[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in the field without creative labor should belong to the scope of the present invention.
[0074] A new energy consumption method based on master-slave game under the background of power market, the process of which is as follows Figure 1 As shown, including:
[0075] 1, acquiring model input data, including the bid-winning quantity of a power generator, the upper and lower limits of the bid-winning quantity of the power generator, the upper and lower limits of the reported electricity price of the power generator, the expected output of the power grid, the upper and lower limits of the market clearing electricity price, the upper and lower limits of the expected output of the power grid and the 24-hour load demand of the next day;
[0076] 2, building a generator sub-model and a power grid sub-model;
[0077] The quotient submodel is as follows:
[0078] There are n power suppliers, and the power generation cost of each supplier is:
[0079] C i ＝0.5a i P i 2 (t)+b i P i (t)+c i
[0080] Among them, c i The generation cost of the ith generator; P i (t) the bid-winning quantity of the ith power generator in the T period; a i 、b i And c i Are the power generation cost coefficients respectively;
[0081] The total electricity sales revenue of the ith power generator in the T period is:
[0082] B i ＝P i (t)λ(t)
[0083] Among them, b i Is the total electricity sales revenue; λ(t) is the market clearing price;
[0084] The objective of the quotient submodel is to maximize its own income, and the objective function of the model is:
[0085]
[0086] P imin(t) ≤P i (t)≤P imax (t)
[0087] Where, n is the total number of quotation periods of each power generator on the second day; P imax (t) and p imin(t) Respectively representing the upper and lower limits of the scalar quantity of the generator I in the T period;
[0088] In addition, the constraint conditions for the electricity price reported by the generator I on the second day are:
[0089] μ imin (t)≤μ i (t)≤μ imax (t)
[0090] In which μ i (t) is the reported electricity price of generator I in the second day's T period; μ imax (t) and μ imin (t) respectively indicate the upper and lower limits of the electricity price reported by the generator in the T period;
[0091] The goal of the sub-model of power grid is to minimize the total cost of electricity purchase;
[0092] Total electricity purchase cost includes electricity purchase cost c b And the penalty cost of new energy consumption c p , the calculation method decibel is:
[0093]
[0094]
[0095] The objective function of the sub-model of power grid is:
[0096]
[0097] In which, p i (t) the bid-winning quantity of the ith power generator in the T period; λ(t) is the market clearing price; r p Penalty cost coefficient for new energy consumption; P iE (t) The expected output of the power grid in T period;
[0098] The constraint conditions of market clearing electricity price λ(t) are:
[0099] λ min (t)≤λ(t)≤λ max (t)
[0100] In which λ max (t) and λ min (t) respectively, the upper and lower limits of market clearing price in T period;
[0101] At the same time, after considering the security of the power grid, set constraints:
[0102] P imin(t) ≤P i (t)≤P imax (t)
[0103] P iEmin (t)≤P iE (t)≤P iEmax (t)
[0104]
[0105] In which, p imax (t) and p imin(t) Respectively representing the upper and lower limits of the scalar quantity of the generator I in the T period; P iEmax (t) and p iEmin (t) The upper and lower limits of the expected output of the power grid; P D (t) is the total power purchased by the power grid from various power producers in T period, that is, the load demand in this period; It is the constraint of supply and demand balance equation that the system needs to meet in T period;
[0106] 3, constructing a new energy consumption model based on the master-slave game according to the generator sub-model and the power grid sub-model, specifically:
[0107] Establish a one-master multi-slave game model, with the structure as follows Figure 2 As shown, the power grid submodels are set as game subjects, and multiple generator submodels are set as game slaves; The game slaves are generator 1, generator 2, ... and generator N, respectively, aiming at maximizing their respective profits; The core of the optimization of the game is to find the optimal bidding strategy of generator I for other generators. When generator I doesn't change the bidding strategy, all the other generators won't change the bidding strategy, and then Nash equilibrium will be reached. The main body of the game is the sub-model of the power grid, aiming at minimizing the total cost of electricity purchase and solving the problem of wind and light abandonment. The optimization core of the main body of the game is to allocate the winning bids of various power producers and determine the market clearing price under the condition of considering security constraints.
[0108] Step 4: Solve the new energy consumption model to obtain the solution set of the new energy consumption model, the process of which is as follows Figure 3 As shown in, specifically:
[0109] Step 4-1: according to the historical data, give the output quotation of the generator I;
[0110]Step 4-2: Solve the optimal quotation of the power supplier I for other power suppliers. The specific solution method is as follows:
[0111] The improved adaptive genetic algorithm is used to determine the individual fitness, which can improve the search efficiency and convergence, and generate high-quality individuals through crossover and mutation, in which each individual represents a bidding strategy of generator I, and the individual fitness refers to the total electricity sales gain of generator I; The condition of reaching Nash equilibrium is that the other generators will not actively change their bidding strategies without changing their bidding strategies, that is, find the Nash equilibrium solution;
[0112] The two key parameters in the improved adaptive algorithm are the crossover probability P. O And mutation probability p V , the specific calculation methods are:
[0113] Crossover probability P in improve adaptive genetic algorithm O The calculation method is:
[0114]
[0115] Mutation probability P in improve adaptive genetic algorithm V The calculation method is:
[0116]
[0117] Where f is the average fitness of the population; f max Is the maximum fitness of the population; k 1 And k 2 Is a constant in the interval [0,1]; P O Represents the richness of the population, p O The larger the population, the higher the richness of the population, and vice versa. P V Reflect the difficulty of obtaining the global optimal solution, p V The bigger it is, the easier it is to get the global optimal solution, otherwise it is more difficult to get it.
[0118] When arcsin(f/f max ) ≥π/6 indicates that the average fitness value of the population is closer to the maximum fitness value of the population, and the larger it is than π/6, indicating that the population difference is smaller. At this time, individuals crossing again will not bring more high-quality individuals, so it is necessary to adaptively reduce the crossing probability P. O At the same time, the mutation probability p is adaptively increased. V , in order to get the global optimal solution better, when arcsin(f/f max ) O At the same time, p is adaptively reduced. V , so that individuals can cross each other to obtain high-quality individuals.
[0119] Step 4-3: judge whether the Nash equilibrium solution is obtained, if yes, execute step 4-4, otherwise, return to step 4-2;
[0120] Step 4-4: according to the bidding strategy of each power generator, the sub-model of the power grid aims at minimizing the total cost of electricity purchase, and solves the market clearing price of the next day's electricity market and the winning bid of the power generator;
[0121] Step 4-5: Judge whether the Stackelberg-Nash equilibrium solution is obtained, if so, execute step 4-6, otherwise, return to step 4-4, specifically:
[0122] According to the bidding strategies of various power producers, the sub-model of power grid comprehensively considers the penalty cost of new energy consumption, and takes the minimum total cost of electricity purchase as the goal, and determines the market clearing price of the next day's electricity market and the bid-winning quantity of each power producer through particle swarm optimization algorithm, and finds the Stackelberg-Nash equilibrium solution. The condition of finding the Stackelberg-Nash equilibrium solution is that the total cost of electricity purchase of power grid is the lowest on the premise that each power producer reaches Nash equilibrium.
[0123] Step 4-6: output the solution set of the new energy consumption model, including the optimal quotation of the power generator I for other power generators, the market clearing price of the next day's electricity market and the bid winning amount of the power generator;
[0124] Step 5: Use the new energy consumption model solution set for new energy consumption.
[0125] In this embodiment, the electricity market of a certain day before is taken as an example for simulation. There are three power producers in the electricity market of the day before, and they use G. 1 、G 2 And g 3 It means that when the simulation time period is 7–8 hours of the peak power consumption on the second day, the load demand in this time period is P. D = ＝84MW, market clearing price. Power generation quotient g 1 、G 2 And g 3 The reported electricity price μ in this time period i ∈ [0,100] USD/(MW h), active power bid amount P. i ∈(0,40]MW, expected output of power grid P iE ∈(0,40]MW, penalty cost coefficient r of new energy consumption p Take 50 USD/(MW h). Power generation quotient g 1 Wind power accounted for 7.67%, solar power accounted for 6.51%, and other forms of power generation accounted for 85.82%; Power generation quotient g 2 Wind power accounts for 8.37%, solar power accounts for 6.24%, and other forms of power account for 85.39%; Power generation quotient g 3 Wind power accounted for 8.96%, solar power accounted for 7.03%, and other forms of power generation accounted for 84.01%.
[0126] The basic parameters of each generator are shown in Table 1.
[0127] Table 1 Basic parameters of power producers
[0128] Power generation quotient a/(USD/MW 2 )
[0129] Power generation quotient g 1 、G 2 And g 3 Reported electricity price and bid-winning quantity in two modes, as shown in the following details Figure 4 and Figure 5 Shown. according to Figure 4 and Figure 5 Multiply the winning bid of the bid by the generation proportion of the generator, and then combine the reported electricity price and the objective function of the generator sub-model to further obtain the generator G under the two modes of the generator not participating in the game and participating in the cooperative game. 1 、G 2 And g 3 Wind power generation and total profit.
[0130] According to the established one-master-multi-slave electricity market game model, combined with the electricity market example a few days ago, the improved adaptive genetic algorithm and particle swarm optimization algorithm are combined for simulation calculation. Then, according to the Stackelberg-Nash equilibrium solution obtained by simulation calculation, that is, e-commerce G is distributed in the master-slave game mode. 1 、G 2 And g 3 Report the electricity price and bid amount, and calculate the total profit and wind and solar power generation of each power generator under this mode; Finally, the above calculation results are compared and analyzed with two modes, that is, the generator does not participate in the game and participates in the cooperative game.
[0131] by Figure 6 It can be seen that after several rounds of games, the generator G 1 、G 2 And g 3 The final reported electricity price is stable at 68.5 USD/(MW h), 70.1 USD/(MW h) and 71.3 USD/(MW h) at this time. And through Figure 4 、 Figure 5 and Figure 7According to the comparison, the generator g 1 、G 2 And g 3 The final reported electricity price in the mode of participating in the master-slave game is lower than that in the modes of not participating in the game and participating in the cooperative game. However, considering the penalty cost C of new energy consumption p Compared with the two modes, the bidding amount of the generator participating in the master-slave game mode has been improved, that is, the actual output of the generator is closer to the expected output of the power grid. Based on this, the above three modes of e-commerce G are further calculated and compared. 1 、G 2 And g 3 The total profit and the corresponding wind and solar power generation are shown in Table 2.
[0132] Table 2 Comparison of three situations: not participating in the game, participating in the cooperative game and participating in the master-slave game
[0133]
[0134] From Table 2, it can be seen that under the mode of participating in the master-slave game, both the total profit and the actual wind and solar power consumption of the generator are improved compared with the other two modes. Therefore, the generator can win more benefit distribution by participating in the master-slave game in the electricity market, and effectively solve the problem of wind and light abandonment.
[0135] The invention provides a method based on the master-slave game theory to solve the contradiction between the goal of minimizing the power purchase cost of the power grid and the goal of maximizing the revenue of the power generator. At present, cooperative game is widely used in the market. Cooperative game means that all participants bid freely by means of alliance and cooperation. Cooperative game can increase the overall income of power producers. However, in reality, vicious competition may occur due to the lack of dominant players, resulting in excessively high or low market clearing price. Therefore, considering the current power market environment in China, aiming at seeking the contradictory equilibrium solution, the present invention proposes a new energy consumption model based on master-slave game in the background of power market to solve the contradiction between the goal of minimizing the power purchase cost of the power grid and the goal of maximizing the revenue of the power generator. Firstly, according to the game theory of one master and many slaves, the power grid is taken as the main body of the game, and new energy sources are introduced to absorb the penalty cost, with the aim of minimizing the total cost of electricity purchase and solving the problem of wind and light abandonment. Then, each generator is regarded as the slave of the game, aiming at the highest revenue from electricity sales, and a one-master multi-slave power market game model is constructed. Finally, the Stackelberg-Nash equilibrium solution of the model is solved by the improved adaptive genetic algorithm and particle swarm optimization algorithm. The simulation results show that the model provided by the invention can enable all parties in the game to obtain the optimal distribution of benefits, and can effectively solve the problem of abandoning wind and light.
[0136] The above are only the specific embodiments of the present invention, but the scope of protection of the present invention is not limited to this. Anyone familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.
