CN108565900A - A kind of distributed energy optimizing operation method based on game theory - Google Patents
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Classifications
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
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Abstract
A kind of distributed energy optimizing operation method based on game theory, includes the following steps：S1, the initial data and parameter for obtaining each generator；S2, several generators are chosen as game participant, establishes betting model；S3, equilibrium point initial value is chosen in the policy space of each decision variable；S4, according to equilibrium point initial value, the optimal policy of each game participant is searched on policy space using particle cluster algorithm；S5, by the optimal policy information sharing of each game participant to each generator；S6, judge whether system finds Nash Equilibrium point, if so, the equilibrium point is then exported, if nothing, return to step S3.The present invention solves the output scheduling problem of distributed energy under the premise of considering carbon emission minimum by way of non-cooperative game, the optimal output of electric system constituent parts under each pattern is solved, to obtain maximum economic well-being of workers and staff under the same conditions and minimize the cost paid.
Description
Technical field
The invention belongs to distributed generation technology fields, and in particular to a kind of distributed energy optimization fortune based on game theory
Row method.
Background technology
With the development of the times, environmental problem is attracted wide attention in world wide, and distributed energy electric system
It is a kind of completely new energy supply pattern, the energy consumed is all some renewable resources, these energy belong to cleaning
The energy influences very little caused by environment.Distributed power generation (Distributed Generators, DG), which is applied to power supply, is
System, can provide facility for customer power supply, certain facilitation can be played to the development of power grid industry.And with distribution
The grid-connected gradual application of electricity power, some problems gradually highlight：
(1) most of regenerative resources can be caused it to power train by such as weather, the limitation of the natural conditions such as disaster
The output of system is unstable, can bring intermittent and fluctuation sex chromosome mosaicism in application process.
(2) distributed energy has individual more, and capacity is small, the features such as excessively dispersion, the rule of unpredictable network load
Property, this can bring power supply and demand balance problem.
(3) distributed energy electric system must also consider that such as Generation Side power supply is contributed unstable, and distribution side operation is multiple
Hydridization, the operations of power networks problem such as user side decision-maker diversification.
Invention content
It is an object of the invention to：A kind of distributed energy optimizing operation method based on game theory is considering discharge most
The output scheduling problem for solving distributed energy under the premise of small by way of cooperative game, solves electric system under each pattern
The optimal output of constituent parts, to obtain maximum economic well-being of workers and staff under the same conditions and minimize the cost paid.
In order to reach object above, a kind of distributed energy optimizing operation method based on game theory is provided, including as follows
Step：
S1, the initial data and parameter for obtaining each generator；
S2, several generators are chosen as game participant, establishes betting model；
S3, equilibrium point initial value is chosen in the policy space of each decision variable；
S4, according to equilibrium point initial value, the optimal plan of each game participant is searched on policy space using cluster ion algorithm
Slightly；
S5, by the optimal policy information sharing of each game participant to each generator；
S6, judge whether system finds Nash Equilibrium point, if so, the equilibrium point is then exported, if nothing, return to step S3.
The present invention preferred embodiment be：The initial data and parameter of generator include electrical power generators income, sell carbon row
It delegates power income, cost of electricity-generating, disposal of pollutants cost and load data.
Preferably, in step S2, betting model is non-cooperative game model, the economic load dispatching weight coefficient of each generator
αiWith environment dispatch weight factor betaiFor the strategy in gambling process, the comprehensive benefit that each generator obtains is game income；Power
The generator revenue function that weight coefficient is established：Include the following steps：
S21, distributed motor economic goal function is established：
Wherein, p is the market price that generator sells kilowatt-hour, and γ is that unit carbon emission amount is bought/sold to generator
The market price, αiFor the economic load dispatching weight of generator i, βiFor the environment dispatch weight of generator i, PimaxMost for generator i
Big generated energy, αiPimaxIt is the actual power generation of generator i, βiPimaxIt is the carbon emission amount of generator i purchases/sale, ai,bi,
ciIt is the coefficient of the corresponding cost function of i-th generator respectively, sets ci＞ 0；
S22, distributed motor environmental goals function is established：
f2i=-CNi
Wherein, CNiBe the expense for the exceeded pollutant that generator i is used to administer discharge, h for governance unit pollutant at
This, di,ei,fiRespectively i-th generator carbon emission function coefficients set fi＞ 0；
S23, distributed motor constraints is established：
Power-balance constraint：The general power that as generator is sent out
Maintain an equal level with total load power demand；
Carbon transaction Constraints of Equilibrium：It as sells on carbon power trade market and exists with the carbon emission amount of purchase
Maintain an equal level in total amount.
Preferably, it is non-cooperative game in step S4, in the process, when to proceed to jth wheel excellent by each game participant
Change, can according to previous round game optimization achievement (αi_(j-1),βi_(j-1)), it is obtained according to distributed intelligence optimization algorithm optimal
Plan more combines (αi_j,βi_j), i.e.,：
Preferably, in step S6, when no any competitive bidding agency can obtain more Multi benefit i.e. by changing itself strategy
For Nash Equilibrium.
Preferably, in step S6, if all game participants continuously acquire identical optimal solution in iteration, have：It then indicates under this policy, each game participates in
Person must take into consideration the strategy of other generators and cannot independently change itself strategy, as have found Nash Equilibrium point.
The present invention has the beneficial effect that：
1) it is introduced into carbon transaction mechanism in electricity market, considers carbon emissions trading, is received in the economy of distributed generation unit
Carbon transaction income is added in beneficial function, may be either that carbon emission brings cost, also can is that carbon emission reduction brings income, be realized cleaning energy
It distributes rationally in source.
2) by establishing environment scheduling function, the environmental pollution cost of generator unit is introduced into the total of distributed generator
In income, and Exchanger Efficiency with Weight Coefficient Method is used, the influence of environmental factor is considered while considering economic goal.Again by establishing game
Model obtains the optimal output of mixed economy income and the distributed generator of environmental pollution cost.Obtaining the same of maximum return
When, the pollution generated in power generation process is preferably minimized, the requirement of environmental protection is more in line with.
3) each distributed generator is established as non-cooperative game model by the present invention, and it is equal to solve Nash by elementary particle group
Weigh point, and implementation method is simple, easy to utilize.
Description of the drawings
The present invention will be further described below with reference to the drawings.
Fig. 1 is the method flow schematic diagram of the present invention；
Fig. 2 is system connection figure of the embodiment of the present invention for the IEEE-30 nodes of emulation；
Fig. 3 is the environmental economy scheduling coefficient for six generators that game reaches the equilibrium points Nash in the embodiment of the present invention
Value figure；
Fig. 4 is the payoff diagram of six generators when game reaches equilibrium point in the embodiment of the present invention；
Fig. 5 is that the environmental economy of generator 3 in the embodiment of the present invention dispatches game three-dimensional track figure；
Fig. 6 is that the environmental economy of generator 3 in the embodiment of the present invention dispatches game planar obit simulation figure.
Specific implementation mode
Embodiment one
Referring to Fig. 1, a kind of distributed energy optimizing operation method based on game theory provided in this embodiment, including such as
Lower step：
S1, the initial data and parameter for obtaining each generator；
S2, several generators are chosen as game participant, establishes betting model；
S3, equilibrium point initial value is chosen in the policy space of each decision variable；
S4, according to equilibrium point initial value, the optimal plan of each game participant is searched on policy space using cluster ion algorithm
Slightly；
S5, by the optimal policy information sharing of each game participant to each generator；
S6, judge whether system finds Nash Equilibrium point, if so, the equilibrium point is then exported, if nothing, return to step S3.
In the present embodiment by taking IEEE-30 node standard power networks as an example, as shown in Fig. 2, 6 generators and 24 loads
It is distributed in this system, wherein 6 generators are located at busbar 1,2,5,8,11,13,24 loads are located at busbar
3、4、6、7、9、10、12、14、15、16、17、18、19、20、21、22、23、24、25、26、27、28、29、30。
In step S1, the coefficient (as shown in table 1) of electrical power generators cost function is inputted, generator discharges flow function is
Number (as shown in table 2), wherein ai,bi,ciIt is the coefficient of the corresponding cost function of i-th generator respectively, it is assumed that ci＞ 0；di,
ei,fiIt is i-th generator carbon emission function coefficients respectively, sets fi＞ 0.
Table 1
Table 2
Step S2：Optimize operating energy loss model as the distributed energy of theoretical foundation using game theory comprising two scheduling to refer to
Mark：The carbon containing object of system generates minimum low-carbon environment regulation index and each maximum economic load dispatching of electrical power generators total revenue refers to
Mark.During establishing model, the economic benefit of power production process has been considered not only herein, and considers electric power life
Environment is affected during production, thus establish be it is a kind of consider it is Environmental with economy regulation goal model.
(1) economic goal function
The economic goal function of this paper includes 3 parts：The fuel cost of sale of electricity income, carbon transaction income, generator.
Power selling income：The practical power selling income C of generator iSi, i ∈ N.
Carbon transaction is taken in：The income C that generator i is obtained by buying and selling carbon emission rightLi。
Fuel cost：The cost C of the energy such as the fossil that generator i is consumed in power generation process and other materialsMi。
According to the above analysis, the economic load dispatching function that synthesis can obtain generator i scheduling models herein is as follows：
f1i=CSi+CLi-CMi
Herein：
CSi=p αiPimax
CLi=γ βiPimax
As -1≤βiWhen≤0, generator i needs carbon emission power lacking in buying；As 0≤βiWhen≤1, generator i is sold
Extra carbon emission power.Since carbon transaction is a piecewise function, then the economic load dispatching function of generator i scheduling models can be by
Following piecewise function indicates：
P is the market price that generator sells kilowatt-hour, and γ is the market price that unit carbon emission amount was bought/sold to generator
Lattice, αiFor the economic load dispatching weight of generator i, βiFor the environment dispatch weight of generator i, PimaxFor the maximum generation of generator i
Amount, αiPimaxIt is the actual power generation of generator i, βiPimaxIt is the carbon emission amount of generator i purchases/sale, ai,bi,ciRespectively
It is the coefficient of the corresponding cost function of i-th generator, it is assumed that ci＞ 0.
(2) environmental goals function
In order to keep the pollutant discharge amount generated during electrical energy production minimum, pollutant emission minimum is established in model
Object function further reduces destruction of the system to natural environment.
Discharge the type that function model depends on discharge gas.It is generally believed that CO2 discharge function mathematical models are one two
Secondary function：
CNiIt is the expense for the exceeded pollutant that generator i is used to administer discharge, h is the cost of governance unit pollutant, di,
ei,fiIt is i-th generator carbon emission function coefficients respectively, sets fi＞ 0.
According to the above analysis, the environment scheduling function that can obtain generator i scheduling models herein is as follows：
f2i=-CNi
(3) foundation of constraints
While in view of environmental factor and economic factor, some necessary constraintss must be also satisfied, not so
Whole system will will produce fluctuation, or even jeopardize the safety of power grid.
Power-balance constraint：The general power that generator is sent out maintains an equal level with total load power demand.
Carbon transaction Constraints of Equilibrium：It is fair in total amount to be sold on carbon power trade market with the carbon emission amount of purchase,
Entire carbon power market goes out clear.
The constraints of combining environmental economic load dispatching function and generator, the optimization of distributed environment economic load dispatching can use number
It learns formula and indicates as follows：
max Wi=(f1i,f2i)
subject to:g(Pimax)=0
Objective optimization function W in above formulaiIt is a multiple objective function, not only makes the economic benefit f of each generator1iIt reaches
To optimal value, but also to make the discharge amount of pollution f of generator2iReach minimum.Constraints g is two etc. that generator meets
Formula constraints, power-balance and carbon transaction balance.
Step S3：The foundation of betting model.
Participant：Generator i ∈ N.
Strategy set：Si_α,β。
Revenue function：Wi(αi,βi；α-i,β-i)。
Since betting model is a quadratic function, game revenue function is the continuous quasiconcave function of corresponding strategy, then
There are the equilibrium points pure strategy Nash for the provable game.The equilibrium points Nash of economic load dispatching and environment scheduling are (αi *, βi *), then
By above-mentioned balanced theorem it is found that it should meet
That is (αi *, βi *) it is this optimal Generator strategy in the case where remaining generator selects optimal strategy, the i.e. strategic group
Every generator under closing can reach the highest benefit under equilibrium condition.
Step S4：The model of foundation is solved using particle swarm optimization algorithm.Specific solution flow is as follows：
S41：Setup parameter：p,γ,h,ai,bi,ci,di,ei,fi,PimaxAnd K=0；
S42：Every generator i randomly generates respectively initial economic snake and environment weight αi_0, βi_0；
S43：K=K+1；
S44：if-1≤βi＜ 0
Else if 0≤βi≤0
Wi=p αiPimax-(ai+bi(αiPimax)+ci(αiPimax)2)+γβiPimax-h(di+ei(αiPimax)+fi(αiPimax
)2)
End if
S45：Calculate Wi_k(Wi_kFor the sum of DG1 and DG2 incomes)；
S46：
1) in initialisation range, random initializtion, including random site and speed are carried out to population；
2) adaptive value of each particle is calculated according to object function (fitness) to be optimized.By the position of current each particle
It sets and is stored in the pbesti of each particle with adaptive value, the position of adaptive value optimum individual and adaptive value in all pbesti are deposited
It is stored in gbesti；
3) the history optimal location of more new particle individual, the history optimal location of update particle group；
4) the speed V of more new particle and position X, formula are as follows：
Xi+1=Xi+Vi+1
5) to each particle, the desired positions that its adaptive value is lived through with it are made comparisons, if it is more outstanding, then
Just using this adaptive value as present optimal location；
6) value of relatively more current whole pbesti and gbesti, updates gbesti；
7) if preset condition is met (operational precision or iterations that have often been previously set), terminate
This search, by resultOutput, otherwise jumps back to and 4) is once searched for again.
S47：Meet all mutual constraintss.
Step S5：It obtains taking into account distributed motor environment friendly and fortune using the carried model of particle swarm optimization algorithm
The scheduling result of row economy.Obtain each optimal Generator environment with the economic load dispatching factor as shown in figure 3, each hair is calculated
Respectively maximum return is as shown in Figure 4 for electric unit.
Simulation result also analyzes the trajectory diagram of 3 game iteration optimization of generator, as shown in Figure 5, distributed environment economy
Game scheduling trajectory diagram is a 3 dimensional drawing, and x-axis is economic snake factor alpha, and y-axis is environment weight factor beta, and z-axis is hair
The income of motor.Curved surface in graphics is theoretical value when generator 3 is not involved in game, and the yellow dots on curved surface are brighter, table
3 income of bright generator reaches the maximum value of theoretical value.Red curve in graphics is that iteration when generator 3 participates in game becomes
Change value.Environmental economy scheduling strategy (α after 3 each game of generatori, βi) according to Developing Tactics itself plan of other generators
Slightly, income also changes accordingly.It will be appreciated from fig. 6 that the environmental economy scheduling game general trend of generator 3 is generator
3 income can tend to theoretical maximum, i.e. trajectory diagram approaches the most bright spot of curved surface.Other generators can obtain similar conclusion.
In addition to the implementation, the present invention can also have other embodiment.It is all to use equivalent substitution or equivalent transformation shape
At technical solution, fall within the scope of protection required by the present invention.
Claims (6)
1. a kind of distributed energy optimizing operation method based on game theory, which is characterized in that include the following steps：
S1, the initial data and parameter for obtaining each generator；
S2, several generators are chosen as game participant, establishes betting model；
S3, equilibrium point initial value is chosen in the policy space of each decision variable；
S4, according to equilibrium point initial value, the optimal policy of each game participant is searched on policy space using cluster ion algorithm；
S5, by the optimal policy information sharing of each game participant to each generator；
S6, judge whether system finds Nash Equilibrium point, if so, the equilibrium point is then exported, if nothing, return to step S3.
2. a kind of distributed energy optimizing operation method based on game theory according to claim 1, which is characterized in that institute
The initial data of generator and parameter is stated to include electrical power generators income, sell carbon emission power income, cost of electricity-generating, disposal of pollutants
Cost and load data.
3. a kind of distributed energy optimizing operation method based on game theory according to claim 1, which is characterized in that institute
It states in step S2, the betting model is non-cooperative game model, the economic load dispatching weight coefficient α of each generatoriAnd environment
Dispatch weight factor betaiFor the strategy in gambling process, the comprehensive benefit that each generator obtains is game income；Weight coefficient
The generator revenue function of foundation：Include the following steps：
S21, distributed motor economic goal function is established：
Wherein, p is the market price that generator sells kilowatt-hour, and γ is the market that unit carbon emission amount was bought/sold to generator
Price, αiFor the economic load dispatching weight of generator i, βiFor the environment dispatch weight of generator i, PimaxFor the maximum hair of generator i
Electricity, αiPimaxIt is the actual power generation of generator i, βiPimaxIt is the carbon emission amount of generator i purchases/sale, ai,bi,ciPoint
It is not the coefficient of the corresponding cost function of i-th generator, sets ci＞ 0；
S22, distributed motor environmental goals function is established：
f2i=-CNi
Wherein, CNiIt is used to administer the expense of the exceeded pollutant of discharge for generator i, h is the cost of governance unit pollutant,
di,ei,fiRespectively i-th generator carbon emission function coefficients set fi＞ 0；
S23, distributed motor constraints is established：
Power-balance constraint：General power that as generator is sent out with it is total
Load power demand maintain an equal level；
Carbon transaction Constraints of Equilibrium：As the carbon emission amount with purchase is sold in total amount on carbon power trade market
It is upper to maintain an equal level.
4. a kind of distributed energy optimizing operation method based on game theory according to claim 1, which is characterized in that institute
It is non-cooperative game to state in step S4, and in the process, when each game participant proceeds to the optimization of jth wheel, meeting is according to before
Achievement (the α of one wheel game optimizationi_(j-1),βi_(j-1)), optimal plan is obtained according to distributed intelligence optimization algorithm and more combines (αi_j,
βi_j), i.e.,：
5. a kind of distributed energy optimizing operation method based on game theory according to claim 1, which is characterized in that institute
It states in step S6, is Nash Equilibrium when no any competitive bidding agency can obtain more Multi benefit by changing itself strategy.
6. a kind of distributed energy optimizing operation method based on game theory according to claim 1, which is characterized in that institute
It states in step S6, if all game participants continuously acquire identical optimal solution in iteration, has：It then indicates under this policy, each game ginseng
The strategy of other generators is must take into consideration with person and cannot independently change itself strategy, as has found Nash Equilibrium point.
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