CN110265991A - A kind of distributed and coordinated control method of direct-current grid - Google Patents
A kind of distributed and coordinated control method of direct-current grid Download PDFInfo
- Publication number
- CN110265991A CN110265991A CN201910375829.2A CN201910375829A CN110265991A CN 110265991 A CN110265991 A CN 110265991A CN 201910375829 A CN201910375829 A CN 201910375829A CN 110265991 A CN110265991 A CN 110265991A
- Authority
- CN
- China
- Prior art keywords
- power
- energy
- distributed
- direct
- storage units
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J1/00—Circuit arrangements for dc mains or dc distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J1/00—Circuit arrangements for dc mains or dc distribution networks
- H02J1/14—Balancing the load in a network
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of distributed and coordinated control methods of direct-current grid, comprising the following steps: S1: establishing the total power production cost function of system in conjunction with equilibrium of supply and demand condition and capacity limit according to the distributed generation resource in DC micro power grid system；S2: each generator unit and its intelligent body controller obtain local information, and initialize；S3: cost of electricity-generating function is solved using improved multiple agent consistency algorithm, obtains the slight increase in cost and output power of each unit, while stable DC busbar voltage；S4: carrying out the algorithm iteration of subsequent time, handles the information of acquisition, finally exports optimal power；S5: distributed coordination scheduling controlling is carried out to direct-current grid using optimal power.Compared with prior art, the present invention has the advantages that while realizing minimum cost of electricity-generating, effective stable busbar voltage and the multi objective control for keeping power-balance, renewable energy to maximally utilize.
Description
Technical field
The present invention relates to direct-current grid energy compatibility control technology fields, more particularly, to a kind of point of direct-current grid
Cloth control method for coordinating.
Background technique
As permeability of a large amount of generations of electricity by new energy in traditional power grid is continuously improved, micro-capacitance sensor technology is come into being.It is micro-
Electric power network technique is a kind of small-sized electric system by organic combinations such as distributed generation resource, load, energy storage devices together.At present
Exchange micro-capacitance sensor, but the electricity that the generations of electricity by new energy unit such as photovoltaic, wind-power electricity generation generates are had focused largely on for the research of micro-capacitance sensor
It can largely be direct current, ac-dc conversion device is not only eliminated using direct-current grid, reduce cost, reduce loss, and
And the problems such as frequency stabilization, reactive power are not present in power grid, therefore, to the research of DC micro power grid system by extensive
Concern.Busbar voltage is the key index of reaction system stable operation and power-balance.It can draw when unbalanced power in system
The fluctuation of busbar voltage is played, busbar voltage is excessively high to illustrate that active power is superfluous in system, conversely, then underpower in system.Cause
This, control DC bus-bar voltage stablizes the important goal of usually direct-current grid.
Energy Management System is the necessary means of micro-capacitance sensor trend management, way to manage mainly have based on planning management and
Optimal management.Wherein optimal management considers the economic benefit of system operation, thus at home and abroad attracts wide public concern.
For the control method of direct-current grid, it is broadly divided into centerized fusion and distributed AC servo system.Centerized fusion is adopted
Send and receive status command to central controller with controllable in system is unified, to the stability requirement of communication network compared with
Height, and lead to system operation higher cost, and the high permeability of distributed generation resource and the expansibility of micro-capacitance sensor make tradition
Centralized optimal coordination lack of control flexibility and expansibility.In comparison, distributed AC servo system is more adaptable, more can
Meet the requirement of distributed generation resource plug and play.
Multi-agent system has good enlightening and independence as one of distributed frame, especially suitable
In complicated microgrid energy management.In the distributed AC servo system of multi-agent system, most basic problem, that is, multi-agent system
Consistency.The consistency optimization algorithm of multiple agent multi-agent system can be realized the distributed optimization fortune of electric system
Row, this distributed control structure only need to obtain the information of local intelligent body and its neighbours' intelligent body, and network communication pressure is small,
Meet plug and play.Controllable power equipment realizes information exchange by communication network, and passes through local communication network and other intelligence
Energy body carries out information exchange, realizes the coordination optimization operation of entire micro-grid system.
In the energy management strategies research at present for microgrid, most of research is directed to conventional AC micro-capacitance sensor.In needle
To in the research of direct-current grid, be related to renewable energy utilization maximize and cost of electricity-generating minimize this kind of economic goal compared with
It is few.It is therefore proposed that one kind can realize that operating cost is minimum in direct-current grid, and reasonable distribution each unit output power,
Can fast implement that busbar voltage is stable simultaneously and the multi objective control strategy process of grid power balance very it is necessary to.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of direct-current grids
Distributed and coordinated control method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of distributed and coordinated control method of direct-current grid, comprising the following steps:
Step 1: being built according to the distributed generation resource in DC micro power grid system in conjunction with equilibrium of supply and demand condition and capacity limit
The total power production cost function model of erection system；
Step 2: each generator unit and its intelligent body controller obtain local information, and initialize；
Step 3: cost of electricity-generating function being solved using improved multiple agent consistency algorithm, obtains the cost of each unit
Tiny increment and output power, while stable DC busbar voltage；
Step 4: carrying out the algorithm iteration of subsequent time, the information of acquisition is handled, optimal power is finally exported；
Further, the total power production cost function model in the step 1, describes formula are as follows:
In formula, PB.iFor the output power of energy-storage units i, SBFor the set of controllable energy-storage units, ai、bi、ciIt is corresponding
Function coefficients, i are natural number, CiIndicate the operating cost of generator unit i.
Further, the equilibrium of supply and demand condition in the step 1, describes formula are as follows:
In formula, PGFor the output power of all distributed generation units, SLFor the set of all load cells, PD.iFor energy storage
The active demand in the local of unit i.
Further, the capacity limit in the step 1, describes formula are as follows:
In formula,PB.i(k)、Respectively correspond the output power minimum value for k moment energy-storage units i,
Actual value and maximum value,SOCB.i(k)、Respectively correspond the residue electricity for k moment energy-storage units i
Measure minimum value, actual value and maximum value.
Further, the process of the initialization in the step 2 include it is following step by step:
Step 201: obtaining the local information on load and active power that energy-storage units i intelligent body measures, obtain each unit
Slight increase in cost；
Step 202: according to topological graph at Laplacian Matrix and adjacency matrix.
Further, the algorithm improvement of the multiple agent consistency algorithm in the step 3 includes defining auxiliary variable simultaneously
It further defines the output power of energy-storage units and introduces voltage stabilization function and modified consistency protocol is further set.
Further, the calculation formula of the output power of the energy-storage units are as follows:
In formula, aijIndicate the communication topology weight between intelligent body i and j, NiIndicate the neighboring units collection of energy-storage units i
It closes,WithRespectively indicate the auxiliary variable of t moment intelligent body i and j, dicIt is that energy-storage units i and its local demand are active
Weight coefficient between load, PD.cIndicate the active demand in local corresponding with weight coefficient.
Further, the modified consistency protocol, describes formula are as follows:
In formula, L indicates Laplacian Matrix,λ (t) is that t moment controllably runs list
First cost,For the derivative of the controllable running unit cost of t moment, ε is DC bus-bar voltage error factor, UdcFor DC bus
The setting value of voltage, U are the actual value of DC bus-bar voltage.
Further, in the step 4 to the information of acquisition carry out processing include it is following step by step:
Step 401: when using the output power for occurring some generator unit after consistency algorithm beyond its limit value, repairing
Change the power limit of output power and the output power constraint of controllable energy-storage units is set；
Step 402: after consistency algorithm iteration, judging after whether output power is out-of-limit according to controllable energy-storage units
Output power constraint export corresponding active power.
Further, the output power constraint of the controllable energy-storage units in the step 401, specifically describes formula are as follows:
In formula, λ*For optimal incremental cost,WithRespectively the output power minimum value of energy-storage units i and maximum
Value.
Compared with prior art, the invention has the following advantages that
(1) present invention is changed using improved multiple agent consistency algorithm to direct-current grid operating cost model solution
Into consistency algorithm there is good convergence, fast speed can steadily converge to maximum power point；Pass through each intelligence
Body exchanges incremental cost information with neighbours' intelligent body, and obtains itself d-c bus voltage value, compared to traditional centralization control
System, communications burden are small.
(2) present invention meet controllable energy-storage units operating cost it is minimum under the premise of, while realizing distributed generation resource
Maximum consumption, generated output is optimal and DC bus-bar voltage is stablized, realize the multi-objective coordinated control of direct-current grid.
Detailed description of the invention
Fig. 1 is direct-current grid structural schematic diagram of the present invention；
Fig. 2 is control method flow chart proposed by the present invention；
Fig. 3 is each agent communication network topology structure figure that the present invention uses；
Fig. 4 is slight increase in cost convergence graph obtained in the embodiment of the present invention；
Fig. 5 is the optimal output power convergence graph of each controllable obtained in the embodiment of the present invention；
Fig. 6 is the DC bus-bar voltage change curve in the embodiment of the present invention；
Fig. 7 is the DC bus-bar voltage change curve comparison diagram under the embodiment of the present invention and traditional centerized fusion method.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiment is a part of the embodiments of the present invention, rather than whole embodiments.Based on this hair
Embodiment in bright, those of ordinary skill in the art's every other reality obtained without making creative work
Example is applied, all should belong to the scope of protection of the invention.
Embodiment
Fig. 1 is that DC micro-electric web frame of the present invention mainly includes as seen from Figure 1 in direct-current grid
Distributed generation unit, energy-storage units and load cell.Direct-current grid is connected by static transfer switch (STS) with major network,
Therefore direct-current micro-grid can work in both modes: grid-connect mode and island mode, only consider direct-current grid in the present invention
Work is under island mode.DC micro-electric web frame is divided into communication structure and two layers of physical structure, and communication structure is by control unit
Corresponding intelligent body composition, physical structure are made of control unit in power grid.Each intelligent body can receive and sample accordingly
The instruction and information of local physical unit and its neighboring units, are iterated in intelligent body after receiving corresponding information,
To update the information of local physical unit.
The specific steps process of control method of the present invention is as shown in Figure 2, comprising the following steps:
Step 1:, in conjunction with equilibrium of supply and demand condition and capacity limit, being built according to the distributed generation resource in DC micro power grid system
The total power production cost function of erection system.
The expression formula of the total power production cost function of DC micro power grid system are as follows:
In formula, PB.iFor the output power of energy-storage units i, SBFor the set of controllable energy-storage units, ai、bi、ciIt is corresponding
Function coefficients, i are natural number, CiIndicating the operating cost of generator unit i, energy-storage units are divided into charge and discharge two states,
Symbol is positive when electric discharge, and symbol is negative when charging.
Direct-current grid equilibrium of supply and demand condition are as follows:
In formula, PGFor the output power of all distributed generation units, SLFor the set of all load cells, PD.iFor energy storage
The active demand in the local of unit i.
The working capacity limitation of controllable energy-storage units are as follows:
In formula,PB.i(k)、It respectively corresponds as output power minimum value, the reality of k moment energy-storage units i
Actual value and maximum value,SOCB.i(k)、Respectively correspond for k moment energy-storage units remaining capacity most
Small value, actual value and maximum value.
When not considering that output power and remaining capacity limit, method of Lagrange multipliers can use, objective function is turned
It turns to:
In formula: η is Lagrange multiplier.To variable PB.iWith η derivation, the optimal conditions of objective function is obtained:
By cost CiTo output power PB.iDerivative Definition be i-th of energy-storage units slight increase in cost λi, by optimal item
Part is it is found that the optimal solution of cost objective function is the slight increase in cost λ of each energy-storage unitsiEqual and holding power-balance, it may be assumed that
λ1=λ2=...=λi=λ* i∈SB
In formula: λ*For optimal incremental cost.When meeting " equal incremental principle ", the operating cost of DC micro power grid system
It is minimum.At this point, the relationship between corresponding optimal incremental cost and optimal output power are as follows:
In formula:For the optimal output power of each unit, it may be assumed that
Step 2: each generator unit and its intelligent body controller obtain local information, and initialize.
As shown in Figure 1, the structure of direct-current grid is divided into communication structure and two layers of physical structure, initialization information it is specific
Content includes:
(1) the intelligent body i of unit i obtains the local information on load P measuredD.iAnd active-power PB.i, calculate each unit
Slight increase in cost λi。
(2) adjacency matrix is formed at Laplace matrix according to topological graph.
Laplace matrix is defined as follows:
Information exchange is carried out by communication network between each node of direct-current grid, diagram of communications networks can use G=
{ V, E, A } indicates that definition figure G is a Undirected networks, wherein V={ v1, v2... vnIt is node set, n is node total number；E
The set on the side constituted between each node,A is the adjacency matrix for describing relationship between node and side.If saving
Point ViAnd VjBetween there are communication path, then ViAnd VjIt is mutual neighbours, that is, thinks there is a nonoriented edge, the company between the two
It connects and is expressed as (Vi,Vj), and (Vi,Vj) ∈ E, particularly in non-directed graph, (Vi,Vj) ∈ E is equal to (Vj,Vi)∈E；It is in neighbour
Meet matrix A={ aijIn corresponding adjacency matrix element, aij=aji> 0, otherwise aij=aji=0, diagonal entry aii=0.
Therefore, the neighborhood for defining vertex is Ni={ j ∈ v:(vj, vi)∈E}.Node ViCorresponding in-degree isBy di
Degree matrix D=diag { d of compositionij, then the Laplace matrix for scheming G is defined as L=D-A, wherein lij=-aij,
Step 3: using improved multiple agent consistency algorithm to cost of electricity-generating function solve, obtain each unit at
This tiny increment and output power, while stable DC busbar voltage.
Consistency algorithm has wide application in the various aspects such as collective control, complex dynamic network, coordinated control.As
The method for finding direct-current grid Optimum Economic operating point, has the characteristics that fast convergence rate, the condition of convergence are simple.
In traditional multiple agent consistency algorithm, it is assumed that have n intelligent body, define dynamical equation:
In formula, uiIt (t) is control variable, xi(t) state of different intelligent body in network, subscript are indicated for state variable
Indicate derivative, the specific control law for controlling variable is as follows:
Write as matrix form then are as follows:
The algorithm exchanges information with adjacent intelligent body by each intelligent body, and the state difference between two intelligent bodies is constantly subtracted
The small consistency up to reaching all intelligent bodies, this method are not necessarily to global information, it is only necessary to obtain adjacent local message.
The improved multiple agent consistency algorithm that the present invention uses, in order to solve the problems, such as economic optimization, using λ as one
Cause property variable, when each intelligent body iterates to optimal incremental cost, while can obtain optimal output power.In view of each controllable
The slight increase in cost of unit is different, and the bound of output power is different, the delay in distributed consensus algorithm communication network
It is very important, that is, guarantee the robustness of strategy.Guarantee the robustness of strategy.Assuming that the communication occasions between intelligent body are by t table
Show, defines auxiliary variableDefine the output power of energy-storage units are as follows:
In formula, aijIndicate the communication topology weight between intelligent body i and j, NiIndicate the neighboring units collection of energy-storage units i
It closes,WithRespectively indicate the auxiliary variable of t moment intelligent body i and j, dicHave for energy-storage units i and its local demand
Weight coefficient between workload, if unit has local active demand, otherwise value 1 is 0, PD.cIt indicates and weight system
The corresponding active demand in local of number, j, c are natural number.
Communication protocol proposed by the present invention is as follows:
λi(0)=aiPB.i(0)+bi
Wherein, λi(0)、PB.iIt (0) is respectively λ, PB.iInitial value；Subscript indicates derivative value；The output power of each unit
Iteration meets:
As can be seen that the consistency protocol on the basis of meeting power-balance condition, can iterate to optimal output work
Rate, while meeting equal incremental rate criterion, the least cost of available controllable running unit.
Matrix form after abbreviation are as follows:
For the fluctuation that direct-current grid median generatrix voltage is likely to occur, on the basis of consistency protocol, voltage is introduced
Stability function controls d-c bus voltage value to rated value using consistency protocol, so that DC bus-bar voltage is converged to and is
The rated value of system setting.Modified consistency protocol are as follows:
In formula, L indicates Laplacian Matrix,λ (t) is that t moment controllably runs list
First cost,For the derivative of the controllable running unit cost of t moment, ε is DC bus-bar voltage error factor, UdcFor DC bus
The setting value of voltage, U are the actual value of DC bus-bar voltage, when the setting value of DC bus-bar voltage is greater than DC bus-bar voltage
When actual value, output power increases；It needs to reduce if opposite, when the incremental cost iteration of a intelligent body unit reaches consistent
When, DC bus-bar voltage actual value reaches specified setting value.
Step 4: carrying out the algorithm iteration of subsequent time, the information of acquisition is handled, optimal power is finally exported.
When using consistency algorithm, it is possible to cause the output power of some generator unit beyond its limit value, at this time
The power limit of output power should be modified.Consider the limitation range of output power, the output power constraint of controllable energy-storage units can
With modification are as follows:
In formula, λ*For optimal incremental cost,WithRespectively the output power minimum value of energy-storage units i and maximum
Value.
After consistency algorithm iteration, judge whether output power is out-of-limit, is exported further according to power constraint corresponding
Active power.
For the validity for proving coordination control strategy method of the present invention, the structure that the present embodiment has built direct-current grid is imitative
True mode, as shown in Figure 1.Micro-capacitance sensor models operate in island state, and DC bus-bar voltage rated value is 380V；In analogue system
Comprising two groups of photovoltaic generation units, maximum power is respectively 100kW and 150kW；Three groups of energy-storage units, capacity are 30kWh, respectively
The initial SOC of unit is respectively 70%, 65%, 60%, and the range that SOC is chosen in text is 20%~90%；System original negative pocket
DC load containing 200kW.
Communication topology used by the present embodiment is as shown in figure 3, corresponding adjacency matrix are as follows:
The cost parameter and control parameter of each energy-storage units are as shown in Table 1 and Table 2.
1 cost parameter of table
2 control parameter of table
In order to verify consistency algorithm of the invention to the stabilization of DC bus-bar voltage.For the fortune of direct-current grid
Row, reduces the DC load 1 of 80kW suddenly in 20s, the AC load 2 of 100kW is further added by 35s.And the knot that will be obtained
Fruit compares with traditional droop control method.
Fig. 4 is the optimal cost tiny increment that the consistency algorithm that model of the present invention uses obtains.All controllable storages in system
The slight increase in cost λ of energy unit converges to identical value, and optimal cost tiny increment λ in 5s*=12.68 yuan/kW.
Fig. 5 is the optimal output power that the consistency algorithm that model of the present invention uses obtains.It is found by Fig. 5 analysis, respectively
Energy storage output power converges to optimal output power in 11s, energy storage 1, energy storage 2, energy storage 3 optimal output power be respectively
PB.1=18.11kW, PB.2=17.61kW, PB.3=30.7kW completes the optimal energy management of the microgrid.
Fig. 6 is the control that the consistency algorithm that model of the present invention uses changes DC bus-bar voltage.When micro-capacitance sensor is stablized
After operation, reduce the DC load of 80kW in 20s, increases the AC load of 100kW in 35s, obtained DC bus electricity
Corrugating is as shown in Figure 7.As can be seen that load occur fluctuation after 5s in, improved consistency algorithm be able to respond and incite somebody to action
The DC bus-bar voltage of fluctuation is again stable to rated value.
Fig. 7 is the control and the sagging control of tradition that the consistency algorithm that model of the present invention uses changes DC bus-bar voltage
The comparison of method.It can be seen that the method proposed in the present invention to the effect of stable DC busbar voltage by carrying out analysis to Fig. 7
Fruit is more significant.
Table 3 is that the consistency algorithm that model of the present invention uses and the direct-current grid that traditional droop control method obtains are run
Cost.The operating cost for the DC micro power grid system that distributed consensus algorithm designed by the invention obtains is than sagging method
The operating cost of control reduces 13%.
As can be seen that the multiple agent consistency algorithm that model of the present invention proposes effectively reduces the fortune of direct-current grid
Row cost realizes reasonable distribution each unit output power on the basis of operating cost is minimum, while can fast implement straight
Flow the multi objective control of busbar voltage stabilization and grid power balance.
Operating cost under the different control methods of table 3
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (10)
1. a kind of distributed and coordinated control method of direct-current grid, which comprises the following steps:
Step 1: system is established in conjunction with equilibrium of supply and demand condition and capacity limit according to the distributed generation resource in DC micro power grid system
The total power production cost function model of system；
Step 2: each generator unit and its intelligent body controller obtain local information, and initialize；
Step 3: cost of electricity-generating function being solved using improved multiple agent consistency algorithm, obtains the micro- increasing of cost of each unit
Rate and output power, while stable DC busbar voltage；
Step 4: carrying out the algorithm iteration of subsequent time, the information of acquisition is handled, optimal power is finally exported；
Step 5: distributed coordination scheduling controlling being carried out to direct-current grid using optimal power.
2. a kind of distributed and coordinated control method of direct-current grid according to claim 1, which is characterized in that the step
Total power production cost function model in rapid 1, describes formula are as follows:
In formula, PB.iFor the output power of energy-storage units i, SBFor the set of controllable energy-storage units, ai、bi、ciFor corresponding function system
Number, i is natural number, CiIndicate the operating cost of generator unit i.
3. a kind of distributed and coordinated control method of direct-current grid according to claim 1, which is characterized in that the step
Equilibrium of supply and demand condition in rapid 1, describes formula are as follows:
In formula, PGFor the output power of all distributed generation units, SLFor the set of all load cells, PD.iFor energy-storage units
The active demand in the local of i.
4. a kind of distributed and coordinated control method of direct-current grid according to claim 1, which is characterized in that the step
Capacity limit in rapid 1, describes formula are as follows:
In formula,PB.i(k)、It respectively corresponds as output power minimum value, the actual value of k moment energy-storage units i
And maximum value,SOCB.i(k)、Respectively correspond the remaining capacity minimum for k moment energy-storage units i
Value, actual value and maximum value.
5. a kind of distributed and coordinated control method of direct-current grid according to claim 1, which is characterized in that the step
The process of initialization in rapid 2 include it is following step by step:
Step 201: obtain the local information on load that measures of energy-storage units i intelligent body and active power, obtain each unit at
This tiny increment；
Step 202: according to topological graph at Laplacian Matrix and adjacency matrix.
6. a kind of distributed and coordinated control method of direct-current grid according to claim 1, which is characterized in that the step
The algorithm improvement of multiple agent consistency algorithm in rapid 3 includes the output for defining auxiliary variable and further defining energy-storage units
Simultaneously modified consistency protocol is further arranged in power and introducing voltage stabilization function.
7. a kind of distributed and coordinated control method of direct-current grid according to claim 6, which is characterized in that described
The calculation formula of the output power of energy-storage units are as follows:
In formula, aijIndicate the communication topology weight between intelligent body i and j, NiIndicate the neighboring units set of energy-storage units i,WithRespectively indicate the auxiliary variable of t moment intelligent body i and j, dicIt is that energy-storage units i and its local demand are active negative
Weight coefficient between lotus, PD.cIndicate the active demand in local corresponding with weight coefficient.
8. a kind of distributed and coordinated control method of direct-current grid according to claim 6, which is characterized in that described
Modified consistency protocol, describes formula are as follows:
In formula, L indicates Laplacian Matrix,λ (t) be the controllable running unit of t moment at
This,For the derivative of the controllable running unit cost of t moment, ε is DC bus-bar voltage error factor, UdcFor DC bus-bar voltage
Setting value, U be DC bus-bar voltage actual value.
9. a kind of distributed and coordinated control method of direct-current grid according to claim 1, which is characterized in that the step
In rapid 4 to the information of acquisition carry out processing include it is following step by step:
Step 401: when using the output power for occurring some generator unit after consistency algorithm beyond its limit value, modifying defeated
Out the power limit of power and be arranged controllable energy-storage units output power constraint；
Step 402: after consistency algorithm iteration, judging after whether output power is out-of-limit according to the defeated of controllable energy-storage units
Power constraint exports corresponding active power out.
10. a kind of distributed and coordinated control method of direct-current grid according to claim 9, which is characterized in that described
The output power of controllable energy-storage units in step 401 constrains, and specifically describes formula are as follows:
In formula, λ*For optimal incremental cost,WithRespectively the output power minimum value of energy-storage units i and maximum value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910375829.2A CN110265991B (en) | 2019-05-07 | 2019-05-07 | Distributed coordination control method for direct-current micro-grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910375829.2A CN110265991B (en) | 2019-05-07 | 2019-05-07 | Distributed coordination control method for direct-current micro-grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110265991A true CN110265991A (en) | 2019-09-20 |
| CN110265991B CN110265991B (en) | 2023-04-28 |
Family
ID=67914288
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910375829.2A Active CN110265991B (en) | 2019-05-07 | 2019-05-07 | Distributed coordination control method for direct-current micro-grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110265991B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111030075A (en) * | 2019-11-26 | 2020-04-17 | 深圳供电局有限公司 | Control method of direct current power supply and distribution system |
| CN112260291A (en) * | 2020-10-26 | 2021-01-22 | 上海电力大学 | Multi-mode economic operation method of alternating current-direct current hybrid micro-grid based on incremental cost |
| CN112801379A (en) * | 2021-01-30 | 2021-05-14 | 河南城建学院 | Smart power grid distributed energy management system based on cloud computing and big data |
| CN113285474A (en) * | 2021-06-09 | 2021-08-20 | 中国电力科学研究院有限公司 | Parameter-optimized alternating-current micro-grid energy storage SOC balanced distributed control method |
| CN113704750A (en) * | 2021-08-27 | 2021-11-26 | 国网河北省电力有限公司电力科学研究院 | Network attack detection method and device of distributed power generation system and terminal equipment |
| CN113988478A (en) * | 2021-12-03 | 2022-01-28 | 国网黑龙江省电力有限公司电力科学研究院 | Distributed economic optimization method for direct-current micro-grid interconnection system based on equal micro-increment rate |
| CN117117973A (en) * | 2023-10-24 | 2023-11-24 | 国网浙江省电力有限公司宁波供电公司 | Distributed power supply scheduling method and device based on time scale and storage medium |
| CN118174343A (en) * | 2024-05-11 | 2024-06-11 | 北京智芯微电子科技有限公司 | Micro-grid coordinated control method, device, system, storage medium and electronic equipment |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140025352A1 (en) * | 2012-07-17 | 2014-01-23 | International Business Machines Corporation | Planning economic energy dispatch in electrical grid under uncertainty |
| CN105391056A (en) * | 2015-11-18 | 2016-03-09 | 南京邮电大学 | Power system distributed economic dispatching method taking unbalanced communication network into consideration |
| CN105406517A (en) * | 2015-12-02 | 2016-03-16 | 南京邮电大学 | Finite time average consistency algorithm-based economic dispatching method for power system |
| CN107451704A (en) * | 2017-09-15 | 2017-12-08 | 厦门大学 | A kind of multiple time delay intelligent grid economic load dispatching method based on PREDICTIVE CONTROL |
| CN107623337A (en) * | 2017-09-26 | 2018-01-23 | 武汉大学 | A kind of energy management method for micro-grid |
| KR101840264B1 (en) * | 2016-09-19 | 2018-03-20 | 한양대학교 에리카산학협력단 | Supply-demand balancing method and system for power management in smart grid |
| CN108320080A (en) * | 2018-01-05 | 2018-07-24 | 上海电力学院 | The real-time dynamic power allocation method in energy internet based on two layers of consistency algorithm |
| CN108494022A (en) * | 2018-04-23 | 2018-09-04 | 东北大学 | A kind of accurate dispatching method based on distributed generation resource in micro-capacitance sensor |
| CN108599258A (en) * | 2018-06-06 | 2018-09-28 | 合肥工业大学 | A kind of isolated island microgrid economic optimization dispatching method based on distributed AC servo system |
| CN108808668A (en) * | 2018-06-29 | 2018-11-13 | 广东电网有限责任公司 | It is a kind of meter and multiclass power supply active distribution network distributed optimization dispatching algorithm |
-
2019
- 2019-05-07 CN CN201910375829.2A patent/CN110265991B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140025352A1 (en) * | 2012-07-17 | 2014-01-23 | International Business Machines Corporation | Planning economic energy dispatch in electrical grid under uncertainty |
| CN105391056A (en) * | 2015-11-18 | 2016-03-09 | 南京邮电大学 | Power system distributed economic dispatching method taking unbalanced communication network into consideration |
| CN105406517A (en) * | 2015-12-02 | 2016-03-16 | 南京邮电大学 | Finite time average consistency algorithm-based economic dispatching method for power system |
| KR101840264B1 (en) * | 2016-09-19 | 2018-03-20 | 한양대학교 에리카산학협력단 | Supply-demand balancing method and system for power management in smart grid |
| CN107451704A (en) * | 2017-09-15 | 2017-12-08 | 厦门大学 | A kind of multiple time delay intelligent grid economic load dispatching method based on PREDICTIVE CONTROL |
| CN107623337A (en) * | 2017-09-26 | 2018-01-23 | 武汉大学 | A kind of energy management method for micro-grid |
| CN108320080A (en) * | 2018-01-05 | 2018-07-24 | 上海电力学院 | The real-time dynamic power allocation method in energy internet based on two layers of consistency algorithm |
| CN108494022A (en) * | 2018-04-23 | 2018-09-04 | 东北大学 | A kind of accurate dispatching method based on distributed generation resource in micro-capacitance sensor |
| CN108599258A (en) * | 2018-06-06 | 2018-09-28 | 合肥工业大学 | A kind of isolated island microgrid economic optimization dispatching method based on distributed AC servo system |
| CN108808668A (en) * | 2018-06-29 | 2018-11-13 | 广东电网有限责任公司 | It is a kind of meter and multiclass power supply active distribution network distributed optimization dispatching algorithm |
Non-Patent Citations (4)
| Title |
|---|
| SUN Q Y: "A multiagent-based consensus algorithm for distributed coordinated control of distributed generators in the energy Internet", 《IEEE TRANSACTIONS ON SMART》 * |
| YANG MI: "sliding mode based load frequency contro for multi-area interconnected power system containing renewable energy", 《2014 IEEE CONFERENCE AND EXPO TRANSPORTATION ELECTRIFICATION ASIA-PACIFIC》 * |
| 王惠洲: "基于联盟区块链交易平台的电动汽车有序充电相对鲁棒优化", 《中国电力》 * |
| 谢俊: "基于多智能体系统一致性算法的电力系统分布式经济调度策略", 《电力自动化设备》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111030075A (en) * | 2019-11-26 | 2020-04-17 | 深圳供电局有限公司 | Control method of direct current power supply and distribution system |
| CN111030075B (en) * | 2019-11-26 | 2022-03-22 | 深圳供电局有限公司 | Control method of direct current power supply and distribution system |
| CN112260291A (en) * | 2020-10-26 | 2021-01-22 | 上海电力大学 | Multi-mode economic operation method of alternating current-direct current hybrid micro-grid based on incremental cost |
| CN112260291B (en) * | 2020-10-26 | 2023-09-22 | 上海电力大学 | Multi-mode economic operation method for AC/DC hybrid micro-grid based on incremental cost |
| CN112801379A (en) * | 2021-01-30 | 2021-05-14 | 河南城建学院 | Smart power grid distributed energy management system based on cloud computing and big data |
| CN113285474A (en) * | 2021-06-09 | 2021-08-20 | 中国电力科学研究院有限公司 | Parameter-optimized alternating-current micro-grid energy storage SOC balanced distributed control method |
| CN113285474B (en) * | 2021-06-09 | 2022-07-01 | 中国电力科学研究院有限公司 | Parameter-optimized alternating-current micro-grid energy storage SOC balanced distributed control method |
| CN113704750A (en) * | 2021-08-27 | 2021-11-26 | 国网河北省电力有限公司电力科学研究院 | Network attack detection method and device of distributed power generation system and terminal equipment |
| CN113988478A (en) * | 2021-12-03 | 2022-01-28 | 国网黑龙江省电力有限公司电力科学研究院 | Distributed economic optimization method for direct-current micro-grid interconnection system based on equal micro-increment rate |
| CN117117973A (en) * | 2023-10-24 | 2023-11-24 | 国网浙江省电力有限公司宁波供电公司 | Distributed power supply scheduling method and device based on time scale and storage medium |
| CN117117973B (en) * | 2023-10-24 | 2024-01-12 | 国网浙江省电力有限公司宁波供电公司 | Distributed power supply scheduling method and device based on time scale and storage medium |
| CN118174343A (en) * | 2024-05-11 | 2024-06-11 | 北京智芯微电子科技有限公司 | Micro-grid coordinated control method, device, system, storage medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110265991B (en) | 2023-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110265991A (en) | A kind of distributed and coordinated control method of direct-current grid | |
| Zhao et al. | Distributed agent consensus-based optimal resource management for microgrids | |
| CN108363306B (en) | Micro-grid distributed controller parameter determination method based on linear quadratic optimization | |
| CN108039726B (en) | Energy local area network distributed cooperative control method based on multi-agent system | |
| CN108134401B (en) | Multi-target power flow optimization and control method for alternating current-direct current hybrid system | |
| CN108599154A (en) | A kind of three-phase imbalance power distribution network robust dynamic reconfiguration method considering uncertain budget | |
| CN110912177A (en) | Multi-objective optimization design method for multi-terminal flexible direct current power transmission system | |
| Jing et al. | Analysis, modeling and control of a non-grid-connected source-load collaboration wind-hydrogen system | |
| Zhang et al. | Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response | |
| CN115276019B (en) | Power flow optimization method based on self-adaptive droop control | |
| CN116073453A (en) | Alternating current-direct current hybrid power distribution network optimal scheduling method based on graph calculation | |
| CN107508318B (en) | Active control method and system based on voltage sensitivity partition | |
| CN109560572A (en) | A kind of optimal control method of the microgrid multiple target operation based on switching undisturbed | |
| Song et al. | Optimal Design Method for the Partitioning of OLTC-Inverter Control Parameters in Distribution Stations with High-proportion Residential Photovoltaics | |
| Wang et al. | A multi-agent based hierarchical control system for DERs management in islanded micro-grid | |
| CN211556873U (en) | Multi-state self-adaptive coordination control system for micro-grid | |
| CN114925880A (en) | Virtual energy storage power plant distributed cooperation method based on non-precise alternative direction multiplier method | |
| Li et al. | Bi-level optimal dispatch method of active distribution network based on improved artificial fish swarm algorithm | |
| CN114977335A (en) | Power distribution network operation optimization method and system with flexible multi-state switch | |
| Meng et al. | Optimal control of reactive power and voltage in active distribution network based on source-grid-load interaction | |
| Du et al. | Energy router based minimum loss cost routing strategy in energy internet | |
| Li et al. | Fixed-time distributed algorithm for economic dispatch in smart grid | |
| Xiong et al. | Secondary Frequency Regulation Strategy of Virtual Synchronization Technology Based Microgrid Considering the Integrated Benefit | |
| Wang et al. | Research on Double Layer Coordinated Optimization Planning of Distribution Network with Distributed Generation | |
| Liao et al. | Simulation Study on Generation Grid Load Coordination Model of Active Distribution Network Based on Deep Learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |