New energy electric power economic emission scheduling method optimized by variation black-wing plinary algorithm

Abstract

The invention relates to a new energy electric power economic emission scheduling method containing wind power and photoelectricity based on variation black-wing plinary algorithm optimization, which comprises the following steps: S1, converting fuel cost and pollution emission into a single objective function by adopting a weight sum method, and establishing a new energy electric power economic emission scheduling model in combination with constraint conditions; s2, randomly initializing an active power output scheduling matrix of the generator set; s3, randomly initializing another scheduling scheme, and sequentially taking out the output of a certain generator to replace the active output of the corresponding generator set in the scheduling matrix to form a new scheduling matrix; s4, according to the Newton iteration method, the active power output of the last group of generators is solved through a power flow equation; s5, calculating the penalty function value of the last group of generator active output exceeding the constraint; s6, calculating the sum of the fuel cost and the pollutant emission of the generator set in the scheduling scheme, and adding a penalty function value for common normalization; s7, updating the active power output of the generator set based on a variation black-wing-based algorithm; s8, if the maximum number of iterations is reached, outputting the optimal generator set output; otherwise, turning to S3.

Description

Technical Field

[0001] The present invention relates to the field of economic emission dispatch of power systems, and particularly to a comprehensive power economic emission dispatch method containing new energy power generation optimized by a mutated black-winged kite algorithm. Background Art

[0002] The energy industry is facing an urgent need to address environmental challenges and improve system efficiency. Solving environmental problems and developing efficient systems are major challenges faced by researchers. The problem of global warming is becoming increasingly serious, and greenhouse gas emissions have become a global challenge. China announced the "dual carbon" goal at the United Nations General Assembly in 2020: to reach the carbon emission peak before 2030 and achieve carbon neutrality before 2060. According to data in 2023, the pollution emissions in the global energy sector have reached a record high, and there is an urgent need to reduce carbon emissions. Among them, fossil fuels are the main sources of pollution and the root cause of climate change, accounting for more than 80% of global emissions. As a major energy consumer, the power industry has become a key area for emission reduction. Therefore, how to rationally allocate fuel resources in power production, reduce emissions, and improve energy utilization efficiency has become a key issue in power dispatch.

[0003] Wind energy and solar energy, as clean and renewable energy sources, have wide distribution and huge development potential, and have almost no economic cost and environmental burden, making them ideal energy choices. Compared with traditional thermal power generation, new energy power generation can not only reduce fuel costs but also reduce environmental pollution. However, the randomness and intermittency of wind energy and solar energy increase the challenges of grid stability and dispatch. Especially when the proportion of new energy gradually increases, it may affect the safety of the power system and lead to inefficient operation of thermal power generation units. To improve the utilization efficiency of new energy and simplify dispatch, enhancing prediction accuracy has become a research focus in the power industry.

[0004] To address these challenges and optimize the utilization of new energy, the research on the dynamic economic emission dispatch problem (DEED) is crucial for promoting the transformation of the energy structure from dependence on fossil fuels to a renewable energy-based core.

[0005] The DEED problem is to guide the output of generators according to the predicted load demand within a dispatch period (24 hours), aiming to minimize the generation cost and pollutant emissions, and achieve a win-win situation in economic and environmental benefits while improving the energy utilization efficiency of the power system. The DEED problem is essentially a multi-objective optimization problem, which involves finding a balance between cost-benefit and environmental impact, and these two objectives often conflict with each other. An effective solution to the DEED problem can not only reduce the generation cost but also reduce environmental pollution, thus bringing significant social and environmental benefits.

[0006] Due to the fact that the DEED problem involves multiple variables and complex constraint conditions, traditional optimization methods often struggle to find the optimal solution. Therefore, many researchers have adopted swarm intelligence optimization methods (such as particle swarm optimization, differential evolution algorithm, genetic algorithm, etc.) to solve such problems. They can provide feasible solutions within a reasonable time and achieve good results even when faced with complex and non-linear objective functions. Although swarm intelligence optimization methods can, to a certain extent, solve the power economic emission dispatch problem, they usually face challenges such as large computational volume, being prone to falling into local optima, and slow convergence speed.

[0007] Therefore, in view of the deficiencies of existing algorithms, a mutated black-winged kite algorithm that comprehensively considers global and local optimal search strategies is developed to solve the new energy power economic emission dispatch problem, so as to improve the efficiency and accuracy of problem-solving and promote the development of the power industry towards a low-carbon and economic direction. Summary of the Invention

[0008] The object of the present invention is to provide a new energy power dispatch problem containing wind and solar power generation that can simultaneously optimize fuel cost and pollution emissions, and a power economic emission dispatch method that comprehensively considers local convergence and global convergence.

[0009] To achieve the above invention object, the present invention provides a new energy power economic emission dispatch method optimized by a mutated black-winged kite algorithm, including the following steps:

[0010] S1. Using the weighted summation method to transform the two optimization objective functions of fuel cost E and pollution emission C into a single-objective optimization function, and combining the power flow equation constraints of the power system, the active power output constraints of the generator sets, and the engine ramp rate constraints to jointly establish a new energy power system economic emission dispatch model;

[0011] S2. Suppose there are N groups of active power outputs of thermal power generator sets that need to be dispatched, randomly initialize M different dispatch schemes, and calculate N - 1 groups of active power outputs P i , i = 1, 2,..., N - 1, which jointly constitute the active power output scheme matrix A of the generator sets;

[0012]

[0013] where P imin and P imax are respectively the upper and lower limits of the active power output of the i-th group of generator sets, and rand is a random number in [0, 1];

[0014] S3. Randomly initialize another 1 dispatch scheme, with N - 1 groups of active power outputs being P′, and sequentially take out a certain group of generator active power output P′ from this dispatch scheme i to replace the corresponding generator set active power output P in each dispatch scheme in A i, a new scheduling scheme matrix A' of M*(N-1) is formed, as shown in formula (2);

[0015]

[0016] S4. According to Newton's iterative method, the active power output P of the Nth group of generators is obtained from the power flow equation constraints N ;

[0017] S5. Calculate the penalty function value when the active power output P of the Nth group of generators N exceeds the constraint range of the generator set;

[0018] S6. Calculate the sum of the fuel costs and pollution emissions of the N groups of generator sets in the scheduling schemes A and A', and jointly constitute the fitness function value y i and y' i , and then normalize it to [0,1];

[0019] S7. Execute the mutated black-winged kite algorithm to optimize and update the active power output P of the generator set i ;

[0020] S8. If the maximum number of iterations is reached, output the optimal active power output P of the generator set gbest ; Otherwise, go to S3.

[0021] Among them, in the step S7, the specific implementation of executing the mutated black-winged kite algorithm to optimize the active power output of the generator is as follows:

[0022] Step S7.1: Calculate the latest position of the black-winged kite, and the specific formulas are as shown in (3) and (4);

[0023] Attack behavior:

[0024]

[0025] Migration behavior:

[0026]

[0027] Step S7.2: Calculate the approximate optimal scheduling scheme P best , and the specific formula is as shown in (5);

[0028]

[0029] Step S7.3: Use the approximate global optimal point P found by the attack behavior and migration behavior of the black-winged kite algorithm best as the initial optimal position of the cross strategy. Update the position of P i , and the specific formulas are as shown in (6) and (7);

[0030] Vertical cross:

[0031]

[0032] Horizontal intersection:

[0033]

[0034] Wherein, r, r1, and r2 are random numbers between [0, 1].

[0035] Step S7.4: Calculate the active power output P of the generator set new , and the specific formula is as shown in (8);

[0036] P new = P i -rd (8)

[0037] Wherein, r is a decreasing factor that decreases from 1 to 0; d is the spacing.

[0038] Step S7.5: Calculate the active power output P of the generator set gbset , and the specific formula is as shown in (9);

[0039]

[0040] Wherein, rand is a random number between [0, 1].

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] The present invention uses the mutated black-winged kite algorithm to optimize the new energy power economic dispatch containing wind and solar power generation. It can not only optimize the iterative update equation to avoid falling into local optimal solutions, but also solve a more economical and less polluting power economic dispatch scheme. Brief description of the drawings

[0043] Figure 1 . Schematic diagram of the method framework process; Specific embodiments

[0044] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further details the present invention in conjunction with the accompanying drawings and specific implementation cases. It should be understood that the specific implementation cases described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] As Figure 1 shown, in an embodiment of the present invention, a new energy power economic emission dispatch method optimized based on the mutated black-winged kite algorithm is proposed, including the steps:

[0046] S1. Use the weighted summation method to transform the two optimization objective functions of fuel cost \(E\) and pollution emission \(C\) into a single-objective optimization function, and combine the power flow equality constraints of the power system, the active power output constraints of the generator sets, and the engine ramp rate constraints to jointly establish the economic emission dispatch model of the new energy power system. The specific form is:

[0047] The specific form of the fuel cost is:

[0048]

[0049] where \(N\) is the number of generator sets in the system; \(P i is the active power output of the \(i\)-th generator set; \(a i , \(b i and \(c i are the fuel cost coefficients of the \(i\)-th generator set; \(e i and \(f i are the valve point effect coefficients.

[0050] The specific form of the pollution emission is:

[0051]

[0052] where \(\alpha i , \(\beta i , \(\gamma i , \(\eta i and \(\delta i represent the pollution emission coefficients.

[0053] Using the weighted sum method to transform the fuel cost and pollution emission into a single-objective function is

[0054] \(F = wC+\gamma(1 - w)E\ (12)

[0055] where \(w\) represents the weight factor and \(\gamma\) represents the proportionality factor.

[0056] The power flow equality constraints of the power system are:

[0057]

[0058] where, \(P D is the user load demand, \(P L is the transmission network loss, \(B ij is the network loss coefficient.

[0059] The upper and lower limits constraints of the generator set are:

[0060]

[0061] where and are the upper and lower limits of the active power output of the \(i\)-th generator set.

[0062] The ramp rate constraint formula of the unit is as follows:

[0063]

[0064] In the formula, UR i and DR i respectively represent the maximum values of the upward ramp rate and the downward ramp rate of the i-th thermal power generating unit.

[0065] S2. Suppose there are N groups of active power outputs of generating units to be dispatched, randomly initialize M dispatching schemes, and initialize and calculate N - 1 groups of active power outputs P i , i = 1, 2,... N - 1, which together form the active power output scheme matrix A of the generating units;

[0066]

[0067] where P imin and P imax are respectively the upper and lower limits of the active power output of the i-th group of generating units, and rand is a random number in [0, 1];

[0068] S3. Randomly initialize another dispatching scheme, with N - 1 groups of active power outputs being P′ i , i = 1, 2,... N - 1. Sequentially take out the active power output P′ i of a certain group of generators from this dispatching scheme, and replace the corresponding active power output P i of the generating units in each dispatching scheme in A to form a new M*(N - 1) dispatching scheme matrix A′, as shown in formula (18);

[0069]

[0070] S4. According to the Newton iteration method, find A and A′ from the power flow equality constraints, and the active power output P N of the N-th group of generators. The following are the iterative solution steps:

[0071] S4.1 According to the active power outputs P i of N - 1 groups of generating units, find the initial active power output of the N-th group of generating units:

[0072]

[0073] where is the initial active power output of the N-th group of generating units.

[0074] S4.2 According to the active power outputs P i of N groups of generating units, find the active power loss of the system:

[0075]

[0076] Among them

[0077] S4.3 Based on the user load P D , the active power outputs P i of N - 1 generator sets, and the active power loss of the system find the new active power output of the Nth generator set

[0078]

[0079] S4.4 Calculate the error If ε > the allowable error, return to step S4.2; otherwise save and exit.

[0080] S5. Calculate the active power output P N of the Nth group of generator sets and the penalty function value for exceeding the constraint range of the generator sets. The specific form of the penalty function is as follows:

[0081]

[0082] where λ is the penalty factor.

[0083] S6. Calculate the sum of the fuel costs and pollutant emissions of the N groups of generator sets in the scheduling schemes A and A′, and jointly form the fitness function values y i and y′ i ’, and then normalize them to [0, 1];

[0084] The specific process is as follows:

[0085]

[0086] S7. Execute the mutated black - winged kite algorithm to update the active power output P i of the generator sets:

[0087] Step S7.1: Calculate the latest position of the black - winged kite, and the specific formulas are shown in (24) and formula (25);

[0088] Attack behavior:

[0089]

[0090] Migration behavior:

[0091]

[0092] Step S7.2: Calculate the approximate optimal scheduling scheme P best , and the specific formula is shown in (26);

[0093]

[0094] Step S7.3: Use the attack behavior and migration behavior of the black-winged kite algorithm to find the approximate global optimal point P best As the initial optimal position of the vertical and horizontal crossover strategy. Update the position of P i The specific formulas are shown in (27) and (28);

[0095] Vertical crossover:

[0096]

[0097] Horizontal crossover:

[0098]

[0099] where r, r1, and r2 are random numbers between [0, 1].

[0100] Step S7.4: Calculate the active power output P of the generator set new The specific formula is shown in (29);

[0101] P new = P i - rd (29)

[0102] where r is the decreasing factor, decreasing from 1 to 0; d is the spacing.

[0103] Step S7.5: Calculate the active power output P of the generator set gbset The specific formula is shown in (30);

[0104]

[0105] where rand is a random number between [0, 1].[[]END]]

[0106] S8. If the maximum number of iterations is reached, output the optimal active power output P of the generator set gbest ; Otherwise, go to S3;

[0107] The optimal active power output P of the generator set gbest is the optimal new energy power economic emission dispatch scheme.

[0108] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A new energy power economic emission dispatch method based on the optimization of the mutated black-winged kite algorithm, characterized in that: S1. The weighted summation method is used to transform the two optimization objective functions of fuel cost E and pollution emission C into a single-objective optimization function, and combined with the power flow equation constraints of the power system, the active power output constraints of the generator sets, and the engine ramp rate constraints, to jointly establish an economic emission dispatch model for the new energy power system. The specific form is: The specific form of the fuel cost is: where N is the number of generator sets in the system; P i is the active power output of the i-th generator set; a i , b i and c i are the fuel cost coefficients of the i-th generator set; e i and f i are valve point effect coefficients. The specific form of the pollution emission is: where α i , β i , γ i , η i and δ i represent pollution emission coefficients. The fuel cost and pollution emission are transformed into a single-objective function by the weight summation method as F = wC + γ(1 - w)E (3) Where w represents the weight factor and γ represents the proportionality factor. The power flow equation constraint of the power system is: Among them, P D is the user load demand, and P L is the transmission network loss, and B ij is the network loss coefficient. The upper and lower limit constraints of the generator set are: wherein and are the upper and lower limits of the active power output of the i-th generating unit, respectively. The formula for the unit ramp rate constraint is: where UR i and DR i respectively represent the maximum values of the upward ramping rate and the downward ramping rate of the i-th thermal power generating unit. S2. There are N groups of active power outputs of generator sets to be scheduled. Randomly initialize M scheduling schemes, and initialize and calculate the active power outputs P of N - 1 groups according to formula (8) i , where i = 1, 2, … N - 1, which together form the active power output scheme matrix A of the generator sets where P imin and P imax are the upper and lower limits of the active power output of the i-th generator set respectively, and rand is a random number in [0, 1]; S3. Randomly initialize another scheduling plan, and the active power outputs of N - 1 groups are P′ i , where i = 1, 2, … N - 1. Sequentially take out the active power output P′ of a certain group of generators from this scheduling plan i , and replace the active power output P of the corresponding generator sets in each scheduling plan in A i , to form a new scheduling plan matrix A′ of M * (N - 1), as shown in formula (9); S4. According to Newton's iterative method, find A and A' from the power flow equation constraints, and the active power output P of the Nth group of generators N . The following are the iterative solution steps: S4.1 Calculate the initial active power output of the Nth generator set based on the active power outputs P of N - 1 generator sets i , and obtain the initial active power output of the Nth generator set: wherein is the initial active power output of the Nth generating unit. S4.2 Calculate the active power loss of the system according to the active power output P of N generator sets i , and obtain the active power loss of the system: Among them S4.3 According to the user load P D , the active power output P of N - 1 generator sets i , and the active power loss of the system calculate the new active power output of the Nth generator set S4.4 Calculation Error If ε > allowable error, return to step S4.2; otherwise, save and exit. S5. Calculate the active power output P of the Nth group of generators N The penalty function value that exceeds the constraint range of the generator set. The specific form of the penalty function is as follows: Where λ is the penalty factor. S6. Calculate the sum of the fuel costs and pollution emissions of the N sets of generator units in scheduling schemes A and A′, and jointly constitute the fitness function values y i and y′ i , and then normalize them to [0, 1]; The specific process is: S7. Execute the mutated black-winged kite algorithm to update the active power output P of the generator unit i : Step S7.1: Calculate the latest position of the black-winged kite, and the specific formulas are shown in (14) and formula (15); Attack behavior: Migration behavior: Step S7.2: Calculate the approximate optimal scheduling scheme P best , and the specific formula is as shown in (16); Step S7.3: The approximate global optimal point P found by the attack behavior and migration behavior of the black-winged kite algorithm best is used as the initial optimal position of the crisscross strategy. Update the position of P i , and the specific formulas are shown in (17) and (18); Longitudinal crossover: Transverse crossover: Where r, r1, r2 are random numbers between [0, 1]. Step S7.4: Calculate the active power output P of the generating unit new , and the specific formula is as shown in (19); P new = P i - rd (19) where r is a decreasing factor that decreases from 1 to 0; d is the spacing. Step S7.5: Calculate the active power output P of the generator set gbset , and the specific formula is as shown in (20); Where rand is a random number between [0, 1]. S8. If the maximum number of iterations is reached, output the optimal generator output P gbset ; Otherwise, go to S3; Optimal generator output P gbset That is the optimal new energy power economic emission dispatch plan.

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