CN105391090B - A kind of intelligent grid multiple agent multiple target uniformity optimization method - Google Patents
A kind of intelligent grid multiple agent multiple target uniformity optimization method 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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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Abstract
The invention discloses a kind of intelligent grid multiple agent multiple target uniformity optimization method, it is characterized in that：According to the characteristics of the system of intelligent grid multiple agent Collaborative Control factor, analyze the different characteristic features of different force devices, and the target call each proposed, appropriate object function is chosen when target is various and obtains optimization operation control method and parameter, ensure the reliability and economy during system operation, and verify the validity of Optimal Operation Strategies.The present invention can not only be according to power network and part throttle characteristics, establish the Optimized model of multiple agent, the distributed generating optimization algorithm for considering part information sharing is studied using multi-agent theory, it can also carry out simulation analysis to example and study to improve the constringent correlation technique of distributed algorithm according to the convergence of different communication topology parsers.
Description
Technical field
The invention belongs to intelligent grid to optimize coordinated scheduling technical field, is related to a kind of multi-objective coordinated control of multiple agent
Intelligent grid Optimal Operation Strategies, and in particular to a kind of intelligent grid multiple agent multiple target uniformity optimization method.
Background technology
Intelligent grid is an important branch of artificial intelligence, is artificial intelligence in the world at the beginning of 20 end of the centurys to 21 century
Front subject.With computer technology, fast-developing and to modern science the continuous spy of artificial intelligence theory, control theory
Rope, intelligent grid have turned into one of hot issue of different ambits research.The distributed collaboration of intelligent grid is controlled to carrying
High distribution network reliability, improve the quality of power supply, improve power distribution network performance driving economy, optimization distribution network operation arrangement etc. all with ten
Divide important meaning.
Power-balance controls, i.e. Real-time Economic Dispatch, is a basic problem in Operation of Electric Systems, it refers to generate electricity
Machine and flexible load make the maximization of economic benefit of whole Operation of Electric Systems under conditions of a series of operation constraints are met
Optimization problem.Traditionally solves Economic Dispatch Problem using centralized optimization technology, including classic optimization method and modern times
Artificial intelligence approach.
However, when using centralized optimization method, system needs to own in control centre's issue instruction scheduling whole system
Generator and flexible load, control centre need with each scheduler object carry out information exchange.Also, flexible load is wide
" plug and play " technology that general infiltration and force device need will make power network and communication network topological structure changeable, cause to collect
Middle optimization method needs higher communication topology construction cost.Therefore, it is necessary to which the stronger optimized algorithm of adaptability, limited in communication
Remain to effectively run in the case of failing with unreliable or even control centre.
The content of the invention
It is an object of the invention in place of overcome the deficiencies in the prior art, there is provided a kind of intelligent grid multiple agent multiple target
Uniformity optimization method, is required according to net load interaction, more from intelligent grid with reference to different type intelligent grid multiple agent characteristic
The angle of intelligent body multiple-target system uniformity, which is established, coordinates Controlling model.The present invention can not only be special according to power network and load
Property, the Optimized model of multiple agent is established, is studied using multi-agent theory and considers that the distribution output of part information sharing is excellent
Change algorithm, moreover it is possible to according to the convergence of different communication topology parsers, simulation analysis are carried out to example and study raising point
The correlation technique of cloth Algorithm Convergence.
In order to solve the above technical problems, the present invention uses following technical scheme：
A kind of intelligent grid multiple agent multiple target uniformity optimization method, it is characterised in that according to the more intelligence of intelligent grid
The characteristics of system of energy body Collaborative Control factor, the different characteristic features of different force devices are analyzed, and each proposed
Target call, appropriate object function is chosen when target is various and obtains optimization operation control method and parameter, it is ensured that system is transported
Reliability and economy during row, and verify the validity of Optimal Operation Strategies；Implementation step includes：
Step 1, according to power system network structure, the union simulation platform based on MATLAB and NETLOGO is established, its
In, power system component model is established in MATLAB, the intelligent body that power system component is represented defined in NETLOGO is general
Module, meanwhile, the data exchange interface module built between MATLAB and NETLOGO realizes information exchange；
Step 2, for various load types, respectively according to each target of load datum quantity, electricity price, and corresponding load
Goal orientation degree, establish the load-Respondence to the Price of Electric Power characteristic model for corresponding respectively to various loads and power supply type；Described load
Including rigid load and flexible load, described power supply includes distributed power source and energy-storage travelling wave tube；Wherein, rigid load refers to not
The interactive load of power network is participated in, flexible load refers to participate in the interactive load of power network；
Step 3, according to the load for corresponding to various load types respectively established in the step 2-Respondence to the Price of Electric Power characteristic mould
Type, the object function of each target of each load is obtained respectively；And each load is directed to respectively, by each target of load
Object function be weighted processing, obtain the catalogue scalar functions of corresponding each load respectively；
Step 4, described each load is randomly dispersed in NETLOGO three-dimensional aspects, obtains the initial of each load
Strategy；For the network node in NETLOGO three-dimensional aspects, electricity price is set at random, and establishes load agency；
Step 5, using the initial policy of each load as load datum quantity, respectively for each mesh of each load
Target goal orientation degree, obtain strategy corresponding to each load respectively by the way of+i or-i, and combine the first of each load
Begin the tactful set of strategies for forming each load；Described i is each step iteration step length, and described step refers to that electricity price often changes one
Secondary, the tactful respective change of load is once；
Step 6, using the intelligent grid uniformity optimized algorithm of the multi-objective coordinated control of multiple agent, respectively to each negative
The catalogue scalar functions of lotus optimize coordination computing, and each load of selection acquisition corresponds to its maximum general objective functional value respectively
Strategy, the preference policy as each load；
Make xiThe state of force device is represented, according to consistency protocol, and if only if, and network opens up all nodes of bowl spares
When state value is all equal, unanimously, i.e., the node of the network has all reached：
x1=x2=...=xn
The intelligent grid uniformity optimized algorithm of the multi-objective coordinated control of described multiple agent, including procedure below：
Assuming that the cost of electricity-generating function of generating set and the electricity consumption benefit function of flexible load are quadratic function, generator
The cost of electricity-generating function of group is as follows：
Ci(PGi)=αi+βiPGi+γiP2 Gi, i ∈ SG
The electricity consumption benefit function of flexible load is as follows：
Bi(PDi)=ai+biPDi+ciP2 Di,i∈SD
Economic Dispatch Problem refers to that generator and flexible load under conditions of a series of operation constraints are met, make whole electricity
The optimization problem of the maximization of economic benefit of Force system operation, i.e.,：
PGi,min≤PGi≤PGi,max, i ∈ SG
PDj,min≤PDj≤PDj,max, j ∈ SD
Wherein, PDjRepresent flexible load j demand power, PGiRepresent generating set i power output；SGRepresent generator
Set, SDRepresent flexible load set；Solved using the method for Lagrange multipliers of classics, make λ represent corresponding with equality constraint
Lagrange multiplier, do not consider to constrain, above-mentioned RegionAlgorithm for Equality Constrained Optimization can be converted into：
To variable PGi,PDjLocal derviation is asked to obtain optimality condition with λ, i.e.,：
Above formula is the equation of comptability, can be obtained according to the equation of comptability：
That is the optimal solution of economic load dispatching is to make the incremental cost of generator equal with the increment benefit of flexible load, wherein m
Generator number is represented, k represents the number of flexible load；
Assuming that all flexible loads are run with generating set in its power constraints；In the consistency algorithm
In, the IC of generating set and the IB of flexible load are defined as follows：
IC and IB is selected as uniformity variable, using consistency algorithm, from generating set (Follower
Generator the more new formula of IC) is：
More new formula from the IB of load (Follower Load) is：
In order to meet the power-balance constraint in power system, represent flexible load actual demand power with generating electricity with Δ P
Difference between unit real output：
The IC of main generator group (Leader Generator) more new formula is：
The IB of main load (Leader Load) more new formula is：
Wherein ε is convergence coefficient, is a positive scalar, and it restrains with the distributed optimization of main generator group and main load
Speed is relevant；
Step 7, the goal orientation degree of each target in the preference policy of described each load respectively, will be each
Load is moved in NETLOGO three-dimensional aspects on corresponding position respectively, and the target for updating each target of each load is inclined
Xiang Du；Then load-Respondence to the Price of Electric Power characteristic model corresponding to, obtains the power of now each load, and combines load
Agency obtains the general power that each load is acted on behalf of respectively for the administration of corresponding load；
Step 8, the general power that described each load is acted on behalf of is sent into MATLAB by NETLOGO, in MATLAB
The electricity price of generator output and corresponding each network node is obtained, and is back in NETLOGO, updates NETLOGO three-dimensional aspects
Electricity price on middle map network node；
Step 9, using the electricity price in described NETLOGO three-dimensional aspects on each network node as traction signal, and point
Electricity price on map network node is not distributed to each load of its administration by described each load agency；
Step 10, the position of each load when being completed according to the step 9 in NETLOGO three-dimensional aspects, and it is each
The goal orientation degree of each target of load, updates the initial policy of each load, and according to the method in the step 5, more
Set of strategies corresponding to new each load, it is corresponding with reference to each load then according to the catalogue scalar functions of corresponding each load
Electricity price, obtain each load respectively and correspond to each tactful general objective functional value in its set of strategies；
Step 11, judge whether general objective functional value is more than corresponding to the initial policy of load for each load respectively
General objective functional value in its set of strategies corresponding to other strategies, is the then load stop motion；Otherwise return to step 4.
In the step 1, union simulation platform of the described foundation based on MATLAB and NETLOGO, refer to：
A kind of intelligent grid Multi-Agent simulation platform being made up of MATLAB and NETLOGO, wherein utilizing MATLAB's
Computing function and programming technique, to establish the model of power system component and establish complicated electric power networks simulation model；And
NETLOGO is a programmable modeling environment emulated to nature and social phenomenon, suitable for the complexity to Temporal Evolution
System is modeled；Described NETLOGO completes building for power system component general module, and MATLAB carries out power system
Items calculate, and the network parameter for solving to obtain realizes information exchange by the interface routine between MATLAB and NETLOGO.
Described to obtain the catalogue scalar functions for corresponding to each flexible load respectively in the step 3, its process is：
If economic benefit BkAs the income of force device, it is defined as follows：
Wherein EkFor the summation of net input and output, ρkThe price of electricity, D are bought for loadkFor load reference power, BkFor economy
Benefit,
μkFor the tendency degree of economy,For the tendency degree of comfort level, υkThe price of electricity, G are sold for distributed power sourcekTo divide
Cloth power source reference power；
It is as follows to define force device comfort level：
Wherein CkFor force device comfort level；
The overall utility of force device is weighted to obtain general objective function representation, the definition of catalogue scalar functions by two object functions
It is as follows：
Wherein RkFor the overall utility of force device.
It is described that each load is randomly dispersed in NETLOGO three-dimensional aspects in the step 4, form multiple negative
Lotus node, and obtain the initial target tendency degree of each target of each load, the initial policy of as each load, its process
For：
For the network node in described NETLOGO three-dimensional aspects, electricity price is set at random, and according to NETLOGO tri-
Load bus in dimension aspect, load agency is established, the quantity of described load agency is consistent with the quantity of load bus, described
Load agency corresponded with load bus, the corresponding each load of described each load agency's administration, and described each
Individual load agency is respectively used to the information transfer between each load and MATLAB of its administration.
In the step 5, the initial policy using each load is as load datum quantity, respectively for each negative
The goal orientation degree of each target of lotus, obtain strategy corresponding to each load respectively by the way of+i or-i, and combine each
The initial policy of individual load forms the set of strategies of each load：
Wherein, i=1, in NETLOGO three-dimensional aspects, eight points are included around each load, eight points are respectively That is the corresponding eight different strategies of each load, the set of strategies of each load is respectively constituted.
The implementation process of the step 8 is：
The general power that described each load is acted on behalf of is passed through into the data exchange interface mould between MATLAB and NETLOGO
Block, sent by NETLOGO into MATLAB, optimal load flow is carried out for the general power of each load agency respectively in MATLAB
Calculate, obtain the electricity price of generator output and corresponding each network node, and by the electricity price of each network node, pass through
Data exchange interface module between MATLAB and NETLOGO is back in NETLOGO, and it is right in NETLOGO three-dimensional aspects to update
Answer the electricity price on network node.
In the step 9, the electricity price in the three-dimensional aspect using NETLOGO on each network node is believed as traction
Number, and each load that the electricity price on map network node is distributed to its administration is acted on behalf of by each load respectively, refer to：
Electric power system dispatching platform is often run once with a fixed time period, is calculated at each period end real-time
Electricity price, prediction in short-term electricity price, calculate mains frequency and node voltage, and to each load agency, big load issue the period electricity price,
Frequency, voltage, it is necessary to when issue history and forecasted electricity market price, frequency, voltage before and after the period simultaneously；The electricity price, frequency, electricity
Pressure is referred to as traction signal, instructs to draw each load adjustment itself power demand, is serviced while number one is maximized
In power network.
In the step 10, described each load that obtains respectively corresponds to each tactful catalogue offer of tender in its set of strategies
Numerical value, refer to：
Each load is directed to respectively, judges whether general objective functional value corresponding to the initial policy of load is more than its set of strategies
In it is other strategy corresponding to general objective functional value, if greater than the then load stop motion.
Compared with prior art, the present invention is containing having the advantage that and beneficial effect：
(1) present invention establishes the union simulation platform based on MATLAB and NETLOGO according to power system network structure,
The data exchange interface module built between MATLAB and NETLOGO realizes information exchange, it is proposed that required according to net load interaction,
With reference to different type intelligent grid multiple agent characteristic, established from the angle of intelligent grid multiple agent multiple-target system uniformity
Coordinate Controlling model, appropriate object function is chosen when target is various and obtains optimization operation control method and parameter, it is ensured that is
Reliability and economy during system operation, and verify the validity of Optimal Operation Strategies；
(2) present invention considers that the system containing flexible load multiple agent Collaborative Control has the characteristics of its is peculiar, and this kind of
Vied each other between model attributes game, so the intelligent grid distributed consensus using the multi-objective coordinated control of multiple agent
Optimized algorithm and Optimal Operation Strategies, on the basis of system reliability is ensured, make system that there is good optimization operational effect,
Effectively verify the multi-objective coordinated control optimization operation result of multiple agent；
(3) distributed net load interaction multi-agent system Controlling model is the composite can be widely applied to, especially suitable for soft
Intelligent grid multiple agent multiple target uniformity optimization method under property load.
Brief description of the drawings
Fig. 1 is a kind of intelligent grid multiple agent multiple target uniformity optimization method flow chart of the present invention.
Fig. 2 is the action space schematic diagram of the load k standardization of the present invention.
Fig. 3 is the power network Multi-Agent simulation plateform system based on NETLOGO of the present invention.
Embodiment
The present invention is described in further details with reference to the accompanying drawings and examples.
Fig. 1 show a kind of flow chart of intelligent grid multiple agent multiple target uniformity optimization method of the present invention.This
Inventive method analyzes the different allusion quotations of different force devices according to the characteristics of the system of intelligent grid multiple agent Collaborative Control factor
Type feature, and the target call each proposed, appropriate object function is chosen when target is various and obtains optimization operation control
Mode and parameter processed, it is ensured that reliability and economy during system operation, and verify the validity of Optimal Operation Strategies；It is implemented
Step includes：
Step 1, according to power system network structure, the union simulation platform based on MATLAB and NETLOGO is established, its
In, power system component model is established in MATLAB, the intelligent body that power system component is represented defined in NETLOGO is general
Module, meanwhile, the data exchange interface module built between MATLAB and NETLOGO realizes information exchange.
Union simulation platform of the described foundation based on MATLAB and NETLOGO, refers to：It is a kind of by MATLAB with
The intelligent grid Multi-Agent simulation platform that NETLOGO is formed, wherein using MATLAB computing function and programming technique, to build
The model of vertical power system component and the electric power networks simulation model for establishing complexity；And NETLOGO is one to natural and society
The programmable modeling environment that phenomenon is emulated, suitable for being modeled to the complication system of Temporal Evolution；Described NETLOGO
Building for power system component general module is completed, MATLAB carries out every calculating of power system, solves obtained network ginseng
Number realizes information exchange by the interface routine between MATLAB and NETLOGO.
The described power network Multi-Agent simulation platform being made up of MATLAB and NETLOGO is as shown in figure 3, power system is adjusted
Platform and the more intelligent simulation platforms of NETLOGO are spent by MATLAB interfaces, realize the MAS control of load.Power system is adjusted
Spend platform and be mainly responsible for electricity price calculating and prediction, while carry out the Power System Dynamic Simulation of correlation.With based on response electricity price
Exemplified by load emulation, electric power system dispatching platform then needs to carry out optimal load flow calculating, obtains the electricity of now power network interdependent node
Valency, while the load that the electricity price is assigned in NETLOGO by NETLOGO and MATLAB interfaces is acted on behalf of.And NETLOGO is emulated
Platform mainly complete operation of power networks environment build and the modeling work of electric network element, be embodied in and taken in NETLOGO
Build topological structure, load agency and three layers of operation of power networks environment of load group；Simultaneously according to the respective characteristic of load group,
Its characteristic is modeled in NETLOGO.NETLOGO is substantially carried out history electricity price, Spot Price, prediction electricity with MATLAB interfaces
The data of valency and related traction signal between NETLOGO and MATLAB communicate.
Step 2, for various load types, respectively according to each target of load datum quantity, electricity price, and corresponding load
Goal orientation degree, establish the load-Respondence to the Price of Electric Power characteristic model for corresponding respectively to various loads and power supply type；In power system
In diversified load and power supply be present, described load includes rigid load and flexible load, and described power supply includes point
Cloth power supply and energy-storage travelling wave tube；Wherein, rigid load refers to be not involved in the interactive load of power network, and flexible load refers to participate in power network
Interactive load.
It is modeled respectively for load and power supply different qualities.The electricity price perunit value difference that load power consumption and power supply generate electricity
For [ρk,υk].In simulation framework proposed by the invention, electricity price is to [ρk,υk] it is that Agent is assigned to leading for each load
Fuse number.For the load on different buses, each pair electricity price is probably different.
In the present invention it is assumed that the load and power supply that use are to pursue economy and comfort level as target.In prior art
In, the Respondence to the Price of Electric Power characteristic of load and the generating of power supply arrange to be not consider comfort level.Accordingly, it is considered to after comfort level, load
Demand can not be according to conventional model Accurate Prediction, and the output of power supply can not obtain according to traditional generating arrangement method.
The behavior that load and power supply pursue respective target can be equivalent to corresponding 2-D spaces：1) to the tendency degree μ of economyk, performance
The maximization of consumer cost is avoided on the one hand, is on the other hand obtained and is maximized economic well-being of workers and staff.2) to the tendency degree of comfort level
Performance individual considers itself desire and wish, and they understand use device or equipment to meet their standard of living (physiology side
Face).The goal behavior feature of each load is usedDescription, AkIn economy (as shown in Fig. 2 abscissa) and relax
Different values is endowed in terms of appropriateness (as shown in Fig. 2 ordinate) two.
The efficiency that load obtains from electricity consumption can be quantified in terms of the economic benefit and in terms of comfort level, the two sides
The value in face depends on behavior pattern of the individual to power input output facet.Here, define the power of the load and power supply
Input and output are：
1) rigid load：Load qkDo not change with electricity price, that is, be not involved in the interactive load of power network；
2) flexible load：Refer to and participate in the interactive load of power network,Wherein dkNeeded for load
The amount of asking, DkFor load reference power, ρkThe price of electricity is bought for load；
3) distributed power source：Wherein gkFor the generated energy of distributed power source, GkTo divide
Cloth power source reference power, υkThe price of electricity is sold for distributed power source；
4) energy-storage travelling wave tube：It is during charging
It is during electric discharge
The network power output of load is that the demand of reference power is calculated.The reference work(of load in this model
Rate is constant, the problem of not being related to technical elements.Load-Respondence to the Price of Electric Power characteristic is by the state parameter μ in formulak,Determine, its
Described in price ρk, υkRelated demand slip and production increase rate beSo as to cause the elasticity of power to become
Change.In addition, with traditional model based on fixing response on the contrary, in our method, social action is clearly modeled, and synthesis is examined
Consider due to Flexible change caused by the interaction of social action.
Load is as shown in Figure 2 in the state position in space.If load k is in position AkPlace, represents that the load only considers to pass through
Ji interests：ρk(υk) growth can cause decreasing or increasing for power output.Position B means that it can not be according to the change of price
And change output or the input quantity of electric energy, what it considered is comfort level.Compared with both of these case, not in borderline position,
Its price and a certain degree of economic interests and comfort level are all related.For example, load k is located at point C, for economy, in valency
Lattice ρkWhen=1, power demand reduces 0.3, and for comfort level, power consumption will reduce 0.3.This is meant that in price ρk=1
When final demand will reduce 0.3* (1-0.3).Similarly at point D, in price ρkFinal demand will reduce 0.7* (1- when=1
0.7).In price υkFinal generated energy will reduce 0.7* (1-0.7) when=1.For theoretically, ρkAnd υkIt can be separately provided.
But to prevent load arbitrage, it is assumed that ρk=-υk。
As the income of load, economic benefit BkIt is defined as follows：
Wherein EkFor the summation of net input and output.
Load comfort level is defined as follows：
The overall utility of load is weighted to obtain general objective function representation by two object functions, and catalogue scalar functions define such as
Under：
Step 3, according to the load for corresponding to various load types respectively established in the step 2-Respondence to the Price of Electric Power characteristic mould
Type, the object function of each target of each load is obtained respectively；And each load is directed to respectively, by each target of load
Object function be weighted processing, obtain the catalogue scalar functions of corresponding each load respectively.
The described catalogue scalar functions for obtaining corresponding each flexible load respectively, its process are：
If economic benefit BkAs the income of force device, it is defined as follows：
Wherein EkFor the summation of net input and output, ρkThe price of electricity, D are bought for loadkFor load reference power, BkFor economy
Benefit,
μkFor the tendency degree of economy,For the tendency degree of comfort level, υkThe price of electricity, G are sold for distributed power sourcekTo divide
Cloth power source reference power；
It is as follows to define force device comfort level：
Wherein CkFor force device comfort level；
The overall utility of force device is weighted to obtain general objective function representation, the definition of catalogue scalar functions by two object functions
It is as follows：
Wherein RkFor the overall utility of force device.
Step 4, described each load is randomly dispersed in NETLOGO three-dimensional aspects, obtains the initial of each load
Strategy；For the network node in NETLOGO three-dimensional aspects, electricity price is set at random, and establishes load agency, and its process is：
For the network node in described NETLOGO three-dimensional aspects, electricity price is set at random, and according to NETLOGO tri-
Load bus in dimension aspect, load agency is established, the quantity of described load agency is consistent with the quantity of load bus, described
Load agency corresponded with load bus, the corresponding each load of described each load agency's administration, and described each
Individual load agency is respectively used to the information transfer between each load and MATLAB of its administration.
Step 5, using the initial policy of each load as load datum quantity, respectively for each mesh of each load
Target goal orientation degree, obtain strategy corresponding to each load respectively by the way of+i or-i, and combine the first of each load
Begin the tactful set of strategies for forming each load；Described i is each step iteration step length, and described step refers to that electricity price often changes one
Secondary, the tactful respective change of load is once.
In the step 5, the initial policy using each load is as load datum quantity, respectively for each negative
The goal orientation degree of each target of lotus, obtain strategy corresponding to each load respectively by the way of+i or-i, and combine each
The initial policy of individual load forms the set of strategies of each load：
Wherein, i=1, in NETLOGO three-dimensional aspects, eight points are included around each load, eight points are respectively That is the corresponding eight different strategies of each load, the set of strategies of each load is respectively constituted.
Step 6, using the intelligent grid uniformity optimized algorithm of the multi-objective coordinated control of multiple agent, respectively to each negative
The catalogue scalar functions of lotus optimize coordination computing, and each load of selection acquisition corresponds to its maximum general objective functional value respectively
Strategy, the preference policy as each load.
Make xiThe state of force device is represented, according to consistency protocol, and if only if, and network opens up all nodes of bowl spares
When state value is all equal, unanimously, i.e., the node of the network has all reached：
x1=x2=...=xn
The intelligent grid uniformity optimized algorithm of the multi-objective coordinated control of described multiple agent, it is using distributed economical
Scheduling strategy, refer to:
Under flexible load, the target of Economic Dispatch is social welfare maximum.From the angle of distributed optimization,
Using consistency algorithm, the incremental cost (IC) of generating set and the increment benefit (IB) of flexible load are become as uniformity
Amount, Economic Dispatch Problem are solved by way of distributed optimization.The sheet being embedded into each generating set and flexible load
According to the incremental cost of neighbours, either increment benefit updates the incremental cost of oneself or increment benefit to ground controller.Selection one
Whether individual " host groups " and " main load " decision-making increases or reduces global incremental cost and increment benefit.When generator always generates electricity work(
When rate is more than load aggregate demand power, the incremental cost of the overall situation will be reduced, vice versa.When load aggregate demand power is more than hair
During the total generated output of motor, the increment benefit of the overall situation will be increased, vice versa.
The algorithm includes procedure below:
Assuming that the cost of electricity-generating function of generating set and the electricity consumption benefit function of flexible load are quadratic function, generator
The cost of electricity-generating function of group is as follows：
Ci(PGi)=αi+βiPGi+γiP2 Gi, i ∈ SG
The electricity consumption benefit function of flexible load is as follows：
Bj(PDj)=aj+bjPDj+cjP2 Dj,j∈SD
Economic Dispatch Problem refers to that generator and flexible load under conditions of a series of operation constraints are met, make whole electricity
The optimization problem of the maximization of economic benefit of Force system operation, i.e.,：
PGi,min≤PGi≤PGi,max, i ∈ SG
PDj,min≤PDj≤PDj,max, j ∈ SD
Wherein, PDjRepresent flexible load j demand power, PGiRepresent generating set i power output.SGRepresent generator
Set, SDRepresent flexible load set.Solved using the method for Lagrange multipliers of classics, make λ represent corresponding with equality constraint
Lagrange multiplier, do not consider to constrain, above-mentioned RegionAlgorithm for Equality Constrained Optimization can be converted into：
To variable PGi,PDjLocal derviation is asked to obtain optimality condition with λ, i.e.,：
Above formula is the equation of comptability, can be obtained according to the equation of comptability：
That is the optimal solution of economic load dispatching is to make the incremental cost of generator equal with the increment benefit of flexible load, wherein m
Generator number is represented, k represents the number of flexible load.
Assuming that all flexible loads are run with generating set in its power constraints.In the consistency algorithm
In, the IC of generating set and the IB of flexible load are defined as follows：
IC and IB is selected as uniformity variable, using consistency algorithm, from generating set (Follower
Generator the more new formula of IC) is：
More new formula from the IB of load (Follower Load) is：
In order to meet the power-balance constraint in power system, represent flexible load actual demand power with generating electricity with Δ P
Difference between unit real output：
The IC of main generator group (Leader Generator) more new formula is：
The IB of main load (Leader Load) more new formula is：
Wherein ε is convergence coefficient, is a positive scalar.It restrains with the distributed optimization of main generator group and main load
Speed is relevant.
Step 7, the goal orientation degree of each target in the preference policy of described each load respectively, will be each
Load is moved in NETLOGO three-dimensional aspects on corresponding position respectively, and the target for updating each target of each load is inclined
Xiang Du；Then load-Respondence to the Price of Electric Power characteristic model corresponding to, obtains the power of now each load, and combines load
Agency obtains the general power that each load is acted on behalf of respectively for the administration of corresponding load.
Step 8, the general power that described each load is acted on behalf of is sent into MATLAB by NETLOGO, in MATLAB
The electricity price of generator output and corresponding each network node is obtained, and is back in NETLOGO, updates NETLOGO three-dimensional aspects
Electricity price on middle map network node.Its implementation process is：
The general power that described each load is acted on behalf of is passed through into the data exchange interface mould between MATLAB and NETLOGO
Block, sent by NETLOGO into MATLAB, optimal load flow is carried out for the general power of each load agency respectively in MATLAB
Calculate, obtain the electricity price of generator output and corresponding each network node, and by the electricity price of each network node, pass through
Data exchange interface module between MATLAB and NETLOGO is back in NETLOGO, and it is right in NETLOGO three-dimensional aspects to update
Answer the electricity price on network node.
The signal of MATLAB and NETLOGO interface transmission is P, f, V and C etc. in the present invention.By taking C as an example, MATLAB
The C obtained after electricity price derivation is stored into bus matrixes, and in the node system of three machine nine, electricity price information is previously stored
14th row of Bus matrixes, Bus matrixes are as shown in table 1.
Table 1
Connect table
The C of load bus in NETLOGO Calling MATLABs, first have to determine that the node electricity price, should in the position of bus matrixes
Agent at node obtains the electricity price of the node by interface command sentence.On the other hand, Agents is also required to load group is total
By command statement Calling MATLAB, the load during electricity price is calculated re-starts electricity price with existing value replacement and calculated power.
The description of its code is as shown in table 2.
Table 2
Step 9, using the electricity price in described NETLOGO three-dimensional aspects on each network node as traction signal, and point
Electricity price on map network node is not distributed to each load of its administration by described each load agency.Refer to：
Electric power system dispatching platform is often run once with a fixed time period, is calculated at each period end real-time
Electricity price, prediction in short-term electricity price, calculate mains frequency and node voltage, and to each load agency, big load issue the period electricity price,
Frequency, voltage, it is necessary to when issue history and forecasted electricity market price, frequency, voltage before and after the period simultaneously；The electricity price, frequency, electricity
Pressure is referred to as traction signal, instructs to draw each load adjustment itself power demand, is serviced while number one is maximized
In power network.
In NETLOGO Three Dimensional Interfaces, origin is located at southwest corner, and horizontal direction represents economic sexual orientation degree, vertical direction
Represent comfort level tendency degree, number range is all 0-1, each user in client layer in the left and right of the aspect, move up and down point
The change to economic sexual orientation degree and comfort level tendency degree is not represented, and while mobile, load also changes constantly, until most
The maximum point of a general objective is reached afterwards to stop.
Step 10, the position of each load when being completed according to the step 9 in NETLOGO three-dimensional aspects, and it is each
The goal orientation degree of each target of load, updates the initial policy of each load, and according to the method in the step 5, more
Set of strategies corresponding to new each load, it is corresponding with reference to each load then according to the catalogue scalar functions of corresponding each load
Electricity price, obtain each load respectively and correspond to each tactful general objective functional value in its set of strategies；
Described each load that obtains respectively corresponds to each tactful general objective functional value in its set of strategies, refers to：Respectively
For each load, judge whether general objective functional value corresponding to the initial policy of load is more than other tactful institutes in its set of strategies
Corresponding general objective functional value, if greater than the then load stop motion.
Step 11, judge whether general objective functional value is more than corresponding to the initial policy of load for each load respectively
General objective functional value in its set of strategies corresponding to other strategies, is the then load stop motion；Otherwise return to step 4.
In summary, the system power balance control of current multiple agent interaction power network has become a current research
Focus.The polytropy of multiple agent interaction power network and uncertainty make its control become particularly difficult, based on the more mesh of multiple agent
The method of mark uniformity can successfully manage.The mould of each force device composition in the power network MAS control method of design
Block has necessarily intelligent, copes with external disturbance, makes active responding, while pass through the communication of itself and peripheral modules
To realize self adjustment to reach a certain degree of autonomy, Real-Time Scheduling and distributed scheduling are realized, so as to improve power network fortune
Capable reliability and economy.Therefore, force device is participated in into system power balance and regards a multi-agent system as to study
It is feasible.
How intelligent the present invention employ a kind of power network to realize that the interactive Power Systems of multiple agent balance control
Volume modeling, emulation and control program.Program emulation platform is made up of NETLOGO and MATLAB, and wherein NETLOGO undertakes electric power
System intelligent element models and the work of power network MAS control, MATLAB are responsible for every computing of power system, passed through
Interface module between NETLOGO and MATLAB realizes whole system network data exchange.Force device intelligence in simulating scheme
Body transmits interactive information with MATLAB by interface, and force device intelligent body will have related parameter to be uploaded to MATLAB progress power trains
Unite every computing, while fresh signal is assigned to force device intelligent body in Calling MATLAB, each force device intelligent body is examined
Consider own target and make active response.
The present invention considers flexible load characteristic, and load, to pursue economy and comfort level as target, is ground under different electricity prices
Study carefully distributed consensus Optimized Operation strategy, optimize coordination computing, the effect for being optimal systematic function collaboration, checking
The validity of Optimal Operation Strategies.By establish intelligent grid multiple agent multiple-target system coordinate Controlling model, realize indirectly,
Distributed controll.The operation characteristic of accurate description electric network element and system in intelligent grid Complex Networks Theory system, with intelligence
Body form describes flexible load, obtains the interactive operation multiple agent environment of power network based on flexible load information exchange, based on leading
Draw the behavior of the coordination control strategy of the interactive operation of electric network element of control and the electric network element autonomous operation of Behavior-based control criterion
Specification, using suitable algorithm and Optimal Operation Strategies, on the basis of system reliability is ensured, make system that there is well excellent
Change operational effect, and verify the multi-objective coordinated control optimization operation result of multiple agent.
Claims (8)
1. a kind of intelligent grid multiple agent multiple target uniformity optimization method, it is characterised in that how intelligent according to intelligent grid
The characteristics of system of body Collaborative Control factor, analyze the different characteristic features of different force devices, and the mesh each proposed
Mark is required, appropriate object function is chosen when target is various and obtains optimization operation control method and parameter, it is ensured that system operation
When reliability and economy, and verify the validity of Optimal Operation Strategies；Implementation step includes：
Step 1, according to power system network structure, the union simulation platform based on MATLAB and NETLOGO is established, wherein,
Power system component model is established in MATLAB, the intelligent body general module of power system component is represented defined in NETLOGO,
Meanwhile build the data exchange interface module between MATLAB and NETLOGO and realize information exchange；
Step 2, for various load types, respectively according to the target of each target of load datum quantity, electricity price, and corresponding load
Tendency degree, establish the load-Respondence to the Price of Electric Power characteristic model for corresponding respectively to various loads and power supply type；Described load includes
Rigid load and flexible load, described power supply include distributed power source and energy-storage travelling wave tube；Wherein, rigid load refers to be not involved in
The interactive load of power network, flexible load refer to participate in the interactive load of power network；
Step 3, according to the load for the corresponding to various load types respectively-Respondence to the Price of Electric Power characteristic model established in the step 2, divide
The object function of each target of each load is not obtained；And each load is directed to respectively, by the mesh of each target of load
Scalar functions are weighted processing, obtain the catalogue scalar functions of corresponding each load respectively；
Step 4, described each load is randomly dispersed in NETLOGO three-dimensional aspects, obtains the initial policy of each load；
For the network node in NETLOGO three-dimensional aspects, electricity price is set at random, and establishes load agency；
Step 5, using the initial policy of each load as load datum quantity, respectively for each target of each load
Goal orientation degree, obtain strategy corresponding to each load respectively by the way of+i or-i, and combine the initial plan of each load
Slightly form the set of strategies of each load；Described i is each step iteration step length, and described step refers to that electricity price often changes once, is born
The tactful respective change of lotus is once；
Step 6, using the intelligent grid uniformity optimized algorithm of the multi-objective coordinated control of multiple agent, respectively to each load
Catalogue scalar functions optimize coordination computing, and selection obtains the plan that each load corresponds to its maximum general objective functional value respectively
Slightly, the preference policy as each load；
Make xiThe state of force device is represented, according to consistency protocol, the state value of and if only if network opens up all nodes of bowl spares
When all equal, unanimously, i.e., the node of the network has all reached：
x1=x2=...=xn
The intelligent grid uniformity optimized algorithm of the multi-objective coordinated control of described multiple agent, including procedure below：
Assuming that the cost of electricity-generating function of generating set and the electricity consumption benefit function of flexible load are quadratic function, generating set
Cost of electricity-generating function is as follows：
Ci(PGi)=αi+βiPGi+γiP2 Gi, i ∈ SG
The electricity consumption benefit function of flexible load is as follows：
Bi(PDi)=ai+biPDi+ciP2 Di,i∈SD
Economic Dispatch Problem refers to that generator and flexible load under conditions of a series of operation constraints are met, make whole power train
The optimization problem of the maximization of economic benefit of system operation, i.e.,：
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PDj,min≤PDj≤PDj,max, j ∈ SD
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Close, SDRepresent flexible load set；Solved using the method for Lagrange multipliers of classics, make λ represent draw corresponding with equality constraint
Ge Lang multipliers, do not consider to constrain, above-mentioned RegionAlgorithm for Equality Constrained Optimization can be converted into：
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That is the optimal solution of economic load dispatching is to make the incremental cost of generator equal with the increment benefit of flexible load, and wherein m is represented
Generator number, k represent the number of flexible load；
Assuming that all flexible loads are run with generating set in its power constraints；In the consistency algorithm, hair
The IC of group of motors and the IB of flexible load are defined as follows：
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IC and IB is selected as uniformity variable, using consistency algorithm, from generating set (Follower Generator)
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<msub>
<mi>S</mi>
<mi>G</mi>
</msub>
</mrow>
</munder>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
The IC of main generator group (Leader Generator) more new formula is：
<mrow>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mo>&lsqb;</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>&rsqb;</mo>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>f</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</msub>
<msub>
<mi>&lambda;</mi>
<mi>f</mi>
</msub>
<mo>&lsqb;</mo>
<mi>k</mi>
<mo>&rsqb;</mo>
<mo>+</mo>
<mi>&epsiv;</mi>
<mi>&Delta;</mi>
<mi>P</mi>
<mo>,</mo>
<mi>i</mi>
<mo>&Element;</mo>
<msub>
<mi>S</mi>
<mi>G</mi>
</msub>
</mrow>
2
The IB of main load (Leader Load) more new formula is：
<mrow>
<msub>
<mi>&lambda;</mi>
<mi>j</mi>
</msub>
<mo>&lsqb;</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>&rsqb;</mo>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>a</mi>
<mrow>
<mi>j</mi>
<mi>t</mi>
</mrow>
</msub>
<msub>
<mi>&lambda;</mi>
<mi>t</mi>
</msub>
<mo>&lsqb;</mo>
<mi>k</mi>
<mo>&rsqb;</mo>
<mo>+</mo>
<mi>&epsiv;</mi>
<mi>&Delta;</mi>
<mi>P</mi>
<mo>,</mo>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>S</mi>
<mi>D</mi>
</msub>
</mrow>
Wherein ε is convergence coefficient, is a positive scalar, it with main generator group and main load distributed optimization convergence rate
It is relevant；
Step 7, the goal orientation degree of each target in the preference policy of described each load respectively, by each load
Move to respectively in NETLOGO three-dimensional aspects on corresponding position, and update the goal orientation degree of each target of each load；
Then load-Respondence to the Price of Electric Power characteristic model corresponding to, the power of now each load is obtained, and pin is acted on behalf of with reference to load
Administration to corresponding load, the general power of each load agency is obtained respectively；
Step 8, the general power that described each load is acted on behalf of is sent into MATLAB by NETLOGO, obtained in MATLAB
The electricity price of generator output and corresponding each network node, and be back in NETLOGO, it is right in NETLOGO three-dimensional aspects to update
Answer the electricity price on network node；
Step 9, using the electricity price in described NETLOGO three-dimensional aspects on each network node as traction signal, and respectively by
Electricity price on map network node is distributed to each load of its administration by described each load agency；
Step 10, the position of each load when being completed according to the step 9 in NETLOGO three-dimensional aspects, and each load
Each target goal orientation degree, update the initial policy of each load, and according to the method in the step 5, update institute
Set of strategies corresponding to each load is stated, it is electric with reference to corresponding to each load then according to the catalogue scalar functions of corresponding each load
Valency, each load is obtained respectively and corresponds to each tactful general objective functional value in its set of strategies；
Step 11, judge whether general objective functional value corresponding to the initial policy of load is more than its plan for each load respectively
The general objective functional value corresponding to other strategies is slightly concentrated, is the then load stop motion；Otherwise return to step 4.
A kind of 2. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
In the step 1, union simulation platform of the described foundation based on MATLAB and NETLOGO, refer to：
A kind of intelligent grid Multi-Agent simulation platform being made up of MATLAB and NETLOGO, wherein the calculating using MATLAB
Function and programming technique, to establish the model of power system component and establish complicated electric power networks simulation model；And NETLOGO
It is a programmable modeling environment emulated to nature and social phenomenon, suitable for the complication system progress to Temporal Evolution
Modeling；Described NETLOGO completes building for power system component general module, and MATLAB carries out every meter of power system
Calculate, the network parameter for solving to obtain realizes information exchange by the interface routine between MATLAB and NETLOGO.
A kind of 3. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
Described to obtain the catalogue scalar functions for corresponding to each flexible load respectively in the step 3, its process is：
If economic benefit κ1=1 income as force device, it is defined as follows：
Wherein EkFor the summation of net input and output, ρkThe price of electricity, D are bought for loadkFor load reference power, κ1=1 is economic effect
Benefit, μkFor the tendency degree of economy,For the tendency degree of comfort level, υkThe price of electricity, G are sold for distributed power sourcekFor distributed electrical
Source reference power；
It is as follows to define force device comfort level：
Wherein CkFor force device comfort level；
The overall utility of force device is weighted to obtain general objective function representation by two object functions, and catalogue scalar functions define such as
Under：
Wherein RkFor the overall utility of force device.
A kind of 4. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
It is described that each load is randomly dispersed in NETLOGO three-dimensional aspects in the step 4, obtain the initial of each load
Strategy；For the network node in NETLOGO three-dimensional aspects, electricity price is set at random, and establishes load agency, and its process is：
For the network node in described NETLOGO three-dimensional aspects, electricity price is set at random, and according to NETLOGO three-dimension layers
Load bus in face, load agency is established, the quantity of described load agency is consistent with the quantity of load bus, and described is negative
Lotus is acted on behalf of to be corresponded with load bus, the corresponding each load of described each load agency's administration, and described each negative
Lotus agency is respectively used to the information transfer between each load and MATLAB of its administration.
A kind of 5. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
In the step 5, the initial policy using each load is as load datum quantity, respectively for each of each load
The goal orientation degree of target, obtain strategy corresponding to each load respectively by the way of+i or-i, and combine each load
Initial policy forms the set of strategies of each load：
Wherein, i=1, in NETLOGO three-dimensional aspects, eight points are included around each load, eight points are [μ respectivelyk+
1,φk], [μk-1,φk], [μk,φk+ 1], [μk,φk- 1], [μk+1,φk+ 1], [μk-1,φk- 1], Δ P, ε, i.e., each
The corresponding eight different strategies of load, respectively constitute the set of strategies of each load.
A kind of 6. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
The implementation process of the step 8 is：
By the general power that described each load is acted on behalf of by the data exchange interface module between MATLAB and NETLOGO, by
NETLOGO is sent into MATLAB, and optimal load flow calculating is carried out for the general power of each load agency respectively in MATLAB,
Obtain the electricity price of generator output and corresponding each network node, and by the electricity price of each network node, by MATLAB with
Data exchange interface module between NETLOGO is back in NETLOGO, updates map network section in NETLOGO three-dimensional aspects
Electricity price on point.
A kind of 7. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
In the step 9, the electricity price in the three-dimensional aspect using NETLOGO on each network node is divided as traction signal
Each load that the electricity price on map network node is distributed to its administration is not acted on behalf of by each load, is referred to：
Electric power system dispatching platform is often run once with a fixed time period, and electricity in real time is calculated at each period end
Valency, prediction electricity price, calculating mains frequency and node voltage, and period electricity price, the frequency is issued to each load agency, big load in short-term
Rate, voltage, it is necessary to when issue history and forecasted electricity market price, frequency, voltage before and after the period simultaneously；The electricity price, frequency, voltage
Traction signal is referred to as, instructs to draw each load adjustment itself power demand, is served while number one is maximized
Power network.
A kind of 8. intelligent grid multiple agent multiple target uniformity optimization method according to claim 1, it is characterised in that
In the step 10, described each load that obtains respectively corresponds to each tactful general objective functional value in its set of strategies, is
Refer to：
Each load is directed to respectively, judges whether general objective functional value corresponding to the initial policy of load is more than its in its set of strategies
General objective functional value corresponding to its strategy, if greater than the then load stop motion.
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