Improved dragonfly algorithm-based power system economic dispatch optimization method and system

Abstract

The application particularly relates to an improved dragonfly algorithm-based power system economic dispatch optimization method and system, which comprises the following steps: determining a target function and constraint conditions; optimizing a power system economic dispatch model by using the improved dragonfly algorithm; initializing related parameters in the dragonfly algorithm; performing iterative calculation on the fitness value of a dragonfly population individual; updating the weight parameters of five behaviors in the dragonfly population, and calculating the five behaviors in the dragonfly population and updating the search radius; judging whether there are other same kinds around the current dragonfly, and updating the step length vector and the position vector; judging whether the maximum iteration number is reached, if yes, stopping iteration and outputting an optimal solution; if not, continuing iteration operation. The application can process the power system economic dispatch problem under multiple targets and multiple constraint conditions, and realize the maximum economicization of the power system dispatch under wind power grid connection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of economic dispatch of power system, and particularly relates to an economic dispatch optimization method and system of power system based on improved dragonfly algorithm. BACKGROUND

[0002] The consumption of fossil energy and the environmental pollution problems caused by it have made clean energy represented by wind energy widely concerned. However, the integration of wind power and the demand for low-carbon power development have brought new challenges to the reliable operation and economic dispatch of power systems. It is of great significance to develop a reasonable power system dispatch strategy under wind power integration in maintaining the safe and stable operation of the power system, promoting the utilization rate of renewable energy, and improving the economy of power system operation.

[0003] When dealing with the dispatch problem of the power system, the calculation amount gradually increases with the increase of the number of iterations, and the efficiency is low. Intelligent optimization algorithm can effectively cope with the changing and complex optimization demand in the power system due to its global search ability and adaptability. However, the integration of wind power and the introduction of carbon trading increase the complexity of the power system dispatch plan, the number of decision variables increases, and the objective function and constraint conditions also become more complex, and some intelligent optimization algorithms have the problem of falling into local optimal solution and failing to find global optimal solution.

[0004] Therefore, it is necessary to provide a new economic dispatch optimization method and system of power system based on improved dragonfly algorithm. SUMMARY

[0005] Based on the above problems existing in the prior art, the purpose of the embodiments of the present application is to provide an economic dispatch optimization method and system of power system based on improved dragonfly algorithm, which can handle the economic dispatch problem of power system under multiple objectives and multiple constraint conditions, and further realize the maximum economic of power system dispatch under wind power integration considering stability, environmental protection and low carbon.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is: an economic dispatch optimization method of power system based on improved dragonfly algorithm, comprising:

[0007] S1, determining the objective function and constraint condition of the economic dispatch model of the power system;

[0008] S2, taking the dragonfly algorithm as the benchmark algorithm, and using the improved dragonfly algorithm to optimize the economic dispatch model of the power system;

[0009] S3, initializing the related parameters in the dragonfly algorithm, the related parameters including: population size, maximum number of iterations, individual dimension, upper and lower limits of individual motion position range, dragonfly population position vector and step size vector;

[0010] S4, the fitness value of the individual of the dragonfly population is calculated iteratively, and the positions of the food and the natural enemy in the population are updated, that is, the two individuals with the best and worst fitness are updated;

[0011] S5, the weight parameters of the five behaviors in the dragonfly population are updated, and the five behaviors in the dragonfly population are calculated, and the search radius is updated;

[0012] S6, it is judged whether there are other same species around the current dragonfly, if there are other same species around the current dragonfly, the step length vector formula and the position vector updating formula of the dragonfly population are obtained by changing the weight of the formation behavior and the inertia weight to update the step length vector and the position vector; if there are no other same species around the current dragonfly, the dynamic learning factor is introduced into the calculation formula of the Levy flight to update the position vector; then the updated position vector is checked based on the constraint condition;

[0013] S7, it is judged whether the maximum iteration number is reached, if yes, the iteration is stopped and the optimal solution is output; if not, the iteration operation is continued to step S4.

[0014] Further, the objective function of the economic dispatching model of the power system is the sum of the power generation cost of the thermal power unit, the power generation cost of the wind power unit and the carbon trading cost.

[0015] Further, the power generation cost calculation formula of the thermal power unit is:

[0016]

[0017] wherein, F G is the power generation cost of the thermal power unit, t is the dispatching time, T is a dispatching period, i is the group number of the thermal power unit, N is the total number of the thermal power units in the system, P i,t is the output of the i th thermal power unit at t, parameters a i , b i , c i are the coal consumption coefficients of the i th thermal power unit, e i and h i are the valve point effect coefficients of the i th thermal power unit, g i is the pollution gas emitted by the unit power generation of the thermal power unit, E i is the cost coefficient of the pollution gas treatment.

[0018] Further, the constraint conditions of the economic dispatching model of the power system include: system power balance constraint, thermal power unit output power constraint, wind power unit output power constraint, thermal power unit climbing rate constraint, reserve quantity constraint, thermal power unit start-stop constraint, thermal power unit carbon emission right quota constraint and purchase carbon emission right quota constraint.

[0019] Further, the system power balance constraint is:

[0020]

[0021] where t is a scheduling time, T is a scheduling period, i is a group number of the thermal power unit, N is a total number of the thermal power units in the system, P i,t is an output of the i th thermal power unit at t, M is a number of the grid-connected wind power units, j is a group number of the wind power unit, P j,t is an output of the j th wind power unit at t, P load,t is a total load of the system at t.

[0022] Further, the improved dragonfly algorithm-based optimization model for economic dispatch of the power system comprises:

[0023] Step S21, changing a group behavior weight a in the dragonfly algorithm;

[0024] Step S22, changing an inertia weight ω in the dragonfly algorithm;

[0025] Step S23, introducing a dynamic learning factor into the Levy flight.

[0026] Further, in S6, the new step length vector formula of the dragonfly population obtained by changing the group behavior weight and the inertia weight is:

[0027] ΔX k+1 = (sS i +a′A i +cC i +fF i +eE i )+ω′ΔX k

[0028] where ΔX k+1 is the changed step length vector of k+1, s is a weight coefficient of the separation behavior, S i is a separation behavior position vector of the i th dragonfly individual, a' is the changed group behavior weight, A i is a group behavior vector of the i th dragonfly individual, c is a weight coefficient of the aggregation behavior, C i is an aggregation behavior position vector of the i th dragonfly individual, f is a weight coefficient of the foraging behavior, F i is a foraging behavior position vector of the i th dragonfly individual, e is a weight coefficient of the enemy avoidance behavior, E i is an enemy avoidance behavior position vector of the i th dragonfly individual, ω' is the changed inertia weight, ΔX k is the step length vector of k, and k is a current iteration number.

[0029] Further, in S6, the new position vector update formula of the dragonfly population is obtained by changing the group behavior weight and inertia weight as follows:

[0030] X k+1 = X k + ΔX k+1

[0031] wherein X k+1 is the position of the individual in k+1 time, X k is the position of the individual in k time, and ΔX k+1 is the step vector in k+1 time.

[0032] Further, in S6, the calculation formula of introducing the dynamic learning factor into the Levy flight is as follows:

[0033]

[0034] wherein η is the adaptive learning factor, λ is the related control parameter, k max is the maximum iteration number, and k is the current iteration number.

[0035] The position update formula of the dragonfly individual at this time is as follows:

[0036] X k+1 = X k + ηLe'vy(d) × X k

[0037] wherein η is the adaptive learning factor, Le'vy(x) is the Levy flight, X k+1 is the position of the individual in k+1 time, and X k is the position of the individual in k time.

[0038] The power system economic dispatch optimization system based on the improved dragonfly algorithm is applied to the power system economic dispatch optimization method based on the improved dragonfly algorithm, and the system comprises:

[0039] A preprocessing module is configured to determine the objective function and the constraint condition of the power system economic dispatch model.

[0040] An improved modeling module is configured to take the dragonfly algorithm as a benchmark algorithm and optimize the power system economic dispatch model by using the improved dragonfly algorithm.

[0041] An initialization module is configured to initialize the related parameters in the dragonfly algorithm, and the related parameters include the population number, the maximum iteration number, the individual dimension, the upper and lower limits of the individual motion position range, the dragonfly population position vector, and the step vector.

[0042] An iterative calculation module is configured to perform iterative calculation of fitness values of individuals in a dragonfly population, and update positions of food and natural enemies in the population, i.e., two individuals with optimal and worst fitness values;

[0043] A behavior calculation module is configured to update weight parameters of five behaviors in the dragonfly population, and calculate the five behaviors in the dragonfly population, and update a search radius;

[0044] A position updating module is configured to determine whether there are other individuals of the same kind around the current dragonfly, and if there are other individuals of the same kind around the current dragonfly, update a step length vector and a position vector by changing a weight of a formation behavior and an inertia weight, obtaining a new step length vector formula and a position vector updating formula of the dragonfly population; if there are no other individuals of the same kind around the current dragonfly, update the position vector by introducing a dynamic learning factor into a calculation formula of a Levy flight; and then check the updated position vector based on a constraint condition;

[0045] A judgment output module is configured to determine whether a maximum iteration number is reached, and if the maximum iteration number is reached, stop iteration and output an optimal solution; if the maximum iteration number is not reached, return to continue iteration operation.

[0046] The beneficial effects of the present application are: the objective function and constraint condition of the economic dispatch model of the power system are determined; the improved dragonfly algorithm is used to optimize the economic dispatch model of the power system based on the benchmark algorithm of the dragonfly algorithm; the related parameters in the dragonfly algorithm are initialized, the related parameters including: the population quantity, the maximum iteration number, the individual dimension, the upper and lower limits of the individual motion position range, the dragonfly population position vector and the step length vector; iteration is carried out, the fitness value of the dragonfly population individual is calculated, the positions of the food and the natural enemy in the population are updated, that is, the two individuals with the optimal and worst fitness; the weight parameters of the five behaviors in the dragonfly population are updated, and the five behaviors in the dragonfly population are calculated, the search radius is updated; it is judged whether there are other same kinds around the current dragonfly, if there are other same kinds around the current dragonfly, the new step length vector formula and the position vector updating formula of the dragonfly population are obtained by changing the weight of the formation behavior and the inertia weight to update the step length vector and the position vector; if there are no other same kinds around the current dragonfly, the dynamic learning factor is introduced into the calculation formula of the Levy flight to update the position vector; then the updated position vector is checked based on the constraint condition; it is judged whether the maximum iteration number is reached, if yes, the iteration is stopped and the optimal solution is output; if not, the iteration operation is continued. The power system economic dispatch optimization method based on the improved dragonfly algorithm changes the weights of the fixed value formation behavior and the inertia weight into a decreasing function which gradually decreases with the iteration number, which speeds up the convergence speed of the algorithm, reduces the risk of the algorithm falling into local optimum, and is more conducive to finding the global optimal solution; when the step length is too large in the Levy flight, the local optimal solution is easily ignored, so the adaptive learning factor is introduced into the dragonfly individual position updating formula in the Levy flight, so that the dragonfly can adaptively determine the moving distance according to the advantages and disadvantages of its own position when updating the position, and the quality of global search of the algorithm is improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] The present application will be further described below in combination with the drawings and examples.

[0048] In the drawings:

[0049] Figure 1 The flow chart of the power system economic dispatch optimization method based on the improved dragonfly algorithm provided for the first embodiment of the present application is shown in the figure;

[0050] Figure 2 The convergence comparison chart of the original dragonfly algorithm and the improved dragonfly algorithm provided for the first embodiment of the present application is shown in the figure;

[0051] Figure 3 The output situation diagram of each unit after optimization using the improved dragonfly algorithm provided for the first embodiment of the present application is shown in the figure;

[0052] Figure 4A module schematic diagram of an improved dragonfly algorithm-based power system economic dispatch optimization system provided for the second embodiment of the present application is shown in the figure.

[0053] Figure 5 A structure schematic diagram of a network side server provided according to the third embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described below in connection with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0055] First Embodiment

[0056] The first embodiment of the present application provides an improved dragonfly algorithm-based power system economic dispatch optimization method, which comprises the following steps: determining a target function and constraint conditions of a power system economic dispatch model; taking a dragonfly algorithm as a benchmark algorithm, and optimizing the power system economic dispatch model by using an improved dragonfly algorithm; initializing related parameters in the dragonfly algorithm, wherein the related parameters comprise a population size, a maximum iteration number, an individual dimension, upper and lower limits of an individual motion position range, a dragonfly population position vector and a step length vector; performing iteration, calculating fitness values of dragonfly population individuals, updating positions of food and natural enemies in the population, i.e., two individuals with the best and worst fitness; updating weight parameters of five behaviors in the dragonfly population, and calculating the five behaviors in the dragonfly population, updating a search radius; judging whether there are other individuals of the same kind around the current dragonfly, if there are other individuals of the same kind around the current dragonfly, updating a step length vector and a position vector by changing a formation behavior weight and an inertia weight, to obtain a new step length vector formula and a position vector updating formula of the dragonfly population; if there are no other individuals of the same kind around the current dragonfly, updating the position vector by introducing a dynamic learning factor into a calculation formula in the Levy flight; subsequently, checking the updated position vector based on the constraint conditions; judging whether the maximum iteration number is reached, if yes, stopping iteration and outputting an optimal solution; if no, returning to continue iteration operation. The improved dragonfly algorithm-based power system economic dispatch optimization method of the present application changes the weights of the fixed value formation behavior and the inertia weight into a decreasing function which gradually decreases with the iteration number, thereby accelerating the convergence speed of the algorithm, reducing the risk of the algorithm falling into a local optimum, and being more conducive to finding a global optimal solution; considering that the step length is too large in the Levy flight and local optimal solutions are easily ignored, an adaptive learning factor is introduced into a dragonfly individual position updating formula in the Levy flight, so that the dragonfly can adaptively determine the moving distance according to the advantages and disadvantages of its own position when updating the position, thereby improving the global search quality of the algorithm.

[0057] The following is a detailed description of the implementation details of the power system economic dispatch optimization method based on the improved dragonfly algorithm in this embodiment. The following content is only for the convenience of understanding the implementation details, and is not necessary for the implementation of this solution. The specific process of this embodiment is as follows: Figure 1 As shown, this embodiment is applied to the power system economic dispatch optimization system based on the improved dragonfly algorithm.

[0058] Step S1, determining the objective function and constraints of the power system economic dispatch model.

[0059] Specifically, the objective function for determining the power system economic dispatch model is the sum of the power generation cost of the thermal power unit, the power generation cost of the wind power unit and the carbon trading cost.

[0060] Step S11, calculating the power generation cost of the thermal power unit in the objective function.

[0061] The power generation cost of thermal power units is generally the coal consumption cost of each unit during the dispatch cycle. The coal consumption cost calculation formula is:

[0062]

[0063] Among them, F G is the coal consumption cost, t is the scheduling time, T is a scheduling cycle, i is the group number of the thermal power unit, N is the total number of thermal power units in the system, P i,t is the output of the i-th thermal power unit at time t; f(P i,t ) is the coal consumption cost function of thermal power units, parameter a i 、b i 、c i is the coal consumption coefficient of the i-th thermal power unit.

[0064] In actual operation, the phenomenon of wire drawing when the intake valve of a steam turbine unit is suddenly opened during operation will superimpose a pulsating effect on the traditional fuel cost curve, namely the "valve point effect". This effect will significantly affect the fuel consumption rate and operating efficiency of the unit. The coal consumption cost function formula after considering the valve point effect is:

[0065]

[0066] Among them, f(P i,t ) is the coal consumption cost function after considering the valve point effect, P i,t is the output of the i-th thermal power unit at time t, parameter a i 、b i 、c i is the coal consumption coefficient of the i-th thermal power unit, e i and h iThe valve point effect coefficient of the i-th thermal power unit, The lower limit of the output power of the i-th thermal power unit.

[0067] The thermal power unit generates power by burning fossil fuels, and the emission of pollutants will pollute the environment. In addition to considering the cost of coal consumption and the valve point effect in actual operation, the cost of pollutant treatment should also be included in the cost of thermal power generation. In summary, the cost calculation formula of thermal power generation is:

[0068]

[0069] Where F G is the cost of thermal power generation, t is the dispatch time, T is a dispatch cycle, i is the unit number of the thermal power unit, N is the total number of thermal power units in the system, P i,t is the output of the i-th thermal power unit at time t, and parameters a i , b i , c i are the coal consumption coefficients of the i-th thermal power unit, e i and h i are the valve point effect coefficients of the i-th thermal power unit, g i is the amount of pollutant gas emitted per unit of thermal power generation, and E i is the cost coefficient of treating pollutant gas.

[0070] Step S12, calculate the wind turbine generation cost in the objective function.

[0071] The operation and maintenance cost of the wind farm is linearly related to the output power of the wind farm. The calculation formula of the operation and maintenance cost of the wind turbine is:

[0072]

[0073] Where F w is the operation and maintenance cost of the wind turbine, t is the dispatch time, T is a dispatch cycle, M is the number of grid-connected wind turbines, j is the unit number of the wind turbine, C w is the generation cost coefficient of the wind turbine, and P j,t is the output power of the j-th wind turbine at time t.

[0074] In practical applications, if the actual output power of the wind turbine is less than the predicted wind power, the grid reserve needs to be used to compensate for the insufficient wind power, thereby generating an overestimation cost of wind power. Conversely, if the actual output power of the wind turbine is greater than the predicted wind power, an additional underestimation cost will be caused. The overestimation cost calculation formula of wind power is as follows:

[0075]

[0076] Among them, F w(+) is the overestimation cost of wind power, t is the dispatching time, T is a dispatching cycle, M is the number of wind turbines connected to the grid, j is the group number of wind turbines, C w(+) is the overestimation cost coefficient of wind power, P' j,t , P j,t is the output power of the j-th wind turbine at the dispatching time t, P' j,t is the predicted value of the output power of the j-th wind turbine at the scheduling time t.

[0077] The formula for calculating the underestimated cost of wind power is as follows:

[0078]

[0079] Among them, F w(-) is the underestimated cost of wind power, t is the dispatching time, T is a dispatching cycle, M is the number of wind turbines connected to the grid, j is the group number of wind turbines, C w(-) is the underestimated cost coefficient of wind power, P j,t is the output power of the j-th wind turbine at the dispatching time t, P' j,t is the predicted value of the output power of the j-th wind turbine at the scheduling time t.

[0080] In summary, the power generation cost of a wind turbine should be calculated by adding up the three parts. The calculation formula for the power generation cost of a wind turbine is:

[0081] F W =F w +F w(+) +F w(-)

[0082] Among them, F W is the power generation cost of the wind turbine, F w is the operation and maintenance cost of wind turbines, F w(+) is the overestimated cost of wind power, F w(-) The underestimated cost of wind power.

[0083] Step S13: Calculate the carbon trading cost in the objective function.

[0084] The present invention adopts the baseline method as the initial allocation method of carbon quotas, and the carbon emissions calculation formula of thermal power units is:

[0085]

[0086] Among them, E Gp is the carbon emission of the thermal power unit, t is the scheduling time, T is a scheduling cycle, i is the group number of the thermal power unit, N is the total number of thermal power units in the system, δ iThe carbon emission intensity of the i th thermal power unit for unit electricity quantity is P j,t The output power of the j th wind power unit at the t th scheduling moment is ΔT is the scheduling time length.

[0087] The carbon emission right quota calculation formula of the thermal power unit is:

[0088]

[0089] Wherein, E Gq The carbon emission right quota of the thermal power unit is t is the scheduling moment, T is a scheduling period, i is the group number of the thermal power unit, N is the total number of the thermal power units in the system, η is the unit electricity quantity carbon emission standard, P j,t The output power of the j th wind power unit at the t th scheduling moment is ΔT is the scheduling time length.

[0090] The carbon trading cost calculation formula is:

[0091]

[0092] Wherein, F g The carbon trading cost is K C The carbon trading price is E Gp The carbon emission of the thermal power unit is E Gq The carbon emission right quota of the thermal power unit.

[0093] Comprehensively considering the above factors, the objective function of the power system economic dispatching model in the application is the sum of the thermal power unit power generation cost, the wind power unit power generation cost and the carbon trading cost, and the objective function is expressed as:

[0094] min F = F G +F W +F g

[0095] Wherein, F G The thermal power unit power generation cost is P W The wind power unit power generation cost is F g The carbon trading cost. The purpose of the power system economic dispatching model is to make the sum of the objective function minimum.

[0096] The constraint conditions of the power system economic dispatching model include: system power balance constraint, thermal power unit output power constraint, wind power unit output power constraint, thermal power unit climbing rate constraint, reserve quantity constraint, thermal power unit start-stop constraint, thermal power unit carbon emission right quota constraint and purchase carbon emission right quota constraint.

[0097] The system power balance constraint is:

[0098]

[0099] where t is the dispatch time, T is a dispatch cycle, i is the group number of thermal power units, N is the total number of thermal power units in the system, P i,t is the output of the ith thermal power unit at time t, M is the number of grid-connected wind power units, j is the group number of wind power units, P j,t is the output power of the jth wind power unit at the tth dispatch time, p load,t is the total load of the system at time t.

[0100] The output power constraint of the thermal power unit is:

[0101]

[0102] where P i,tmin is the minimum value of the power of the ith thermal power unit, P i,t is the output of the ith thermal power unit at time t, P i,tmax is the maximum value of the power of the ith thermal power unit, and N is the total number of thermal power units in the system.

[0103] The output power constraint of the wind power unit is:

[0104] 0≤P j,t ≤P j,tmax , (j = 1, 2, …, M)

[0105] where P j,t is the output power of the jth wind power unit at the tth dispatch time, p j,tmax is the maximum power of the jth wind power unit, and M is the number of grid-connected wind power units.

[0106] In actual operation of the power grid, the input and output of the thermal power unit need to be completed in a short time, so there is a high requirement for the ramp rate of the conventional thermal power unit. The ramp rate constraint of the thermal power unit is:

[0107] P i,t -P i,t-1 ≤r iu ×T 60

[0108] P i,t-1 -P i,t ≤r id ×T 60

[0109] where P i,t is the output of the ith thermal power unit at time t, P i,t-1 is the output power of the ith thermal power unit at time t-1, r iu is the maximum rising rate of the output power of the ith thermal power unit, T 60 is the dispatch time cycle, which is generally 1 h, and rid is the maximum decrease rate of the output power of the i-th thermal power unit.

[0110] The reserve capacity constraint is:

[0111]

[0112] wherein i is the group number of the thermal power unit, N is the total number of thermal power units in the system, d i is the reserve capacity coefficient of the thermal power unit, is the maximum output power of the i-th thermal power unit, is the minimum output power of the i-th thermal power unit, P i,t is the output power of the i-th thermal power unit at time t, P j,t is the output power of the j-th wind power unit at the scheduling time t, t,up is the positive reserve capacity, t,down is the negative reserve capacity.

[0113] The thermal power unit start-stop constraint is:

[0114]

[0115] wherein, is the cumulative start-up time of the i-th thermal power unit at the scheduling time t, is the minimum continuous start-up time, is the cumulative shut-down time of the i-th thermal power unit at the scheduling time t, is the minimum continuous shut-down time.

[0116] The thermal power unit carbon emission right quota constraint and the purchased carbon emission right quota constraint are:

[0117]

[0118] E Gp -E Gq ≤E qH

[0119] wherein E Gq is the carbon emission right quota of the thermal power unit, is the maximum carbon emission right quota, E Gp is the carbon emission amount of the thermal power unit, E qH is the maximum carbon emission right quota that can be purchased.

[0120] Step S2, taking the dragonfly algorithm as a benchmark algorithm, an improved dragonfly algorithm is used to optimize the economic dispatching model of the power system.

[0121] In particular, the existing dragonfly algorithm is a new type of swarm intelligence algorithm developed by simulating the behavior of dragonfly population in nature. The algorithm draws on five behaviors of dragonfly population: separation behavior, formation behavior, aggregation behavior, predation behavior and enemy avoidance behavior. The five behaviors of dragonfly population can represent the movement mode of dragonfly in the population, which can be used for position update of dragonfly individuals in the search and optimization process of the algorithm.

[0122] The separation behavior formula is:

[0123]

[0124] Wherein, S i is the separation behavior position vector of the i-th dragonfly individual, X is the current position of the dragonfly, X j is the position of the j-th dragonfly around the current dragonfly, and N is the number of surrounding dragonflies.

[0125] The formation behavior formula is:

[0126]

[0127] Wherein, A i is the formation behavior vector of the i-th dragonfly individual, V j is the flight speed of the j-th dragonfly individual around, and N is the number of surrounding dragonflies.

[0128] The aggregation behavior formula is:

[0129]

[0130] Wherein, C i is the aggregation behavior position vector of the i-th dragonfly individual, X j is the position of the j-th dragonfly around the current dragonfly, X is the position of the current individual, and N is the number of surrounding dragonflies.

[0131] The predation behavior formula is:

[0132] F i =X + -X

[0133] Wherein, F i is the predation behavior position vector of the i-th dragonfly individual, X is the position of the current individual, and X + is the position of the food; that is, the position of the individual with the best fitness value in the current population.

[0134] The enemy avoidance behavior formula is:

[0135]

[0136] Wherein, E iis the position vector of the i-th dragonfly individual's predator avoidance behavior, X is the current individual's position; X - is the position of the predator; that is, the position of the individual with the worst fitness value in the current population.

[0137] In order to update the position of the dragonfly individual in the search space in real time, a step vector ΔX and a position vector X are introduced for the position update of the dragonfly individual. The position vector represents the relative position of the dragonfly in the population, and the step vector represents the distance moved by the individual to the next iteration. The step vector formula is as follows:

[0138] ΔX k+1 = (sS i + aA i + cC i + fF i + eE i ) + ωΔX k

[0139] where ΔX k+1 is the step vector of k+1, s is the weight coefficient of the separation behavior, S i is the separation behavior position vector of the i-th dragonfly individual, a is the weight coefficient of the flocking behavior, A i is the flocking behavior vector of the i-th dragonfly individual, c is the weight coefficient of the aggregation behavior, C i is the aggregation behavior position vector of the i-th dragonfly individual, f is the weight coefficient of the foraging behavior, F i is the foraging behavior position vector of the i-th dragonfly individual, e is the weight coefficient of the predator avoidance behavior, E i is the predator avoidance behavior position vector of the i-th dragonfly individual, ω is the inertia weight, ΔX k is the step vector of k, and k is the current iteration number.

[0140] The update formula of the position vector is as follows:

[0141] X k+1 = X k + ΔX k+1

[0142] where X k+1 is the position of the individual of k+1, X k is the position of the individual of k, and ΔX k+1 is the step vector of k+1.

[0143] When there is no other individual of the same kind around the dragonfly individual, the above formulas are no longer applicable. The random walk of Levy flight mixes short distance search and long distance search, which can increase the diversity and randomness of the population and expand the search range. After adopting the Levy flight strategy, the position update formula of the dragonfly is as follows:

[0144] X k+1 =X k +Le′vy(d)×X k

[0145]

[0146]

[0147] wherein, k is the current iteration number, X k+1 is the position of the individual in k+1 times, X k is the position of the individual in k times, d is the dimension of the position vector, r1 and r2 are two random numbers in [0,1], β is a constant, β = 1.5 in the present application, and Le'vy(x) is a Levy flight.

[0148] Although the dragonfly algorithm has excellent global search ability, when solving some complex optimization problems, the dragonfly algorithm also has the shortcomings of slow convergence speed and low convergence precision. When the dragonfly individual updates the position, the flocking weight a and the inertia weight ω which determine the step vector are both fixed values, and the size of the weight will affect the strength of the global or local search ability of the dragonfly algorithm. At the same time, when the dragonfly algorithm adopts the Levy flight strategy, the individual moving step is random, and when the step is too large, it is not conducive to the local optimization of the algorithm. Therefore, it is necessary to improve the original dragonfly algorithm.

[0149] The improved dragonfly algorithm is used to optimize the economic dispatch model of the power system, which comprises the following steps:

[0150] In step S21, the flocking behavior weight a in the dragonfly algorithm is changed.

[0151] Specifically, compared with other optimization problems, the optimization dispatch of the power system has a larger search space and a more complex objective function, and the dragonfly algorithm will show a slow convergence speed in the initial stage, which will lead to the algorithm being easily trapped in a local optimal solution. However, when the convergence speed of the dragonfly algorithm is too fast with the increase of the iteration number, it is not conducive to the local optimization of the algorithm, therefore, in the present application, the flocking behavior weight a is changed to a weight value which gradually decreases with the iteration number, and the changed flocking behavior weight a' is:

[0152]

[0153] wherein, a' is the changed flocking behavior weight, a s is the introduced initial weight, a e is the introduced terminal weight, k max is the maximum iteration number, and k is the current iteration number.

[0154] In step S22, the inertia weight ω in the dragonfly algorithm is changed.

[0155] The inertia weight ω determines the moving step length of the individual in iteration, and its change is more sensitive to the search accuracy of the algorithm. When the weight is large, the algorithm has strong global search ability, but weak local search ability; when the inertia weight is small, the local search ability is improved, and the global search is relatively weak. In the present application, the fixed value inertia weight ω is changed into a nonlinear decreasing function that gradually decreases with the iteration number, so that the dragonfly algorithm can search for the global optimal value. The changed inertia weight ω' is:

[0156]

[0157] where ω' is the changed inertia weight, k max is the maximum iteration number, k is the current iteration number, ω max is the initial inertia weight value, ω min is the final inertia weight value.

[0158] According to the changed swarm behavior weight and inertia weight, the new step length vector formula of the dragonfly population is:

[0159] ΔX k+1 = (sS i + a' A i + c C i + f F i + e E i ) + ω' ΔX k

[0160] where ΔX k+1 is the changed step length vector of k+1, s is the weight coefficient of the separation behavior, S i is the separation behavior position vector of the i-th dragonfly individual, a' is the changed swarm behavior weight, A i is the swarm behavior vector of the i-th dragonfly individual, c is the weight coefficient of the aggregation behavior, C i is the aggregation behavior position vector of the i-th dragonfly individual, f is the weight coefficient of the foraging behavior, F i is the foraging behavior position vector of the i-th dragonfly individual, e is the weight coefficient of the enemy avoidance behavior, E i is the enemy avoidance behavior position vector of the i-th dragonfly individual, ω' is the changed inertia weight, ΔX k is the step length vector of k, and k is the current iteration number.

[0161] Step S23, introducing a dynamic learning factor into the Levy flight.

[0162] Levy flight is used for global optimization when there is no same kind around the individual of dragonfly. However, the moving step of Levy flight is randomly determined, which can cause the algorithm to ignore local optimal value when the step is large. In order to solve the problem, the adaptive learning factor η is introduced into the Levy flight, and the calculation formula is as follows:

[0163] η = 2 λ-1

[0164]

[0165] Wherein, η is the adaptive learning factor, λ is the relevant control parameter, k max is the maximum iteration number, and k is the current iteration number.

[0166] The position updating formula of the individual of dragonfly at this time is as follows:

[0167] X k+1 = X k + ηLe'vy(d) * X k

[0168] Wherein, η is the adaptive learning factor, Le'vy(x) is the Levy flight, X k+1 is the position of the individual at k+1 time, and X k is the position of the individual at k time.

[0169] Due to the addition of the learning factor, the moving step of the Levy flight can be adaptively changed with the iteration number, the step is large in the early stage, which is beneficial to the algorithm to quickly jump out of the local optimal solution, and the step is small in the later stage of the algorithm, which improves the global search quality.

[0170] Step S3, initialize the related parameters in the dragonfly algorithm, and the related parameters include: population size, maximum iteration number, individual dimension, upper and lower limits of individual motion position range, dragonfly population position vector and step vector.

[0171] Specifically, the output P i,t of the thermal power unit is taken as the decision variable, and the initial dragonfly position is set as:

[0172]

[0173] Wherein, N p is the population size, T is the scheduling period, the individual dimension of the dragonfly is N*T, and x i is a scheduling scheme in x.

[0174] The output of the thermal power unit at each time is randomly generated by the calculation formula, and the calculation formula is as follows:

[0175] P i,t = Pi,tmin + r x (P i,tmax - P i,tmin )

[0176] where P i,t is the output of the i-th thermal power unit at time t, P i,tmin is the minimum value of the power of the i-th thermal power unit, P i,tmax is the maximum value of the power of the i-th thermal power unit, and r is a random number in the interval [0, 1].

[0177] The upper and lower limits of the individual motion position range, i.e., the constraint condition in step S1.

[0178] Step S4, the fitness values of the individuals of the dragonfly population are calculated by iteration, and the positions of food and natural enemies in the population, i.e., the two individuals with the best and worst fitness, are updated.

[0179] Specifically, the fitness value is a key indicator for measuring the quality of an individual, which reflects the degree of fit of the solution represented by the individual to the target, such as cost minimization, benefit maximization, etc., in the economic dispatching problem of the power system.

[0180] Step S5, the weight parameters of the five behaviors in the dragonfly population are updated, and the five behaviors in the dragonfly population are calculated, and the search radius is updated.

[0181] Specifically, different behaviors affect the moving direction and method of the individuals of the population, etc., and the search radius is updated at the same time. The search radius determines how large a range of space an individual can explore, and appropriate adjustment of the radius helps to find a balance between global search and local search, avoiding the problem of falling into a local optimal solution too early or low search efficiency.

[0182] Step S6, it is judged whether there are other individuals of the same kind around the current dragonfly. If there are other individuals of the same kind around the current dragonfly, the new step length vector formula and the position vector update formula of the dragonfly population are obtained by changing the weight of the formation behavior and the inertia weight to update the step length vector and the position vector. If there are no other individuals of the same kind around the current dragonfly, the dynamic learning factor is introduced into the calculation formula of the Levy flight to update the position vector, and then the updated position vector is checked based on the constraint condition.

[0183] Further, by changing the weight of the formation behavior and the inertia weight, the new step length vector formula of the dragonfly population is obtained as follows:

[0184] ΔX k+1 = (sS i + a' A i + c C i + f F i + e E i ) + ω' ΔX k

[0185] where ΔX k+1 is the modified k+1th step length vector, s is the weight coefficient of the separation behavior, S i is the separation behavior position vector of the ith dragonfly individual, a' is the modified weight of the flocking behavior, A i is the flocking behavior vector of the ith dragonfly individual, c is the weight coefficient of the aggregation behavior, C i is the aggregation behavior position vector of the ith dragonfly individual, f is the weight coefficient of the foraging behavior, F i is the foraging behavior position vector of the ith dragonfly individual, e is the weight coefficient of the predator avoidance behavior, E i is the predator avoidance behavior position vector of the ith dragonfly individual, ω' is the modified weight of inertia, ΔX k is the kth step length vector, and k is the current iteration number.

[0186] Further, by modifying the weight of the flocking behavior and the weight of inertia, the new position vector update formula of the dragonfly population is obtained as follows:

[0187] X k+1 = X k + ΔX k+1

[0188] where X k+1 is the position of the individual at k+1th, X k is the position of the individual at kth, and ΔX k+1 is the k+1th step length vector.

[0189] Further, the dynamic learning factor is introduced into the calculation formula of the Levy flight as follows:

[0190] η = 2 λ-1

[0191]

[0192] where η is the adaptive learning factor, λ is the related control parameter, k max is the maximum iteration number, and k is the current iteration number.

[0193] At this time, the position update formula of the dragonfly individual is as follows:

[0194] X k+1 = X k + ηLe'vy(d) × X k

[0195] where η is the adaptive learning factor, Le'vy(x) is the Levy flight, X k+1 is the position of the individual at k+1th, and X kis the location of the individual k times.

[0196] Step S7, determine whether the maximum number of iterations has been reached. If so, stop the iteration and output the optimal solution; if not, return to step S4 to continue the iterative operation.

[0197] As an example, all experiments in this invention are conducted on a personal computer with a 12th Gen Intel(R) Core(TM) i9-12900KF processor, an NVIDIA GeForce RTX 3080Ti GPU, and 32.0GB of random access memory (RAM). The embodiments of the present invention use a standard IEEE-39 node standard test system to conduct simulation experiments in MATLAB 2022b. The test cycle is one day, divided into 24 hours. This system contains a total of 10 thermal power units and a wind farm, which is integrated into the system through 8 nodes.

[0198] In this embodiment, the initial parameters of the original dragonfly algorithm are set as follows: the population size is set to 50; the maximum number of iterations is 100; the value of s is 0.1; the value of a is 0.1; the value of c is 0.7; the value of f is 1; the value of e is 1; and the value of ω is 0.7. In the improved dragonfly algorithm, a is set to s Set to 0.9; a e Set the value of ω to 0.3; max The value of is set to 1.2; ω min The value of is 0.5; other parameters remain unchanged.

[0199] In order to verify the effectiveness and superiority of the improved dragonfly optimization algorithm and the original dragonfly optimization algorithm in optimizing the economic dispatch of power systems, this patent conducted simulation experiments based on the original dragonfly optimization algorithm and the improved dragonfly optimization algorithm. The convergence curves of the two algorithms are shown in Figure 2. Figure 2 shown.

[0200] The improved dragonfly algorithm gets the optimal operation cost of the system when the iteration is 30 times, while the original dragonfly algorithm gets the optimal operation cost of the system when the iteration is 50 times. This shows that the convergence speed of the algorithm can be improved by changing the weight of the swarming behavior. At the same time, the original dragonfly algorithm is more likely to fall into local optimum than the improved dragonfly algorithm in the optimization process, which shows that the way of changing the inertia weight and introducing a dynamic learning factor in Levy flight can help the algorithm to fully optimize globally while avoiding falling into local optimum. Table 1 below shows the system operation cost after optimizing the power system dispatch strategy using the improved and original dragonfly algorithms. In a dispatch cycle, the system operation cost optimized by the improved dragonfly algorithm is 1370342$, which is 6721$ lower than that optimized by the original dragonfly algorithm, which shows that the improved dragonfly algorithm can better improve the economy of the power system. As shown in Figure 8, the output of each unit optimized by the improved dragonfly algorithm, by reasonably allocating the output size of thermal power units and wind power units, can reduce carbon dioxide emissions as much as possible, and maximize the economy of the power system with wind power grid connection. Figure 3

[0201] Table 1 Comparison of optimization dispatch results of the improved and original algorithms

[0202]

[0203] ​The first embodiment of the present application provides a power system economic dispatch optimization method based on an improved dragonfly algorithm, comprising: determining a target function and a constraint condition of a power system economic dispatch model; taking the dragonfly algorithm as a benchmark algorithm, and optimizing the power system economic dispatch model by using the improved dragonfly algorithm; initializing related parameters in the dragonfly algorithm, the related parameters comprising: a population quantity, a maximum iteration number, an individual dimension, upper and lower limits of an individual motion position range, a dragonfly population position vector, and a step length vector; performing iteration, calculating an adaptability value of a dragonfly population individual, updating positions of food and natural enemies in the population, that is, two individuals with the best and worst adaptabilities; updating weight parameters of five behaviors in the dragonfly population, and calculating the five behaviors in the dragonfly population, updating a search radius; judging whether there are other same species around the current dragonfly, if there are other same species around the current dragonfly, obtaining a new step length vector formula and a position vector updating formula of the dragonfly population by changing a formation behavior weight and an inertia weight to update the step length vector and the position vector; if there are no other same species around the current dragonfly, introducing a dynamic learning factor into a calculation formula in the Levy flight to update the position vector; subsequently, checking the updated position vector based on the constraint condition; judging whether the maximum iteration number is reached, if yes, stopping iteration and outputting an optimal solution; if not, returning to continue iteration operation. The present application not only considers a power generation cost of a thermal power unit, but also includes a power generation cost of a wind power unit and a carbon transaction cost. In order to ensure safe and stable operation of the power system, constraint conditions are fully set, including a system power balance constraint, a thermal power unit output power constraint, a wind power unit output power constraint, a thermal power unit climbing constraint, a thermal power unit start-stop constraint, a reserve quantity constraint, a thermal power unit carbon emission right quota constraint, and a purchased carbon emission right quota constraint; based on power system stability and low carbon, a reasonable power system economic dispatch model under wind power grid connection is formulated, by changing a weight of a formation behavior, a slow convergence speed and a problem of easily falling into a local optimum of the dragonfly algorithm in processing a complex problem are overcome; a fixed value inertia weight is changed into a nonlinear decreasing function with iteration number, so as to facilitate global optimization of the dragonfly algorithm. A learning factor is introduced into the Levy flight, to solve a problem of too large step length and ignoring local optimization in the Levy flight; the improved dragonfly algorithm is used to solve the established dispatch model, and the method is verified to be feasible through an example; compared with the algorithm before improvement, the experimental results show that the improved dragonfly algorithm has better optimization performance, and improves the economy of a dispatch scheme to a certain extent.

[0204] The step division of the above various methods is only for the purpose of clear description, and can be combined into one step or split into multiple steps in implementation, as long as the same logical relationship is included, and all are within the protection scope of the patent; adding irrelevant modifications or introducing irrelevant designs in the algorithm or flow, but not changing the core design of the algorithm and flow, are within the protection scope of the patent.

[0205] Second embodiment:

[0206] As Figure 4 shown, the second embodiment of the present application provides an improved dragonfly algorithm-based power system economic dispatch optimization system, which comprises a preprocessing module 201, an improved modeling module 202, an initialization module 203, an iterative calculation module 204, a behavior calculation module 205, a position updating module 206, and a judgment output module 207.

[0207] Specifically, the preprocessing module 201 is configured to determine the objective function and constraint conditions of the power system economic dispatch model; the improved modeling module 202 is configured to optimize the power system economic dispatch model by using the improved dragonfly algorithm as the benchmark algorithm; the initialization module 203 is configured to initialize the related parameters in the dragonfly algorithm, including the population size, the maximum number of iterations, the individual dimension, the upper and lower limits of the individual motion position range, the dragonfly population position vector, and the step length vector; the iterative calculation module 204 is configured to calculate the fitness value of the dragonfly population individuals by iteration, and update the positions of food and natural enemies in the population, i.e., the two individuals with the best and worst fitness; the behavior calculation module 205 is configured to update the weight parameters of the five behaviors in the dragonfly population, and calculate the five behaviors in the dragonfly population to update the search radius; the position updating module 206 is configured to judge whether there are other individuals of the same kind around the current dragonfly, if there are other individuals of the same kind around the current dragonfly, update the step length vector and the position vector by changing the weight of the formation behavior and the inertia weight to obtain a new step length vector formula and a position vector updating formula; if there are no other individuals of the same kind around the current dragonfly, introduce a dynamic learning factor into the calculation formula of the Levy flight to update the position vector; then, the updated position vector is checked based on the constraint conditions; the judgment output module 207 is configured to judge whether the maximum number of iterations is reached, if yes, stop iteration and output the optimal solution; if not, return to continue the iterative operation.

[0208] It is not difficult to find that the present embodiment is a system embodiment corresponding to the first embodiment, and the present embodiment can be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in the present embodiment, and in order to reduce repetition, they will not be described here. Accordingly, the related technical details mentioned in the present embodiment can also be applied in the first embodiment.

[0209] It is worth mentioning that each module involved in the embodiment is a logical module. In actual application, one logical unit can be one physical unit, or a part of one physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present application, units not closely related to solving the technical problems proposed in the present application are not introduced in the embodiment, but this does not mean that there are no other units in the embodiment.

[0210] The third embodiment of the present application relates to a network side server, as shown in the accompanying drawings, comprising at least one processor 302; and a memory 301 connected with the at least one processor 302; wherein the memory 301 stores instructions executable by the at least one processor 302, and the instructions are executed by the at least one processor 302 to enable the at least one processor 302 to execute the above-mentioned data processing method. Figure 5

[0211] The memory 301 and the processor 302 are connected in a bus manner, the bus can include any number of interconnected buses and bridges, and the bus connects one or more processors 302 and various circuits of the memory 301 together. The bus can also connect various other circuits such as peripheral devices, voltage stabilizers, and power management circuits together, which are well known in the art, and therefore, they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, which provide a unit for communicating with various other devices on a transmission medium. The data processed by the processor 302 is transmitted on a wireless medium through an antenna, further, the antenna also receives data and transmits the data to the processor 302.

[0212] The processor 302 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory 301 can be used to store data used by the processor 302 in performing operations.

[0213] The fourth embodiment of the present application relates to a computer readable storage medium, which stores a computer program. The computer program is executed by the processor to implement the power system economic dispatch optimization method based on the improved dragonfly algorithm in the first embodiment.

[0214] ​That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment methods can be completed by programs instructing the relevant hardware, the programs are stored in a storage medium, and the programs include a plurality of instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0215] The above-mentioned is only the embodiment of the present application, and the common knowledge of specific structures and characteristics in the scheme is not described in detail here. Those skilled in the art know all the common technical knowledge in the field of the present application before the filing date or the priority date, can know all the prior art in the field, and have the ability to apply conventional experimental means before that date. Those skilled in the art can improve and implement the present scheme based on their own ability under the guidance of the present application, and some typical known structures or known methods should not be an obstacle for those skilled in the art to implement the present application. It should be pointed out that, for those skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should be considered as the protection scope of the present application, and these will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

[0216] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. An improved dragonfly algorithm-based power system economic dispatch optimization method, characterized in that, The method comprises the following steps: S1, determining the objective function and constraint condition of the economic dispatch model of the power system; S2, taking the dragonfly algorithm as a benchmark algorithm, and optimizing the economic dispatch model of the power system by using an improved dragonfly algorithm; S3, initializing the related parameters in the dragonfly algorithm, the related parameters comprising: population quantity, maximum iteration number, individual dimension, upper and lower limits of the range of individual motion position, dragonfly population position vector, and step length vector; S4, performing iterative calculation on the fitness value of the dragonfly population individual, and updating the positions of food and natural enemies in the population, that is, the two individuals with the best and worst fitness; S5, updating the weight parameters of the five behaviors in the dragonfly population, and calculating the five behaviors in the dragonfly population, and updating the search radius; S6, judging whether there are other individuals of the same kind around the current dragonfly, if there are other individuals of the same kind around the current dragonfly, obtaining a new step length vector formula and a position vector updating formula of the dragonfly population by changing the weight of the formation behavior and the inertia weight to update the step length vector and the position vector, if there are no other individuals of the same kind around the current dragonfly, introducing a dynamic learning factor into a calculation formula in the Levy flight to update the position vector, and then checking the updated position vector based on the constraint condition; S7, judging whether the maximum iteration number is reached, if yes, stopping iteration and outputting the optimal solution, if no, returning to step S4 to continue iteration operation; The new step length vector formula of the dragonfly population obtained by changing the weight of the formation behavior and the inertia weight is: in, is the changed k+1-time step vector, s is the weight coefficient of the separation behavior, for The i The separation behavior position vector of each dragonfly individual, is the changed teaming behavior weight, For the i The grouping behavior vector of individual dragonflies, c is the weight coefficient of the aggregation behavior, For the i The aggregation behavior position vector of individual dragonflies, f is the weight coefficient of foraging behavior, For the i The foraging behavior position vector of each dragonfly individual, e is the weight coefficient of the avoidance behavior, For the i The position vector of each dragonfly's avoidance behavior, is the changed inertia weight, is the step vector of k times, k is the current number of iterations; The new position vector updating formula of the dragonfly population obtained by changing the weight of the formation behavior and the inertia weight is: wherein, is the position of the individual of order k + 1, is the position of the individual of order k, is the step vector of order k + 1. The calculation formula in which the dynamic learning factor is introduced into the Levy flight is: wherein, is an adaptive learning factor, is a related control parameter, is a maximum number of iterations, k is a current iteration number; The position updating formula of the dragonfly individual at this time is: wherein, is an adaptive learning factor, is a Levy flight, is the position of the individual at iteration k+1, is the position of the individual at iteration k.

2. The power system economic dispatch optimization method based on improved dragonfly algorithm according to claim 1, characterized in that, The objective function of the economic dispatch model of the power system is the sum of the power generation cost of the thermal power unit, the power generation cost of the wind power unit, and the carbon trading cost.

3. The power system economic dispatch optimization method based on the improved dragonfly algorithm according to claim 2, characterized in that, The power generation cost calculation formula of the thermal power unit is: in, is the power generation cost of thermal power units, t To schedule the time, T is a scheduling cycle, i is the group number of the thermal power unit, N is the total number of thermal power units in the system, is the output of the i-th thermal power unit at time t, and the parameter 、 、 For the i Coal consumption coefficient of thermal power units, and For the i The valve point effect coefficient of the thermal power unit, It is the pollutant gas emitted per unit of power generation by thermal power units. is the cost coefficient for treating polluted gases.

4. The power system economic dispatch optimization method based on improved dragonfly algorithm according to claim 1, characterized in that, The constraint condition of the economic dispatch model of the power system comprises: system power balance constraint, thermal power unit output power constraint, wind power unit output power constraint, thermal power unit climbing rate constraint, reserve quantity constraint, thermal power unit start-stop constraint, thermal power unit carbon emission right quota constraint, and purchase of carbon emission right quota constraint.

5. The power system economic dispatch optimization method based on the improved dragonfly algorithm according to claim 4, characterized in that, The system power balance constraint is: in, t To schedule the time, T is a scheduling cycle, i is the group number of the thermal power unit, N is the total number of thermal power units in the system, is the output of the i-th thermal power unit at time t, M is the number of wind turbines connected to the grid, j is the group number of the wind turbine, For the j Typhoon turbines in t Output power at the scheduling time, yes t The total load of the system at the moment.

6. The power system economic dispatch optimization method based on improved dragonfly algorithm according to claim 1, characterized in that, The optimization of the economic dispatch model of the power system by using the improved dragonfly algorithm comprises: Step S21, changing the weight of the formation behavior in the dragonfly algorithm a； Step S22, modifying the inertia weight in the dragonfly algorithm ; Step S23, introducing the dynamic learning factor into the Levy flight.

7. An improved dragonfly algorithm-based power system economic dispatch optimization system, characterized by, The system applied to the economic dispatch optimization method of the power system based on the improved dragonfly algorithm in claim 1 comprises: A preprocessing module for determining the objective function and constraint condition of the economic dispatch model of the power system; An improved modeling module for optimizing the economic dispatch model of the power system by using the improved dragonfly algorithm with the dragonfly algorithm as a benchmark algorithm; An initialization module for initializing the related parameters in the dragonfly algorithm, the related parameters comprising: population quantity, maximum iteration number, individual dimension, upper and lower limits of the range of individual motion position, dragonfly population position vector, and step length vector; An iterative calculation module is configured to perform iterative calculation of fitness values of individuals in a dragonfly population, and update positions of food and natural enemies in the population, i.e., two individuals with optimal and worst fitness values; A behavior calculation module is configured to update weight parameters of five behaviors in the dragonfly population, and calculate the five behaviors in the dragonfly population, and update a search radius; A position updating module is configured to determine whether there are other same species around a current dragonfly, and if there are other same species around the current dragonfly, update a step length vector and a position vector by changing a weight of a formation behavior and an inertia weight, to obtain a new step length vector formula and a position vector updating formula of the dragonfly population; if there are no other same species around the current dragonfly, update the position vector by using a dynamic learning factor introduced into a calculation formula of a Levy flight; and then check the updated position vector based on a constraint condition; A judgment output module is configured to determine whether a maximum iteration number is reached, and if the maximum iteration number is reached, stop iteration and output an optimal solution; and if the maximum iteration number is not reached, return to continue iteration operation.

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