CN108565900B - Distributed energy optimization operation method based on game theory - Google Patents
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
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Abstract
A distributed energy optimization operation method based on game theory comprises the following steps: s1, acquiring original data and parameters of each generator; s2, selecting a plurality of generators as game participants, and establishing a game model; s3, selecting an initial value of a balance point in a strategy space of each decision variable; s4, searching the optimal strategy of each game participant on the strategy space by using a particle swarm algorithm according to the initial value of the equilibrium point; s5, sharing the optimal strategy information of each game participant to each generator; s6, judging whether the system finds Nash equilibrium point, if yes, outputting the equilibrium point, if no, returning to step S3. The invention solves the output scheduling problem of the distributed energy through a non-cooperative game mode on the premise of considering the minimum carbon emission, and solves the optimal output of each unit of the power system under each mode, thereby obtaining the maximum economic benefit and the minimum paid cost under the same condition.
Description
Technical Field
The invention belongs to the technical field of distributed power generation, and particularly relates to a game theory-based distributed energy optimization operation method.
Background
With the development of the times, environmental problems have attracted wide attention worldwide, while a distributed energy power system is a brand new energy supply mode, the consumed energy is some renewable resources, and the energy belongs to clean energy and has little influence on the environment. Distributed Generation (DG) is applied to a power supply system, so that convenience can be provided for power supply of users, and certain promotion effect can be achieved on development of the power grid industry. With the gradual application of the distributed energy power grid connection, some problems are gradually highlighted:
(1) most renewable energy sources are limited by natural conditions such as weather, disasters and the like, so that the output of the renewable energy sources to a power system is unstable, and intermittent and fluctuating problems are caused in the application process.
(2) The distributed energy has the characteristics of more individuals, small capacity, over dispersion and the like, and the regularity of the load of the power grid cannot be predicted, so that the problem of power supply and demand balance can be caused.
(3) The distributed energy power system must also consider the grid operation problems such as unstable power output at the power generation side, complicated operation at the power distribution side, diversified decision bodies at the user side, and the like.
Disclosure of Invention
The invention aims to: the distributed energy optimization operation method based on the game theory solves the output scheduling problem of the distributed energy through a cooperative game mode on the premise of considering the minimum emission, and solves the optimal output of each unit of the power system under each mode, so that the maximum economic benefit and the minimum paid cost under the same condition are obtained.
In order to achieve the purpose, the distributed energy optimization operation method based on the game theory comprises the following steps:
s1, acquiring original data and parameters of each generator;
s2, selecting a plurality of generators as game participants, and establishing a game model;
s3, selecting an initial value of a balance point in a strategy space of each decision variable;
s4, searching the optimal strategy of each game participant in a strategy space by using an ion cluster algorithm according to the initial value of the equilibrium point;
s5, sharing the optimal strategy information of each game participant to each generator;
s6, judging whether the system finds Nash equilibrium point, if yes, outputting the equilibrium point, if no, returning to step S3.
The preferred scheme of the invention is as follows: raw data and parameters of the generator include generator generation revenue, carbon emission sales revenue, generation cost, pollution emission cost, and load data.
Preferably, in step S2, the game model is a non-cooperative game model, and the economic dispatch weight coefficient α of each generator isiAnd an environment scheduling weight coefficient betaiFor strategies in the game process, the comprehensive benefit obtained by each generator is game income; the weight coefficient establishes a generator revenue function:
the method comprises the following steps:
s21, establishing a distributed motor economic objective function:
wherein p is the market price of the generator for selling one-hour electricity, gamma is the market price of the generator for buying/selling unit carbon emission, and alpha isiFor economic dispatch of weight, beta, of generator iiScheduling weights, P, for the environments of Generator iimaxIs the maximum power generation of the generator i, alphaiPimaxIs the actual power generation of the generator i, betaiPimaxIs the amount of carbon emissions purchased/sold by the generator i, ai,bi,ciC is the coefficient of the corresponding cost function of the ith generator, respectivelyi＞0；
S22, establishing a distributed motor environment objective function:
f2i＝-CNi
wherein, CNiCost for generator i to treat discharged overproof pollutants, h cost for treating unit pollutants, di,ei,fiSetting f for the carbon emission function coefficient of the ith generatori＞0；
S23, establishing a distributed motor constraint condition:
and power balance constraint:
that is, the total power generated by the generator is equal to the total load power demand;
carbon trade balance constraints:
namely, the carbon emission amount sold and purchased in the carbon right trading market is equal to the total amount.
Preferably, step S4 is a non-cooperative game, in which each game participant will follow the outcome (α) of the previous game optimization when it goes to the jth optimization roundi_(j-1),βi_(j-1)) Obtaining the best according to a distributed intelligent optimization algorithmCombination of optimization strategies (. alpha.)i_j,βi_j) Namely:
preferably, in step S6, nash equilibrium is obtained when no bidding agent can obtain more benefits by changing its own policy.
Preferably, in step S6, if all game participants continuously obtain the same optimal solution during the iteration, there are:
it means that under this strategy each betting participant must consider the strategy of the other generators and cannot change its own strategy independently, i.e. find the nash balance point.
The invention has the beneficial effects that:
1) a carbon trading mechanism is introduced into the electric power market, carbon emission right trading is considered, carbon trading income is added into an economic profit function of the distributed power generation unit, cost can be brought to carbon emission, profits can be brought to carbon emission reduction, and optimal configuration of clean energy is achieved.
2) The environmental pollution cost of the power generation unit is introduced into the total profit of the distributed generator by establishing an environmental scheduling function, and the influence of environmental factors is considered while the economic objective is considered by adopting a weight coefficient method. And then, the optimal output of the distributed generator of the comprehensive economic benefit and the environmental pollution cost is obtained by establishing a game model. The pollution generated in the power generation process is reduced to the minimum while the maximum benefit is obtained, and the requirement of environmental protection is met better.
3) According to the invention, each distributed generator is established as a non-cooperative game model, and the Nash equilibrium point is solved through the basic particle swarm, so that the realization method is simple and is convenient to popularize and apply.
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The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a system connection diagram of an IEEE-30 node for emulation according to an embodiment of the present invention;
FIG. 3 is an environmental economic dispatch coefficient value diagram of six generators playing to reach Nash equilibrium point in the embodiment of the invention;
FIG. 4 is a graph of the revenue of six generators when the game reaches an equilibrium point in an embodiment of the present invention;
fig. 5 is a three-dimensional trajectory diagram of the eco-economical dispatch game of the generator 3 in the embodiment of the present invention;
fig. 6 is a plan track diagram of an eco-economical dispatch gaming of the power generator 3 in the embodiment of the present invention.
Detailed Description
Example one
Referring to fig. 1, the distributed energy optimization operation method based on the game theory provided in this embodiment includes the following steps:
s1, acquiring original data and parameters of each generator;
s2, selecting a plurality of generators as game participants, and establishing a game model;
s3, selecting an initial value of a balance point in a strategy space of each decision variable;
s4, searching the optimal strategy of each game participant in a strategy space by using an ion cluster algorithm according to the initial value of the equilibrium point;
s5, sharing the optimal strategy information of each game participant to each generator;
s6, judging whether the system finds Nash equilibrium point, if yes, outputting the equilibrium point, if no, returning to step S3.
In this embodiment, taking IEEE-30 node standard power network as an example, as shown in fig. 2, 6 generators and 24 loads are distributed in the system, where 6 generators are respectively located on buses 1, 2, 5, 8, 11, 13, and 24 loads are respectively located on buses 3, 4, 6, 7, 9, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30.
In step S1, the coefficients of the generator power generation cost function (shown in table 1) and the power generation are inputCoefficient of machine emission function (as shown in Table 2), where ai,bi,ciRespectively, of the corresponding cost function of the ith generator, let ci＞0；di,ei,fiRespectively setting the carbon emission function coefficient of the ith generatori＞0。
TABLE 1
TABLE 2
Step S2: the distributed energy optimization operation design model based on the game theory comprises two scheduling indexes: the system comprises a low-carbon environment scheduling index with minimum carbon production and an economic scheduling index with maximum total power generation yield of each generator. In the process of establishing the model, not only the economic benefit of the power production process is considered, but also the influence on the environment in the power production process is considered, so that the model comprehensively considering the environmental performance and the economic scheduling target is established.
(1) Economic objective function
The economic objective function herein includes 3 parts: electricity sales revenue, carbon transaction revenue, and fuel cost of the generator.
And (3) electricity selling income: actual electricity selling income C of generator iSi，i∈N。
Carbon trading revenue: revenue C obtained by the Generator i by buying and selling carbon emissions rightsLi。
Fuel cost: cost C of fossil and other energy and other materials consumed by generator i in power generation processMi。
According to the above analysis, the economic dispatching function of the dispatching model of the generator i in the text can be obtained by synthesis as follows:
f1i＝CSi+CLi-CMi
here:
CSi＝pαiPimax
CLi＝γβiPimax
when-1 is not more than betaiWhen the carbon emission right is less than or equal to 0, the generator i needs to purchase the missing carbon emission right; when 0 is not less than betaiWhen the carbon emission is less than or equal to 1, the generator i sells redundant carbon emission rights. Since carbon traffic is a piecewise function, the economic dispatch function of the generator i dispatch model can be represented by the piecewise function:
p is the market price of the generator for selling one degree of electricity, gamma is the market price of the generator for buying/selling unit carbon emission, alphaiFor economic dispatch of weight, beta, of generator iiScheduling weights, P, for the environments of Generator iimaxIs the maximum power generation of the generator i, alphaiPimaxIs the actual power generation of the generator i, betaiPimaxIs the amount of carbon emissions purchased/sold by the generator i, ai,bi,ciRespectively, of the corresponding cost function of the ith generator, let ci＞0。
(2) Environmental objective function
In order to minimize the pollutant emission generated in the electric energy production process, a pollutant emission minimum objective function is established in the model, and the damage of the system to the natural environment is further reduced.
The emission function model depends on the type of the exhaust gas. It is generally accepted that the mathematical model of the CO2 emission function is a quadratic function:
CNiis the cost of the generator i for treating the discharged overproof pollutants, h is the cost of treating the unit pollutants, di,ei,fiRespectively setting the carbon emission function coefficient of the ith generatori＞0。
From the above analysis, the environmental scheduling function of the generator i scheduling model herein can be derived as follows:
f2i＝-CNi
(3) establishment of constraints
While considering environmental and economic factors, some necessary constraints must be satisfied, otherwise the whole system will fluctuate and even endanger the safety of the power grid.
And power balance constraint: the total power generated by the generator is on the same level as the total load power demand.
Carbon trade balance constraints: the carbon emission amount sold and purchased in the carbon right trading market is equal in total amount, and the whole carbon right market is cleared.
In combination with the eco-economic dispatch function and the generator constraints, the distributed eco-economic dispatch optimization can be expressed mathematically as follows:
max Wi＝(f1i,f2i)
subject to:g(Pimax)＝0
objective optimization function W in the above equationiIs a multi-objective function, not only to make the economic benefit f of each generator1iReach the optimal value and also ensure the pollution discharge f of the generator2iTo a minimum. Constraint g is two equality constraints that the generator satisfies, power balance and carbon trade balance.
Step S3: and (5) establishing a game model.
The participants: the generator i belongs to N.
And (3) policy set: si_α,β。
The revenue function: wi(αi,βi；α-i,β-i)。
As the game model is a quadratic function and the game gain function is a continuous concave-like function of the corresponding strategy, the game can be proved to have a pure strategy Nash balance point. The Nash balance point of the economic dispatch and the environmental dispatch is (alpha)i *，βi *) Then, according to the above-mentioned equilibrium theorem, it should satisfy
I.e., (alpha)i *，βi *) All the strategies are the optimal strategies of the generator under the optimal strategy selected by the other generators, namely, each generator under the strategic combination can achieve the highest benefit under the balanced condition.
Step S4: and solving the established model by using a particle swarm optimization algorithm. The specific solving process is as follows:
s41: setting parameters: p, gamma, h, ai,bi,ci,di,ei,fi,PimaxAnd K is 0;
s42: each generator i randomly generates respective initial economic weight and environmental weight alphai_0，βi_0；
S43：K＝K+1；
S44：if-1≤βi＜0
Else if 0≤βi≤0
Wi＝pαiPimax-(ai+bi(αiPimax)+ci(αiPimax)2)+γβiPimax-h(di+ei(αiPimax)+fi(αiPimax)2)
End if
S45: calculating Wi_k(Wi_kThe sum of the benefits DG1 and DG 2);
S46：
1) in the initialization range, performing random initialization on the particle swarm, including random position and speed;
2) the adaptation value of each particle is calculated from the objective function (fitness) to be optimized. Storing the current position and the adaptive value of each particle in pbesti of each particle, and storing the position and the adaptive value of an individual with the optimal adaptive value in all pbesti in gbesti;
3) updating the historical optimal positions of the particle individuals and updating the historical optimal positions of the particle groups;
4) the velocity V and position X of the particle are updated as follows:
Xi+1＝Xi+Vi+1
5) for each particle, comparing its fitness value with the best location it has experienced, and if it is more excellent, then taking the fitness value as the current best location;
6) comparing the current values of all pbesti and gbesti, and updating the gbesti;
7) if the preset condition is satisfied (often the preset operation precision or iteration times), ending the search and obtaining the result
Outputting, otherwise, jumping back to 4) searching again.
S47: all mutual constraints are satisfied.
Step S5: and solving the extracted model by utilizing a particle swarm optimization algorithm to obtain a scheduling result which gives consideration to both the environment friendliness and the operation economy of the distributed motor. The optimal environment and economic dispatching factor of each generator are obtained as shown in fig. 3, and the maximum profit of each power generation unit is obtained through calculation as shown in fig. 4.
The simulation result also analyzes a trajectory diagram of the 3-game iterative optimization of the generator, and as can be seen from fig. 5, the trajectory diagram of the distributed environment economic game scheduling is a three-dimensional perspective diagram, the x axis is an economic weight coefficient alpha, the y axis is an environment weight coefficient beta, and the z axis is the income of the generator. The curved surface in the three-dimensional graph is a theoretical value when the generator 3 does not participate in the game, and the lighter the yellow point on the curved surface is, the maximum value of the theoretical value is reached by the income of the generator 3. The red curve in the three-dimensional graph is an iterative change value when the generator 3 participates in the game. Environmental economic dispatching strategy (alpha) of generator 3 after each gamei，βi) And adjusting the strategy of the generator according to the strategies of other generators, and correspondingly changing the income of the generator. As can be seen from fig. 6, the general trend of the eco-economic dispatch game of the generator 3 is that the profit of the generator 3 tends to the theoretical maximum value, that is, the trajectory graph approaches the brightest point of the curved surface. Other generators may conclude similarly.
In addition to the above embodiments, the present invention may have other embodiments. All technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (5)
1. A distributed energy optimization operation method based on game theory is characterized by comprising the following steps:
s1, acquiring original data and parameters of each generator;
s2, selecting a plurality of generators as game participants, and establishing a game model; the game model is a non-cooperative game model, and the economic dispatching weight coefficient alpha of each generatoriAnd an environment scheduling weight coefficient betaiFor strategies in the game process, the comprehensive benefit obtained by each generator is game income; the weight coefficient establishes a generator revenue function:
the method comprises the following steps:
s21, establishing a distributed motor economic objective function:
wherein p is the market price of the generator for selling one-hour electricity, gamma is the market price of the generator for buying/selling unit carbon emission, and alpha isiFor economic dispatch of weight, beta, of generator iiScheduling weights, P, for the environments of Generator iimaxIs the maximum power generation of the generator i, alphaiPimaxIs the actual power generation of the generator i, betaiPimaxIs the amount of carbon emissions purchased/sold by the generator i, ai,bi,ciC is the coefficient of the corresponding cost function of the ith generator, respectivelyi＞0；
S22, establishing a distributed motor environment objective function:
f2i＝-CNi
wherein, CNiCost for generator i to treat discharged overproof pollutants, h cost for treating unit pollutants, di,ei,fiSetting f for the carbon emission function coefficient of the ith generatori＞0；
S23, establishing a distributed motor constraint condition:
and power balance constraint:
that is, the total power generated by the generator is equal to the total load power demand;
carbon trade balance constraints:
namely, the carbon emission amount sold and purchased in the carbon right trading market is kept equal in the total amount;
s3, selecting an initial value of a balance point in a strategy space of each decision variable;
s4, searching the optimal strategy of each game participant on the strategy space by using a particle swarm algorithm according to the initial value of the equilibrium point;
s5, sharing the optimal strategy information of each game participant to each generator;
s6, judging whether the system finds Nash equilibrium point, if yes, outputting the equilibrium point, if no, returning to step S3.
2. The distributed energy optimization operation method based on the game theory as claimed in claim 1, wherein the raw data and parameters of the generator comprise generator generation income, carbon emission sale income, generation cost, pollution emission cost and load data.
3. A distributed energy optimization operation method based on game theory as claimed in claim 1, wherein in the step S4, the non-cooperative game is performed, and in the process, when each game participant performs the jth optimization, the outcome (α) of the previous game optimization is obtainedi_(j-1),βi_(j-1)) Obtaining the optimized strategy combination (alpha) in the j round according to the distributed intelligent optimization algorithmi_j,βi_j) Namely:
4. the distributed energy optimization operation method based on game theory as claimed in claim 1, wherein in step S6, when no bidding agent obtains more benefit by changing its own policy, it is nash equilibrium.
5. A game theory-based distributed energy optimization operation method according to claim 1, wherein in step S6, if all game participants continuously obtain the same optimal solution during iteration, there are:
(αi_j,βi_j) For the j-th round of optimization, (α)i_(j-1),βi_(j-1)) For the optimization results of the j-1 th round, (alpha)i_(j+1),βi_(j+1)) Optimizing results for the j +1 th round; then
It means that under this strategy, each betting participant must consider the strategy of other generators and cannot change its own strategy independently, i.e., find nash equilibrium points.
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