier method based on the distributed optimization overall framework includes:
based on a distributed optimization overall framework, performing distributed solution on an overall optimization scheduling objective function through an ADMM algorithm to obtain an objective function of the power distribution network area and objective functions of all benefit subjects in the micro-grid controllable resource area;
wherein the objective function of the distribution network region is:
in the formula, C net The sum of the network loss cost of the power distribution network area side and the electricity purchasing cost of the main network,
being Lagrangian multipliers, P i t The original injection power of the boundary variable is coupled between the distribution network area and the micro-grid group controllable resource area,
injecting virtual power into controllable resource area belonging to micro-grid group, wherein rho is corresponding to alternative direction multiplier methodA penalty parameter of;
the objective function of each interest subject in the micro-grid controllable resource area is as follows:
in the formula, C res,i Is the sum of the costs of the gas turbine, the photovoltaic generator, the wind turbine, the energy storage and the flexible load scheduling at the controllable resource area side of the micro-grid group,
as Lagrange multiplier, P i t The original injection power of the boundary variable is coupled between the distribution network area and the micro-grid group controllable resource area,
and injecting virtual power into the controllable resource area belonging to the microgrid group, wherein rho is a penalty parameter corresponding to the alternative direction multiplier method.
In the foregoing method, the decomposing the constraint condition of the total optimization scheduling by using an alternating direction multiplier method based on the distributed optimization overall framework includes:
and decomposing the constraint condition of the total optimization scheduling by adopting an alternating direction multiplier method based on a distributed optimization overall framework to obtain the constraint condition of the power distribution network area, the constraint condition of the micro-grid group controllable resource area and coupling constraint.
In the above method, the constraint conditions of the distribution network region are as follows:
the input active power of any node in the network is equal to the sum of the transmission active powers of all connection lines of the node; the input reactive power of any node in the network is equal to the sum of the transmission reactive powers of all connection lines of the node; the branch power flow between the nodes i and j is equal to the difference of the branch conductance multiplied by the product of the square of the voltage of the node i and the product of the voltage of the node i, the voltage of the node j and the angle difference cosine value of the voltage of the nodes i and j, and then the product of the voltage of the node i, the voltage of the node j, the sine value of the angle difference sine value of the voltage of the nodes i and j is subtracted; the sum of the active power flowing into and out of the node i is 0;
the branch power flow between the nodes i and j is positioned between the upper limit and the lower limit of the branch power flow, and the voltage amplitude of the node i is positioned between the upper limit and the lower limit of the voltage;
in the formula (I), the compound is shown in the specification,
respectively representing the active power and the reactive power flowing into the node i at the moment t,
representing the electrical load power, V, of node i at time t i t 、
Representing the voltage at node i at time t, G ij 、B ij Is the conductance and susceptance, theta, between nodes i, j at time t t ij The phase angle difference of the voltage of the j node at the t moment i,
representing the branch power flow between the nodes i and j at the time t
Respectively represent the upper and lower limits, P, of the voltage at the node i at time t L,max,i-j 、P L,min,i-j Respectively representing the upper limit and the lower limit of the branch power flow between the nodes i and j at the time t.
In the method, the constraint conditions of the microgrid group controllable resource area are as follows:
the active output of the microgrid group m is between the maximum active output and the minimum active output, and the actual output of the microgrid group m containing controllable resources at the time t minus the output of the microgrid group m at the time t-1 is not more than the maximum and minimum constraints of climbing;
in the formula (I), the compound is shown in the specification,
respectively represent the maximum output and the minimum output of the microgrid group m containing controllable resources at the moment t,
representing the actual output r of the microgrid group m containing controllable resources at the moment t t m,up 、r t m,down Respectively representing the maximum climbing rate and the maximum slip rate of the micro-grid group m with active climbing constraint at the time t, wherein delta t is the climbing calculation interval time;
in the controllable resource, the operation constraints of the energy storage unit are as follows:
the active output of the energy storage unit k is between the maximum active output and the minimum active output, and the energy charged and discharged in the time of subtracting delta t from the energy storage of the energy storage unit k at the time of t-1 cannot exceed the upper limit and the lower limit of the energy storage unit k;
in the formula, k is the number of the energy storage units,
respectively represents the maximum and minimum output force of the energy storage unit k at the moment t,
respectively represent the upper limit and the lower limit of the stored energy of the energy storage unit k at the moment t,
representing the actual power of the energy storage unit k at time t,
representing the actual energy storage of the energy storage unit k at the moment t, wherein delta t is the charging and discharging calculation interval time, and eta is the charging and discharging efficiency;
the coupling constraints are:
coupling boundary variables on the power distribution network side of the node i are equal to coupling boundary variables on the controllable resource side of the microgrid group, and the coupling boundary variables on the controllable resource side of the microgrid group are equal to active variables of all controllable resources passing through the node i
Summing;
in the formula, P i t For coupling boundary variables, K, on the distribution network side i The controllable resource quantity of the microgrid group under the node i,
coupling boundary variables on the controllable resource side of the microgrid cluster,
the active variables of the controllable resources of the inode are shown.
In the above method, solving the distributed optimal scheduling model based on the alternating direction multiplier method includes:
based on an alternative direction multiplier method, independently solving an optimization model of a power distribution network region and a microgrid group controllable resource region;
performing exchange coupling variables;
until the iteration convergence condition is met:
where ε represents the convergence accuracy and k represents the number of iterations.
In the method, the specific process of solving includes:
step 1: for variable P in optimization model i t (k),
Initializing and applying lagrange multiplier
And carrying out initial setting on a penalty function rho, wherein the iteration times k =1;
step 2: independently solving the optimization model of the controllable resources of each microgrid group to obtain an optimization variable meeting the target of the optimization model, and coupling the key boundary variables
Transmitting the data to a power distribution network through a grid-connected point;
and step 3: the method comprises the steps of solving the nonlinear optimization problem of the power distribution network through a particle swarm algorithm by utilizing boundary coupling data obtained by the power distribution network and combining an optimization model of the power distribution network, and then optimizing the obtained P i t (k) Transmitting the data to a controllable resource area of the microgrid group to complete coupling information interaction;
and 4, step 4: verifying a convergence condition; if so, finishing iteration and outputting a microgrid group controllable resource scheduling result; if the iteration is not converged, returning to the step 2, repeating the solving process, and updating the Lagrange multiplier according to the following formula, wherein the iteration times k = k + 1:
a scheduling system, comprising:
a first module: the method is configured for constructing a distributed optimization overall framework, and specifically, virtual region division is carried out on the basis of different benefits of source load storage main bodies in a power distribution network-microgrid group, a power distribution network region is formed by all nodes of the power distribution network, and a controllable resource region of the microgrid group comprises four microgrid groups;
a second module: the method comprises the steps that a total optimization scheduling objective function and constraint conditions are decomposed through an alternating direction multiplier method, and a distributed optimization scheduling model of a power distribution network-micro power grid group considering multi-party benefit agents is constructed;
a third module: and the method is configured to solve the distributed optimization scheduling model based on the alternative direction multiplier method and optimize scheduling according to a solving structure.
An electronic device, a computer-readable storage medium storing computer-executable instructions; and one or more processors coupled to the computer-readable storage medium and configured to execute the computer-executable instructions to cause the apparatus to perform the above-described methods.
A readable storage medium storing computer executable instructions which, when executed by a processor, configure the processor to perform the method described above.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a comprehensive evaluation method and a comprehensive evaluation system for a power grid peak regulation and frequency modulation-oriented energy storage power station, which comprise the following steps: dividing virtual areas based on different benefits of source load storage main bodies in a power distribution network-microgrid group, and constructing a distributed optimization overall framework; decomposing the total optimized dispatching objective function and the constraint condition by an alternating direction multiplier method, and constructing a distributed optimized dispatching model of the distribution network-micro power grid group considering multi-party benefit agents; and solving the distributed optimal scheduling model based on an alternating direction multiplier method. The invention provides a distributed optimal scheduling method of distribution network sub-areas and micro-grid cluster controllable resource sub-areas based on different benefit subjects aiming at the problems of large calculation amount, difficult communication, difficult cooperation of benefit subjects and the like existing in a distribution network-micro-grid cluster centralized scheduling mode, further improves the consumption capacity and the power supply reliability of a distribution system to distributed energy, realizes the coordination and complementation of power generation resources, enhances the operation stability and the reliability, reduces the operation cost of the system, improves the energy utilization efficiency, can keep better economy, and has feasibility and comprehensive advantages.
Drawings
Fig. 1 is a schematic flow chart of an ADMM-based power distribution network-microgrid group cooperative distributed optimization scheduling method provided by the present invention;
fig. 2 is a schematic view of a virtual area decomposition framework of a distribution network-microgrid group provided by the present invention;
FIG. 3 is a schematic diagram of a solution flow of a distributed optimal scheduling model provided by the present invention;
FIG. 4 is a schematic diagram of a network structure according to an embodiment of the present invention;
fig. 5 is a graph of the output results of the controllable resources of the distribution network-microgrid cluster in 96 scheduling periods in a day according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the present invention provides an ADMM-based power distribution network-microgrid group collaborative distributed optimization scheduling method, which specifically includes:
1. the method comprises the following steps of performing virtual region division based on different benefits of source load storage main bodies in a power distribution network-microgrid group, and constructing a distributed optimization overall framework, wherein the method specifically comprises the following steps:
the distribution network area is composed of all nodes of the distribution network, the controllable resource area of the micro-grid group comprises four micro-grid groups, and each micro-grid group respectively comprises different types of controllable resources including a gas turbine, a wind turbine, a photovoltaic system, an energy storage system, a flexible load and the like. Different from the traditional optimization mode, each virtual area can flexibly set an independent objective function, and optimization scheduling solution is respectively carried out in the sub-areas so as to meet the requirements of each benefit subject. The specific virtual area decomposition framework is shown in fig. 2.
The cooperative optimization of each virtual area can be realized only by exchanging coupling boundary variables, and because the controllable resources of each microgrid group are directly connected with the power distribution network, the injection power of the resource grid-connected point can be used as the coupling variable at the original injection power P i On the basis of the method, injection virtual power X belonging to a controllable resource area of a micro-grid group is added i And the decoupling of the controllable resource side of the micro-grid group and the power distribution network side is realized.
2. Decomposing the total optimized scheduling objective function and the constraint condition by an alternating direction multiplier method, and constructing a distributed optimized scheduling model of the microgrid group considering the multi-party benefit agent;
decomposing the total optimized scheduling objective function and the constraint condition by an alternating direction multiplier method, and constructing a distributed optimized scheduling model of the microgrid group considering the multi-party beneficial agent, wherein the distributed optimized scheduling model specifically comprises the definition of the distributed optimized scheduling objective function and the definition of the distributed optimized scheduling constraint condition.
According to the virtual partition result, the whole power grid is divided into a power distribution network area and a micro-grid group controllable resource area (including a plurality of benefit subjects such as photovoltaic, fans, gas turbines, energy storage and flexible loads), and a total objective function is also required to be decomposed into two parts of the power distribution network area and the micro-grid group controllable resource area on a mathematical model.
In particular, the distribution network region comprises mainly a network loss charge C loss And the electricity purchasing cost C from the main network Grid The microgrid group controllable resource area mainly comprises the cost C of the gas turbine MT Photovoltaic cluster scheduling cost C PV Wind turbine generator dispatching cost C WT Energy storage dispatching cost C ESS Flexible load dispatching cost C DR The following are:
C net ＝C Grid +C loss
C res ＝C MT +C ESS +C DR +C PV +C WT
in particular, the cost function for each controllable resource in the microgrid group area is defined as follows:
gas turbine power generation cost model:
C MT for the real-time power generation cost of the gas turbine, P t MT,j Active power of the jth gas turbine at time t, N MT Is the total number of gas turbines, η t MT,j The value of the power generation efficiency is.
Energy storage scheduling cost model:
in the formula, C ESS Cost of real-time scheduling for electrical energy storage, c ESS Cost per unit of stored energy for electricity, N ESS The total amount of the stored energy for electricity,
and
respectively representing the charging and discharging power of the electric energy storage at the moment t.
Flexible load scheduling cost model:
in the formula, C DR Real-time scheduling of costs for flexible loads, c DR Represents the unit scheduling cost of the flexible load,
active regulating quantity representing flexible load at t moment。
The main network electricity purchasing cost model comprises:
in the formula, C Grid The real-time electricity purchasing cost of the active power distribution network is reduced,
representing the unit price of time-sharing electricity purchase, P t Grid Representing the amount of power purchased from the main grid at time t.
Photovoltaic dispatch cost model:
a fan scheduling cost model:
in the formula, C PV Cost for photovoltaic real-time scheduling, C WT For photovoltaic real-time scheduling costs, c PV And c WT Respectively the unit scheduling cost of the photovoltaic power station and the fan.
Loss-to-network cost model:
in the formula, N node Is the total node number, V t i 、V t j The voltages at the i and j nodes at time t,
is the sum of the end nodes with node i as the head, G ij Is the conductance between the i and j nodes, θ t ij And the phase angle difference of the voltage of the nodes i and j at the time t.
In particular, according to the cost model described above, a lagrangian multiplier is added to the separable objective function F to construct an objective function in its lagrangian form:
secondly, carrying out distributed solving on the separable objective function through an ADMM algorithm, wherein the objective function of the distribution network area is as follows:
objective functions of all benefit subjects in the micro-grid group controllable resource area are as follows:
in particular, the distributed optimized scheduling constraints include:
1) Constraint conditions of the distribution network region:
in the formula (I), the compound is shown in the specification,
respectively representing the active power and the reactive power flowing into the node i at the moment t,
representing the electrical load power at node i at time t, G ij 、B ij Is the conductance and susceptance, theta, between nodes i, j at time t t ij The phase angle difference of the voltage of the j node at the t moment i,
respectively representing the upper and lower limits, P, of the voltage at node i at time t L,max,i-j 、P L,min,i-j Respectively representing the upper limit and the lower limit of the branch power flow between the nodes i and j at the time t.
2) Constraint conditions of the microgrid group controllable resource area are as follows:
in the formula (I), the compound is shown in the specification,
respectively represents the maximum and minimum output of the microgrid group m containing controllable resources at the moment t, r t m,up 、r t m,down Respectively representing the maximum climbing rate and the slip rate of the micro-grid group m with active climbing constraint at the time t.
In addition, in the controllable resources, the operation constraints of the energy storage unit are as follows:
in the formula (I), the compound is shown in the specification,
respectively represents the maximum and minimum output force of the energy storage unit k at the moment t,
the energy storage units respectively represent the upper limit and the lower limit of the energy storage unit k at the time t, and eta is the charge-discharge efficiency of the energy storage unit k.
3) Coupling constraint:
in the formula, P i t For the coupling boundary variable on the distribution network side,
for the coupling boundary variable at the controllable resource side of the micro-grid group, the active variable of the controllable resource of each micro-grid group is used
And the sum realizes the calculation.
3. Solving the distributed optimal scheduling model based on an alternating direction multiplier method, which specifically comprises the following steps:
in the optimization model, the optimization models of the power distribution network region and the microgrid group controllable resource region need to be solved independently and exchange coupling variables until an iteration convergence condition is met:
where ε represents the convergence accuracy and k represents the number of iterations.
A flowchart for solving the distributed optimal scheduling model based on the alternative direction multiplier method is shown in fig. 3, and the specific process of solving includes:
step 1: for variable P in optimization model i t (k),
Initialization is performed, and in addition, lagrange multipliers need to be applied thereto
And a penalty function rho is initially set, and the iteration number k =1.
Step 2: the optimization model of the controllable resources of each microgrid group is solved independently to obtain the optimization variables which accord with the self-target, and the key coupling boundary variables are converted into the key coupling boundary variables
And transmitting the data to a power distribution network through a grid-connected point.
And step 3: power distribution networkSolving the nonlinear optimization problem of the power distribution network by utilizing the obtained boundary coupling data and combining with an own optimization model through a particle swarm algorithm, and then optimizing the obtained P i t (k) And transmitting the data to a controllable resource area of the microgrid group to complete coupling information interaction.
And 4, step 4: and checking a convergence condition. If so, finishing iteration and outputting a microgrid group controllable resource scheduling result; if the iteration is not converged, returning to the step 2, repeating the solving process, and updating the Lagrange multiplier according to the following formula, wherein the iteration times k = k + 1:
preferably, the section selects an IEEE-33 node power distribution system as a research object. Wherein, the nodes 3, 7, 20 and 29 are respectively connected with the micro-grid to form a micro-grid group, the node 12 is connected with the centralized energy storage, and the node 28 is connected with the interruptible flexible load. The specific structure of the network is shown in fig. 4. The controllable resource type in the microgrid and the range of grid-connected parameters of the distribution network-microgrid group in 96 scheduling periods in the day are shown in table 1:
TABLE 1 micro grid group resource grid-connection parameter range for 96 scheduling periods
The power distribution network-microgrid group controllable resource output situation in 96 scheduling periods in a day is shown in fig. 5.
The distributed optimization based on the ADMM method is compared with the traditional centralized optimization scheduling result, and table 2 shows the scheduling cost result comparing the distributed optimization result with the traditional centralized optimization result:
TABLE 2 comparison of scheduling cost of distributed and traditional centralized optimization results
As can be seen from table 2, the total scheduling cost in the distributed regulation mode is 12195 yuan, which is equivalent to the scheduling cost 12148 yuan of the centralized regulation mode, and the benefit coordination between the power distribution network region and the controllable resource region of the microgrid cluster is better realized. Compared with the traditional centralized regulation and control mode, the distributed regulation and control mode can solve the problems of heavy calculation burden, difficult communication, opaque information between areas, paradox interest subject targets and the like in the centralized regulation and control on the premise of keeping the economy equivalent to that of the centralized regulation and control mode, and has feasibility and comprehensive advantages in the process of optimizing the distribution network-microgrid group involving multi-interest subjects.
Example 2
Based on the same inventive concept, the present application further provides a scheduling system, comprising:
a first module: the method is configured for constructing a distributed optimization overall framework, and specifically, virtual region division is carried out on the basis of different benefits of source load storage main bodies in a power distribution network-microgrid group, a power distribution network region is formed by all nodes of the power distribution network, and a microgrid group controllable resource region comprises four microgrid groups;
a second module: the method comprises the steps that a total optimization scheduling objective function and constraint conditions are decomposed through an alternating direction multiplier method, and a distributed optimization scheduling model of a power distribution network-micro power grid group considering multi-party benefit agents is constructed;
a third module: and the method is configured to solve the distributed optimization scheduling model based on the alternative direction multiplier method and optimize scheduling according to a solving structure.
Example 3
Based on the same inventive concept, the application also provides an electronic device, a computer readable storage medium storing computer executable instructions; and one or more processors coupled to the computer-readable storage medium and configured to execute the computer-executable instructions to cause the apparatus to perform the above-described methods.
Example 4
Based on the same inventive concept, the present application also provides a readable storage medium storing computer-executable instructions that, when executed by a processor, configure the processor to perform the above-described method.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The distribution network-microgrid group collaborative distributed optimization scheduling method based on the ADMM is characterized by comprising the following steps:
dividing virtual areas based on different benefits of source load storage main bodies in a power distribution network-microgrid group to obtain a distributed optimization overall framework;
decomposing the objective function and the constraint condition of the total optimization scheduling by adopting an alternative direction multiplier method based on a distributed optimization overall framework, and constructing a distributed optimization scheduling model of the power distribution network-microgrid group based on a decomposition result;
and solving the distributed optimal scheduling model based on an alternating direction multiplier method, and optimizing and scheduling according to a solving result.
2. The method of claim 1, wherein the distributed optimization ensemble framework comprises a power distribution network region and a microgrid group controllable resource region, and the step of decomposing the overall optimization scheduling objective function using an alternating direction multiplier method based on the distributed optimization ensemble framework comprises:
based on a distributed optimization overall framework, performing distributed solution on an overall optimization scheduling objective function through an ADMM algorithm to obtain an objective function of the power distribution network area and objective functions of all benefit subjects in the micro-grid controllable resource area;
wherein, the objective function of the distribution network region is:
in the formula, C net The sum of the network loss cost of the power distribution network area side and the electricity purchasing cost of the main network,
in order to be a lagrange multiplier,
the original injection power of the boundary variable is coupled between the distribution network area and the micro-grid group controllable resource area,
injecting virtual power into a controllable resource area belonging to a micro-grid group, wherein rho is a penalty parameter corresponding to an alternative direction multiplier method;
the objective function of each interest subject in the micro-grid controllable resource area is as follows:
in the formula, C res,i Is the sum of the costs of the micro-grid group controllable resource area side gas turbine, the photovoltaic, the wind turbine, the energy storage and the flexible load scheduling,
in order to be a lagrange multiplier,
the original injection power of the boundary variable is coupled between the distribution network area and the micro-grid group controllable resource area,
and injecting virtual power into the controllable resource area belonging to the microgrid group, wherein rho is a penalty parameter corresponding to the alternative direction multiplier method.
3. The method of claim 2, wherein the step of decomposing the constraint of the overall optimization schedule by using an alternative direction multiplier method based on the overall framework of distributed optimization comprises:
and decomposing the constraint condition of the total optimization scheduling by adopting an alternating direction multiplier method based on a distributed optimization overall framework to obtain the constraint condition of the power distribution network area, the constraint condition of the micro-grid group controllable resource area and coupling constraint.
4. The method of claim 3,
the constraint conditions of the distribution network area are as follows:
the input active power of any node in the network is equal to the sum of the transmission active powers of all connection lines of the node; the input reactive power of any node in the network is equal to the sum of the transmission reactive powers of all connection lines of the node; the branch power flow between the nodes i and j is equal to the difference of the branch conductance multiplied by the product of the square of the voltage of the node i and the product of the voltage of the node i, the voltage of the node j and the angle difference cosine value of the voltage of the nodes i and j, and then the product of the voltage of the node i, the voltage of the node j, the sine value of the angle difference sine value of the voltage of the nodes i and j is subtracted; the sum of the active power flowing into the node i and the active power flowing out of the node i is 0;
the branch power flow between the nodes i and j is positioned between the upper limit and the lower limit of the branch power flow, and the voltage amplitude of the node i is positioned between the upper limit and the lower limit of the voltage;
in the formula (I), the compound is shown in the specification,
respectively representing the active power and the reactive power flowing into the node i at the moment t,
representing the power load power of the node i at time t,
representing the voltage at node i at time t, G ij 、B ij Is the conductance and susceptance between the nodes i, j at time t, theta t ij The phase angle difference of the voltage of the j node at the t moment i,
representing the branch power flow between the nodes i and j at the time t
Respectively representing the upper and lower limits, P, of the voltage at node i at time t L,max,i-j 、P L,min,i-j Respectively representing the upper limit and the lower limit of the branch power flow between the nodes i and j at the time t.
5. The method as claimed in claim 3 or 4, wherein the constraint conditions of the controllable resource area of the microgrid group are:
the active output of the micro-grid group m is between the maximum and minimum active outputs, and the output of the micro-grid group m at the t moment, which is subtracted from the actual output of the micro-grid group m containing controllable resources at the t-1 moment, is not more than the maximum and minimum constraints of climbing;
in the formula (I), the compound is shown in the specification,
are respectively provided withRepresenting the maximum and minimum output of the microgrid group m containing controllable resources at the moment t,
representing the actual output of the microgrid cluster m containing controllable resources at the moment t,
respectively representing the maximum climbing rate and the maximum slip rate of the micro-grid group m with active climbing constraint at the time t, wherein delta t is the climbing calculation interval time;
in the controllable resource, the operation constraints of the energy storage unit are as follows:
the active output of the energy storage unit k is between the maximum active output and the minimum active output, and the energy charged and discharged in the time of subtracting delta t from the energy storage of the energy storage unit k at the time of t-1 cannot exceed the upper limit and the lower limit of the energy storage unit k;
in the formula, k is the number of the energy storage units,
respectively represents the maximum and minimum output force of the energy storage unit k at the moment t,
respectively represent the upper limit and the lower limit of the stored energy of the energy storage unit k at the moment t,
representing the actual power of the energy storage unit k at time t,
representing the actual energy storage of the energy storage unit k at the moment t, wherein delta t is the charging and discharging calculation interval time, and eta is the charging and discharging efficiency;
the coupling constraints are:
coupling boundary variables on the power distribution network side of the node i are equal to coupling boundary variables on the controllable resource side of the microgrid group, and the coupling boundary variables on the controllable resource side of the microgrid group are equal to active variables of all controllable resources passing through the node i
Summing;
in the formula (I), the compound is shown in the specification,
for coupling boundary variables on the distribution network side, K i The controllable resource quantity of the microgrid group under the node i,
coupling boundary variables on the controllable resource side of the microgrid cluster,
the active variables of the controllable resources of the inode are shown.
6. The method of claim 1, wherein solving the distributed optimal scheduling model based on an alternating direction multiplier method comprises:
based on an alternative direction multiplier method, independently solving an optimization model of a power distribution network region and a microgrid group controllable resource region;
performing exchange coupling variables;
until the iteration convergence condition is met:
where ε represents the convergence accuracy and k represents the number of iterations.
7. The method of claim 6, wherein the specific process of solving comprises:
step 1: for optimizing variables in model
Initializing and applying lagrange multiplier to
And carrying out initial setting on a penalty function rho, wherein the iteration times k =1;
step 2: independently solving the optimization model of the controllable resources of each microgrid group to obtain an optimization variable meeting the target of the optimization model, and coupling the key boundary variables
Transmitting the data to a power distribution network through a grid-connected point;
and step 3: the method comprises the steps of solving the nonlinear optimization problem of the power distribution network through a particle swarm algorithm by utilizing boundary coupling data obtained by the power distribution network and combining with a self optimization model, and then obtaining the optimized boundary coupling data
Transmitting the data to a microgrid group controllable resource area to complete coupling information interaction;
and 4, step 4: verifying a convergence condition; if so, finishing iteration and outputting a microgrid group controllable resource scheduling result; if the iteration is not converged, returning to the step 2, repeating the solving process, and updating the Lagrange multiplier according to the following formula, wherein the iteration times k = k + 1:
8. a scheduling system, comprising:
a first module: the method is configured for constructing a distributed optimization overall framework, and specifically, virtual region division is carried out on the basis of different benefits of source load storage main bodies in a power distribution network-microgrid group, a power distribution network region is formed by all nodes of the power distribution network, and a controllable resource region of the microgrid group comprises four microgrid groups;
a second module: the method comprises the steps that a total optimization scheduling objective function and constraint conditions are decomposed through an alternating direction multiplier method, and a distributed optimization scheduling model of a power distribution network-micro power grid group considering multi-party benefit agents is constructed;
a third module: and the method is configured to solve the distributed optimization scheduling model based on the alternative direction multiplier method and optimize scheduling according to a solving structure.
9. An electronic device, characterized by a computer-readable storage medium storing computer-executable instructions; and one or more processors coupled to the computer-readable storage medium and configured to execute the computer-executable instructions to cause the apparatus to perform the method of any of claims 1-7.
10. A readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, configure the processor to perform the method of any one of claims 1-7.
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| CN116433225A (en) * | 2023-06-12 | 2023-07-14 | 国网湖北省电力有限公司经济技术研究院 | Multi-time scale fault recovery method, device and equipment for interconnected micro-grid |
| CN117057850A (en) * | 2023-10-12 | 2023-11-14 | 国网浙江省电力有限公司 | Distributed resource cluster scheduling method and device considering carbon market |
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| CN116433225A (en) * | 2023-06-12 | 2023-07-14 | 国网湖北省电力有限公司经济技术研究院 | Multi-time scale fault recovery method, device and equipment for interconnected micro-grid |
| CN116433225B (en) * | 2023-06-12 | 2023-08-29 | 国网湖北省电力有限公司经济技术研究院 | Multi-time scale fault recovery method, device and equipment for interconnected micro-grid |
| CN117057850A (en) * | 2023-10-12 | 2023-11-14 | 国网浙江省电力有限公司 | Distributed resource cluster scheduling method and device considering carbon market |
| CN117057850B (en) * | 2023-10-12 | 2024-01-26 | 国网浙江省电力有限公司 | Distributed resource cluster scheduling method and device considering carbon market |
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