CN106647249B - A kind of power load distributing formula control method based on Netlogo and Matlab - Google Patents
A kind of power load distributing formula control method based on Netlogo and Matlab Download PDFInfo
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Abstract
The present invention provides a kind of power load distributing formula control method based on Netlogo and Matlab, comprising: establishes union simulation platform and data interaction interface module based on Matlab and Netlogo；Establish load-Respondence to the Price of Electric Power characteristic model；Obtain the catalogue scalar functions of each load；The each goal orientation degree size of assumed load establishes load action policy between 0-1；Action policy is showed with the coordinate form in Netlogo, each load moves in Netlogo three-dimensional level the general power on corresponding position, obtaining each load agency respectively respectively；Node electricity price is handed down to its load administered by each load agency；System reaches convergence state, i.e., the probability that some action policy is selected levels off to 1, exports optimal policy.Calculating speed of the present invention is fast, and convergence is strong, and comparing previous electric system simulation system has the characteristics that simulation process is intuitive visible.
Description
Technical field
The present invention relates to a kind of method of extensive Smaller load control field, it is in particular to a kind of based on Netlogo with
The power load distributing formula control method of Matlab.
Background technique
Currently, China is deepening power market reform, resource is more reasonably configured, the utilization rate of resource is improved, promoted
The coordinated development of power industry and society, economy, environment.Direct Purchase of Electric Energy by Large Users transaction is undoubtedly the breach of this time reform, right
The Power Market Construction in China is of great significance.The lattice that power grid enterprises exclusively buy and sell electric power are broken in Direct Purchase of Electric Energy by Large Users transaction
Office introduces competition mechanism in power generation and sale of electricity side；Promote power grid transmission & distribution to separate simultaneously, terminal user is made to enter electricity market.Eventually
The addition of end subscriber means that market permissible load participates in the power-balance adjustment of entire power grid under price-bidding model.In competitiveness
In electricity market, the demand response (DR) of load becomes the emphasis of demand side management, i.e., load is according to price signal or corresponding
Incentive mechanism, adjust self-demand amount to guarantee that system is safe and reliable and economical operation.
Before market is not open, load can not participate in system power balance adjustment, therefore duty control method is studied at present
It is less, and existing emulation platform simulation process is not intuitive as it can be seen that the disadvantages of calculating speed is slower, and convergence is not strong.Load is certainly
The variability of body and uncertainty make control become particularly difficult, and the present invention is based on the methods of multiple agent to successfully manage,
Therefore the present invention proposes that distributed control method controls load.Each electricity in distributed control method provided by the invention
The module of Force system element composition has certain intelligence, copes with external disturbance, makes active responding, while by certainly
The communication of body and peripheral modules adjusts the autonomy to reache a certain level to realize self, realizes Real-Time Scheduling and distributed tune
Degree, to improve the reliability and economy of operation of power networks.
Summary of the invention
To overcome above-mentioned the deficiencies in the prior art, the present invention provides a kind of power load distributing based on Netlogo and Matlab
Formula control method carries out distributed and coordinated control to load.By the foundation of load-Respondence to the Price of Electric Power characteristic model, in conjunction with load
The goal orientation degree of corresponding target constitutes the set of strategies of load, obtains load using nitrification enhancement and corresponds to its maximum catalogue
The strategy of offer of tender numerical value, and the optimal load flow in Matlab is combined to calculate, realization optimizes load multiple agent in electric system
Control.
Realize solution used by above-mentioned purpose are as follows:
A kind of power load distributing formula control method based on Netlogo and Matlab, the control method are calculated based on intensified learning
Method selects corresponding general objective functional value maximum each load multiple agent general module Constructing Policy collection in set of strategies
Strategy includes the following steps:
Step 1: establishing union simulation platform and data interaction interface module based on Matlab and Netlogo: Matlab
Every operation for electric system；Netlogo is for Power System Intelligent element model, the control of power load distributing formula and load generation
The foundation of reason；
Step 2: respectively according to load datum quantity, electricity price, economic sexual orientation degree and comfort level tendency degree, establishing load-electricity
Valence response characteristic model；
Step 3: according to the load-Respondence to the Price of Electric Power characteristic model, obtaining the catalogue scalar functions of each load；
Step 4: each goal orientation degree size of assumed load between 0-1, what the goal orientation degree of all targets was constituted
Collection is collectively referred to as load action policy set；By equidistant discretization m parts of 0-1, the goal orientation degree of each target has m choosing
It selects；
Step 5: each of defining and initialize load the tendency coefficient and probability coefficent of strategy；By each load of probability selection
Action policy, action policy is showed with the coordinate form in Netlogo, each load moves to Netlogo respectively
In three-dimensional level on corresponding position, different positions corresponds to different strategies；
The initial electricity price of setting network node obtains each load at this time according to corresponding load-Respondence to the Price of Electric Power characteristic model
Power, and load agency is combined to be directed to the administration of corresponding load, obtains the general power of each load agency respectively；
Step 6: the general power of each load agency is passed through into the data exchange interface between Matlab and Netlogo
Module is sent in Matlab by Netlogo, is carried out market for the general power of each load agency respectively in Matlab and is gone out
Clearly, the electricity price of corresponding each network node is obtained to update the electricity price of each load bus in Netlogo, then by each load
Node electricity price is handed down to its load administered by agency；
Step 7: each load calculates selected strategy to obtain the study effectiveness of each load strategy, according to amendment
Parameter modifies the selected probability of each strategy in the action policy set, i.e., the described probability coefficent deploys suitable feelings in coefficient
Under condition, system reaches convergence state, i.e., the probability that some action policy is selected levels off to 1, then meets intensified learning process
In termination condition, when not meeting termination condition, circulation execute step 5,6,7；
Preferably, the multiple agent general module includes communication attributes submodule, intelligent attributes submodule and physics category
Temper module；
The communication attributes submodule is used to simulate the information exchanging process between power system component；
The intelligent attributes submodule is used to describe the process that power system component formulates decision；
The physical attribute submodule is used to define the operating status of power system component.
Preferably, in the step 1, the load agency administers all loads under corresponding load node, and is used for it
Information transmission between each load bus and Matlab of administration；
The data interaction interface module is for the data exchange between Matlab and Netlogo.
Preferably, in the step 2, the load-Respondence to the Price of Electric Power characteristic model is by electricity price signal and load itself mesh
The load model that mark fusion considers, such as following formula:
Wherein QiFor load demand, Qi0For load datum quantity, terminal user in area is specialized in power supply by load agency
Load prediction obtains；μiFor economic sexual orientation degree,For comfort level tendency degree, ρiIt is power supply company to the electricity of terminal user's sale of electricity
Valence；
The load own target includes economy and comfort level.
Preferably, in the step 3, the catalogue scalar functions RiInclined by each objective function multiplied by its corresponding target
It is superimposed and obtains after to degree, be shown below:
Wherein, BiFor load profit, CiFor load comfort level.
Preferably, in the step 6, the load is special by load-Respondence to the Price of Electric Power according to initial electricity price and current strategies
Property model obtains the power of each load at this time, to obtain the workload demand amount of load bus in power grid；
Each section is calculated according to the workload demand of power grid at this time using the optimal load flow calculating instrument in Matlab
The electricity price and every generator output situation of point.
Preferably, for each movement, the load i is labeled with the tendency coefficient qiWith the probability coefficent pi, and
Strategy renewing new method in control process is as follows:
Assuming that the optional strategy set of the load i are as follows:
If the strategy after repeated game D wheelIt is selected, wherein [0, m] x ∈, y ∈ [0, m], D wheel updates tendency at this time
Coefficient is qi(x,y)(D), it is p that D, which takes turns update probability coefficient,i(x,y)(D), the target value of this wheel of load i is profiti(x,y)(D)；
If the then strategy after repeated game D+1 wheelIt is selected, wherein x1∈[0,m],y1∈ [0, m], D+1 takes turns at this time
Update tendency coefficientAre as follows:
In formula,For strategyCoefficient is inclined in update when D wheel is selected, and r is forgetting factor,For strategyCatalogue scalar functions when D wheel is selected: work as x1≠ x or y1When ≠ y,Work as x1=x and y1When=y,Wherein e is an empirical parameter, for load in repeated game early learning rank
Encouragement is played the role of in a variety of different quotation strategies of Duan Shengcheng；
D+1 takes turns update probability coefficient at this timeAre as follows:
Load i selects the strategy interaction of next round according to roulette mode according to new select probability, for each behavior
Movement, load are all flagged with tendency coefficient qiWith probability coefficent pi, every wheel strategy is all according to the update of catalogue scale value.
Compared with prior art, the invention has the following advantages:
Control method proposed by the present invention has many advantages, such as that calculating speed is fast, and convergence is strong, can be for the more of intelligent body
Denaturation is controlled, and is coped with extraneous disturbance, is made positive reaction.
The present invention, which compares previous electric system simulation system, has the characteristics that simulation process is intuitive visible, in whole process
The state change situation of each intelligent element module can be clear that in Netlogo.
Detailed description of the invention
Fig. 1: power system load MAS control method flow diagram provided by the invention；
Fig. 2: three machines, nine node system figure of present invention implementation example；
Fig. 3: three machines, the nine meshed network Simulation Interface figure that the present invention carries out.
Specific embodiment
A specific embodiment of the invention is described in further detail with reference to the accompanying drawing.
Technical solution provided by the invention is a kind of more distributed control methods of power system load, and the method is specific
Steps are as follows:
Step 1: according to power system network structure, establishing the union simulation platform based on Matlab and Netlogo,
In, Matlab is responsible for every operation of electric system, and Netlogo undertakes Power System Intelligent element model and power load distributing formula
The work of control, and according to the load bus in Netlogo three-dimensional level, establishes load agency, the quantity of load agency with
The quantity of load bus is consistent, and load agency corresponds with load bus, each load agency administration corresponding load, and each
A load agency is respectively used to the information transmission between each load and Matlab of its administration；Meanwhile build Matlab and
Data exchange interface module between Netlogo realizes information exchange；
Step 2: setting each load, there are two the targets itself to be pursued: economy and comfort level.So that it is determined that
The initial model of four kinds of loads: input quantity is electricity price ρiAnd economic sexual orientation degree μiWith comfort level tendency degreeOutput quantity is
Performance number.Load modeling is as follows:Wherein Qi0By load agency to terminal in power supply franchise area
The load prediction of user obtains；μiTerminal user's workload demand is reflected to sales rate of electricity ρiResponse, different types of user couple
The μ answerediCoefficient is different, is obtained by analyzing the data of acquisition；Response of terminal user's workload demand to comfort level is reflected,
Different types of user is correspondingCoefficient is different, is obtained by analyzing the data of acquisition；ρiIt is sold for power supply company to terminal user
The electricity price of electricity.
Step 3: for the load model built, the objective function and catalogue scalar functions of each target of load are determined.Load
Profit:Load comfort level:Wherein μi∈[0,
1],To obtain catalogue scalar functions:
Step 4: by the strategy of load iEquidistant discretization (m+1) × (m+1) part processing, therefore load i
Set of strategies beThe wherein tactful meaning of serial number x × y are as follows:x
∈ [0, m], y ∈ [0, m].
Step 5: each strategy interaction of load is flagged with tendency coefficient qiWith probability coefficent pi, initialize the every of load
A strategy tendency coefficient is 1, and probability coefficent isThe movement plan of each load is selected by probability (such as roulette)
SlightlyEach load is moved in Netlogo three-dimensional level respectively on corresponding position.Setting network node is initially electric
Valence obtains the power of each load at this time then according to corresponding load-Respondence to the Price of Electric Power characteristic model, and combines load generation
Reason is directed to the administration of corresponding load, obtains the general power of each load agency respectively；
Step 6: the general power P that each load is acted on behalf ofL1、PL2、PL3... pass through the number between Matlab and Netlogo
It according to exchange interface module, is sent in Matlab by Netlogo, respectively for the general power of each load agency in Matlab
Market clearing is carried out, the electricity price C of corresponding each network node is obtained1、C2、C3....Netlogo passes through Matlab and Netlogo
Between data exchange interface call the node electricity price that is calculated in Matlab, update each load agency in Netlogo
Electricity price is acted on behalf of by each load node electricity price being handed down to its load administered；
Step 7: each load calculates selected strategy to obtain the study effectiveness of each load strategy, according to amendment
Parameter modifies the selected probability of each strategy in the action policy set, i.e., the described probability coefficent deploys suitable feelings in coefficient
Under condition, system reaches convergence state, i.e., the probability that some action policy is selected levels off to 1, then meets intensified learning process
In termination condition, when not meeting termination condition, circulation execute step 5,6,7；
For each movement, the load i is labeled with the tendency coefficient qiWith the probability coefficent pi, and controlling
Strategy renewing new method in journey is as follows:
Assuming that the optional strategy set of the load i are as follows:
If the strategy after repeated game D wheelIt is selected, wherein [0, m] x ∈, y ∈ [0, m], D wheel updates tendency at this time
Coefficient is qi(x,y)(D), it is p that D, which takes turns update probability coefficient,i(x,y)(D), the target value of this wheel of load i is profiti(x,y)(D)；
If the then strategy after repeated game D+1 wheelIt is selected, wherein x1∈[0,m],y1∈ [0, m], D+1 takes turns at this time
Update tendency coefficientAre as follows:
In formula,For strategyCoefficient is inclined in update when D wheel is selected, and r is forgetting factor,For strategyCatalogue scalar functions when D wheel is selected: work as x1≠ x or y1When ≠ y,Work as x1=x and y1When=y,
Wherein e is an empirical parameter, generates a variety of different quotation strategies in the repeated game early learning stage for load and plays
Encouragement effect；
D+1 takes turns update probability coefficient at this timeAre as follows:
Load i selects the strategy interaction of next round according to roulette mode according to new select probability, for each behavior
Movement, load are all flagged with tendency coefficient qiWith probability coefficent pi, every wheel strategy is all according to the update of catalogue scale value.
Finally it should be noted that: above embodiments are merely to illustrate the technical solution of the application rather than to its protection scopes
Limitation, although the application is described in detail referring to above-described embodiment, those of ordinary skill in the art should
Understand: those skilled in the art read the specific embodiment of application can still be carried out after the application various changes, modification or
Person's equivalent replacement, but these changes, modification or equivalent replacement, are applying within pending claims.
Claims (5)
1. a kind of power load distributing formula control method based on Netlogo and Matlab, which is characterized in that the control method is based on strong
Change learning algorithm, to each load multiple agent general module Constructing Policy collection, corresponding catalogue scalar functions are selected in set of strategies
It is worth maximum strategy, includes the following steps:
Step 1: establish union simulation platform and data interaction interface module based on Matlab and Netlogo: Matlab is used for
Every operation of electric system；Netlogo is for Power System Intelligent element model, the control of power load distributing formula and load agency
It establishes；
Step 2: respectively according to load datum quantity, electricity price, economic sexual orientation degree and comfort level tendency degree, establishing load-electricity price and ring
Answer characteristic model；
Step 3: according to the load-Respondence to the Price of Electric Power characteristic model, obtaining the catalogue scalar functions of each load；
Step 4: each goal orientation degree size of assumed load is between 0-1, the set of the goal orientation degree composition of all targets
Referred to as load action policy set；By equidistant discretization m parts of 0-1, the goal orientation degree of each target has m selection；
Step 5: each of defining and initialize load the tendency coefficient and probability coefficent of strategy；By the dynamic of each load of probability selection
Make strategy, action policy is showed with the coordinate form in Netlogo, each load moves to Netlogo three-dimensional respectively
In level on corresponding position, different positions corresponds to different strategies；
The initial electricity price of setting network node obtains the function of each load at this time according to corresponding load-Respondence to the Price of Electric Power characteristic model
Rate, and load agency is combined to be directed to the administration of corresponding load, the general power of each load agency is obtained respectively；
Step 6: the general power of each load agency is passed through into the data exchange interface mould between Matlab and Netlogo
Block is sent in Matlab by Netlogo, is carried out market for the general power of each load agency respectively in Matlab and is gone out
Clearly, the electricity price of corresponding each network node is obtained to update the electricity price of each load bus in Netlogo, then by each load
Node electricity price is handed down to its load administered by agency；
Step 7: each load calculates selected strategy to obtain the study effectiveness of each load strategy, according to corrected parameter
Modify the selected probability of each strategy in the action policy set, i.e., the described probability coefficent, in the case where coefficient deploys suitable situation,
System reaches convergence state, i.e., the probability that some action policy is selected levels off to 1, then meets the end during intensified learning
Only condition, when not meeting termination condition, circulation executes step 5,6,7；
In the step 2, the load-Respondence to the Price of Electric Power characteristic model is that electricity price signal is merged consideration with load own target
Load model is shown below:
Wherein QiFor load demand, Qi0For load datum quantity, the load of terminal user in area is specialized in power supply by load agency
Prediction obtains；μiFor economic sexual orientation degree,For comfort level tendency degree, ρiIt is power supply company to the electricity price of terminal user's sale of electricity；
The load own target includes economy and comfort level；
In the step 3, the catalogue scalar functions RiBy each objective function multiplied by being superimposed after its corresponding goal orientation degree and
, it is shown below:
BiFor load profit, CiFor load comfort level.
2. control method according to claim 1, which is characterized in that the multiple agent general module includes communication attributes
Submodule, intelligent attributes submodule and physical attribute submodule；
The communication attributes submodule is used to simulate the information exchanging process between power system component；
The intelligent attributes submodule is used to describe the process that power system component formulates decision；
The physical attribute submodule is used to define the operating status of power system component.
3. control method according to claim 1, which is characterized in that in the step 1, the load agency administration is corresponded to
All loads under load bus, and for the information transmission between each load bus and Matlab of its administration；
The data interaction interface module is for the data exchange between Matlab and Netlogo.
4. control method according to claim 1, which is characterized in that in the step 6, the load is according to initial electricity price
And current strategies, the power of each load at this time is obtained by load-Respondence to the Price of Electric Power characteristic model, to obtain load in power grid
The workload demand amount of node；
Each node is calculated according to the workload demand amount of power grid at this time using the optimal load flow calculating instrument in Matlab
Electricity price and every generator output situation.
5. control method according to claim 1, which is characterized in that for each movement, the load i is labeled with described
It is inclined to coefficient qiWith the probability coefficent pi, and the strategy renewing new method in control process is as follows:
Assuming that the optional strategy set of the load i are as follows:
If the strategy after repeated game D wheelIt is selected, wherein [0, m] x ∈, y ∈ [0, m], D wheel updates tendency coefficient at this time
For qi(x,y)(D), it is p that D, which takes turns update probability coefficient,i(x,y)(D), the target value of this wheel of load i is profiti(x,y)(D)；
If the then strategy after repeated game D+1 wheelIt is selected, wherein x1∈[0,m],y1∈ [0, m], D+1 wheel updates at this time
It is inclined to coefficientAre as follows:
In formula,For strategyCoefficient is inclined in update when D wheel is selected, and r is forgetting factor,
For strategyCatalogue scalar functions when D wheel is selected: work as x1≠ x or y1When ≠ y,Work as x1=x and y1When=y,Wherein e is an empirical parameter, for load in repeated game early learning rank
Encouragement is played the role of in a variety of different quotation strategies of Duan Shengcheng；
D+1 takes turns update probability coefficient at this timeAre as follows:
Load i selects the strategy interaction of next round according to roulette mode according to new select probability, dynamic for each behavior
Make, load is all flagged with tendency coefficient qiWith probability coefficent pi, every wheel strategy is all according to the update of catalogue scale value.
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