Application of enhanced crown porcupine optimization method in new energy-containing electric power economic emission scheduling

Abstract

The invention relates to an application of an enhanced crown porcupine optimization method in new energy-containing electric power economic emission scheduling, and the method comprises the following steps: S1, converting the fuel cost and pollution emission into a single objective function through employing a weight sum method, and building a new energy electric power economic emission scheduling model through combining 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, executing an enhanced crown porcupine optimization method to update the active power output of the generator set; s8, if the maximum number of iterations is reached, outputting the optimal generator set output; otherwise, turning to S3.

Description

Technical Field

[0001] This invention relates to the field of economic emission dispatching technology for power systems, and in particular to the application of an enhanced crowned porcupine optimization method in economic emission dispatching of power systems containing renewable energy generation. Background Technology

[0002] Electricity is a crucial foundation for modern socio-economic development. For a long time, thermal power generation has dominated the global power system due to its mature technology and flexible dispatch capabilities. However, thermal power generation is highly dependent on fossil fuels, emitting large amounts of carbon dioxide and other pollutants during its operation, making it a major contributor to air pollution and global warming. Driven by the "dual-carbon goals" and the concept of sustainable development, new energy power generation is gradually becoming an important direction for the power industry. Therefore, how to rationally allocate fuel resources in power production, reduce emissions, and improve energy efficiency has become a key issue in power dispatch.

[0003] Wind and solar energy, as clean energy sources, consume no fuel during operation and have near-zero power generation costs, making them important targets for dispatch optimization. With the large-scale grid connection of new energy sources such as wind and solar power, the structure and operating characteristics of power systems have undergone profound changes. While new energy power generation has advantages such as being clean and renewable, its output is significantly affected by weather conditions, exhibiting strong randomness and uncertainty, which poses new challenges to the modeling and solving of economic dispatch problems. Therefore, research on economic dispatch problems in new energy power systems has significant theoretical and engineering application value. To address these challenges and optimize the utilization of new energy, research on the Dynamic Economic Emission Dispatch (DEED) problem is crucial for promoting the transformation of the energy structure from dependence on fossil fuels to a core of renewable energy.

[0004] DEED (Demand-Based Energy Allocation) is a problem involving the dynamic optimization of generator output within a given scheduling period (typically 24 hours) based on predicted load demand and system operating constraints. Its core objective is to minimize power system generation costs and pollutant emissions while ensuring the safe and reliable operation of the power system. Since generation costs and emission levels are often mutually constraining, DEED is essentially a multi-objective optimization problem with time-coupled characteristics. By solving DEED appropriately, energy efficiency can be improved, operating costs reduced, and greenhouse gas and harmful pollutant emissions effectively decreased.

[0005] Because the DEED problem is characterized by multiple objectives, nonlinearity, nonconvexity, and strong time coupling, traditional mathematical optimization methods often rely on strict model assumptions, are prone to getting trapped in local optima, and have limited adaptability to complex constraints and uncertainties. In contrast, swarm intelligence optimization algorithms do not rely on gradient information, possess strong global search capabilities and good robustness, and can effectively handle high-dimensional, complex, and multimodal optimization problems. Although swarm intelligence optimization methods can solve the power economic emission dispatch problem to some extent, they typically face challenges such as high computational cost, susceptibility to local optima, and slow convergence speed.

[0006] Therefore, to address the shortcomings of existing algorithms, an enhanced porcupine optimization method is developed for application in the economic emission dispatching of new energy power, in order to improve the efficiency and accuracy of problem solving and promote the development of the power industry towards a low-carbon and economical direction. Summary of the Invention:

[0007] The purpose of this invention is to provide a power dispatching method for new energy sources including wind and solar power that can simultaneously optimize fuel costs and pollution emissions, and to comprehensively consider both local and global convergence.

[0008] To achieve the above-mentioned objectives, this invention provides an application of an enhanced crowned porcupine optimization method in the economic emission dispatch of new energy power, comprising the following steps:

[0009] S1. The two optimization objective functions of fuel cost E and pollution emission C are transformed into a single objective optimization function by using the weighted summation method. Combined with the power system power flow equation constraints, generator active power output constraints and engine ramp rate constraints, an economic emission dispatch model for the new energy power system is jointly established.

[0010] S2. Suppose there are N groups of thermal power generating units whose active power output needs to be dispatched. M different dispatch schemes are randomly initialized. Calculate the active power output P of N-1 groups according to formula (1). i , i = 1, 2, ..., N-1, together constitute the active power output scheme matrix A of the generator set;

[0011]

[0012] Where P min and P max , which 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].

[0013] S3. Randomly initialize another scheduling scheme, with N-1 groups of active power output P′. Then, sequentially select a group of generator active power output P′ from this scheduling scheme. i Replace the active power output P of the generator set corresponding to each scheduling scheme in A. iThis forms a new M*(N-1) scheduling scheme matrix A′, as shown in formula (2);

[0014]

[0015] S4. Using Newton's iteration method and the power flow equation constraints, determine the active power output P of the Nth generator group from A and A'. N ;

[0016] S5. Calculate the active power output P of the Nth generator group. N The penalty function value that exceeds the generator set constraint range;

[0017] S6. Calculate the sum of fuel costs and pollution emissions of N generator sets in scheduling schemes A and A′, and combine this sum with the penalty function value to form the fitness function value y. i With y′ i Then normalize to [0,1];

[0018] S7. Implement the enhanced crowned porcupine method to optimize and update the active power output P of the generator set. i ;

[0019] S8. If the maximum number of iterations is reached, output the optimal generator output P. gbest Otherwise, switch to S3.

[0020] Specifically, in step S7, the process of optimizing the generator's active power output using the enhanced crowned porcupine method involves:

[0021] Visual-auditory behavior:

[0022]

[0023] Odor-Physical Behavior:

[0024]

[0025] Step S7.2: Calculate the approximate optimal scheduling scheme P best The specific formula is shown in (5);

[0026]

[0027] Step S7.3: Find the approximate global optimum P using the attack and migration behaviors of the crested porcupine method. best As the initial optimal position for both the horizontal and vertical axes. Update P. i The specific formulas for the position are shown in (6) and (7);

[0028]

[0029] Where N is the population size and F is the number of mutations / perturbations.

[0030] Step S7.4: Calculate the active power output P of the generator set. new The specific formula is shown in (8);

[0031] P new =P i -rd (8)

[0032] Where r is the decreasing factor, decreasing from 1 to 0; d is the spacing.

[0033] Step S7.5: Calculate the active power output P of the generator set. gbest The specific formula is shown in (9);

[0034]

[0035] Where rand is a random number in the range [0,1].

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This invention utilizes the enhanced crowned porcupine method to optimize the economic dispatch of new energy power containing wind and solar power generation. It can not only optimize the iterative update equations and avoid getting trapped in local optima, but also solve for a more economical and less polluting power dispatch scheme. Attached image description:

[0038] Figure 1 Method framework flowchart; Detailed implementation method:

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0040] like Figure 1 As shown in the figure, an enhanced crowned porcupine optimization method proposed in this invention is applied to the economic emission dispatch of new energy power, including the following steps:

[0041] S1. The two objective functions of fuel cost E and pollution emissions C are transformed into a single objective function using a weighted summation method. Combined with constraints on power system flow equations, generator active power output, and engine ramp rate, an economic emission dispatch model for the new energy power system is established. The specific form is as follows:

[0042] The specific form of fuel cost is:

[0043]

[0044] Where N is the number of generator sets in the system; P i Let a be the active power output of the i-th generator set; i b i and c i e is the fuel cost coefficient for the i-th generator set; i and f i This is the valve point effect coefficient.

[0045] The specific forms of pollution emissions are:

[0046]

[0047] Where α i ,β i γ i η i and δ i This represents the pollution emission coefficient.

[0048] The weighted summation method is used to transform fuel cost and pollution emissions into a single objective function.

[0049] F=wC+γ(1-w)E (12)

[0050] Where w represents the weighting factor and γ represents the scaling factor.

[0051] The power flow equation constraints of the power system are:

[0052]

[0053] Where P D To meet user load requirements, P L For transmission network loss, B ij This represents the network loss coefficient.

[0054] The upper and lower limit constraints for the generator set are:

[0055]

[0056] in and Let be the upper and lower limits of the active power output of the i-th generator set.

[0057] The formula for constraining the unit's ramp rate is:

[0058]

[0059] In the formula, UR i and DR i These represent the maximum values ​​of the upward and downward ramp rates of the i-th thermal power generating unit, respectively.

[0060] S2. Suppose there are N groups of generator sets whose active power output needs to be dispatched. M dispatch schemes are randomly initialized. The active power output P of N-1 groups is calculated according to formula (17). i , i = 1, 2, ... N-1, together constitute the active power output scheme matrix A of the generator set;

[0061]

[0062] Where P min and P max , which 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].

[0063] S3. Randomly initialize another scheduling scheme, with N-1 groups of active power output P′. i Let i = 1, 2, ..., N-1, and sequentially extract the active power output P′ of a certain group of generators from the scheduling scheme. i Replace the active power output P of the generator set corresponding to each scheduling scheme in A. i This forms a new M*(N-1) scheduling scheme matrix A′, as shown in formula (18);

[0064]

[0065] S4. Using Newton's iteration method, find A and A' from the power flow equation constraints. ′ The active power output P of the Nth generator group N The following are the iterative solution steps:

[0066] S4.1 Based on the active power output P of N-1 generator sets i Find the initial active power output of the Nth generator unit:

[0067]

[0068] in This represents the initial active power output of the Nth generator unit.

[0069] S4.2 Based on the active power output P of N generator sets i Find the active power loss of the system:

[0070]

[0071] in

[0072] S4.3 Based on user load P D The active power output P of N-1 generator sets i The system's active power loss Find the new active power output of the Nth generator unit.

[0073]

[0074] S4.4 Calculation Error If ε > allowable error, return to step S4.2; otherwise, save. And exit.

[0075] S5. Calculate the active power output P of the Nth generator group. N The penalty function value for exceeding the generator set's constraints. The specific form of the penalty function is as follows:

[0076]

[0077] Where λ is the penalty factor.

[0078] S6. Calculate scheduling schemes A and A ′ The sum of the fuel cost and pollution emissions of N generator sets, together with the penalty function value, constitutes the fitness function value y. i and y′ i’ Then normalize to [0,1];

[0079] The specific process is as follows:

[0080]

[0081] S7. Implement the enhanced crowned porcupine optimization method to update the generator unit's active power output P. i :

[0082] Step S7.1: Calculate the latest position of the crowned porcupine, as shown in formulas (15) and (16);

[0083] Visual-auditory behavior:

[0084]

[0085] Odor-Physical Behavior:

[0086]

[0087] Step S7.2: Calculate the approximate optimal scheduling scheme P best The specific formula is shown in (16);

[0088]

[0089] Step S7.3: Find the approximate global optimum P using the visual-auditory behavior and olfactory-physical behavior of the hooded porcupine method. best The optimal position is used as the initial position for the flight disturbance strategy. Update P. i The specific formulas are shown in (18) and (19);

[0090]

[0091] Step S7.4: Calculate the active power output P of the generator set. new The specific formula is shown in (20);

[0092] P new =P i -rd (20)

[0093] Where r is the decreasing factor, decreasing from 1 to 0; d is the spacing.

[0094] Step S7.5: Calculate the active power output P of the generator set. gbest The specific formula is shown in (21);

[0095]

[0096] Where rand is a random number in the range [0,1].

[0097] S8. If the maximum number of iterations is reached, output the optimal generator output P. gbest Otherwise, switch to S3;

[0098] Optimal generator output P gbest This is the optimal economic emission dispatch scheme for new energy power.

[0099] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

[0100] Experimental results: 6 thermal power plants + 1 wind power plant + 1 solar power plant

[0101] Pollution emissions (θ=0):

[0102] Table 1. Comparison of pollution emissions from different algorithms over 24 hours

[0103]

[0104] Combustion cost (θ=1):

[0105] Table 2 Comparison of combustion costs for different algorithms over 24 hours

[0106]

Claims

1. An application of an enhanced porcupine optimization method in economic emission dispatching of renewable energy power generation, characterized in that: S1. The two optimization objective functions, fuel cost E and pollution emission C, are transformed into a single objective optimization function using the weighted summation method. Combined with the constraints of power system power flow equations, generator active power output, and engine ramp rate, an economic emission dispatch model for the new energy power system is jointly established. Specifically, fuel costs are expressed as follows: Where N is the number of generator sets in the system; P i Let a be the active power output of the i-th generator set; i b i and c i e is the fuel cost coefficient for the i-th generator set; i and f i This is the valve point effect coefficient. The other objective function, pollution emissions, takes the following specific form: Where, α i ,β i γ i η i and δ i This represents the pollution emission coefficient. Using a weighted summation method, fuel cost and pollution emissions are transformed into a single objective function: F=wC+γ(1-w)E (3) Where w represents the weighting factor and γ represents the scaling factor. Subsequently, the power flow equation constraints of the power system are: Among them, P D To meet user load requirements, P L For transmission network loss, B ij This represents the network loss coefficient. The inequality constraints include the generator and unit ramp rates, and the mathematical formula is: in, and UR represents the upper and lower limits of the active power output of the i-th generator unit; i and DR i These represent the maximum values ​​of the upward and downward ramp rates of the i-th thermal power generating unit, respectively. S2. Suppose there are N groups of generator sets whose active power output needs to be dispatched. M dispatch schemes are randomly initialized. The active power output P of N-1 groups is calculated according to formula (8). i (i = 1, 2, ..., N-1) together constitute the active power output scheme matrix A of the generator set; Where, P min and P max 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 the range [0,1]. S3. Randomly initialize another scheduling scheme, with N-1 groups of active power output P. i (i = 1, 2, ..., N-1) The active power output P′ of a certain group of generators is sequentially selected from the scheduling scheme. i Replace the active power output P of the generator set corresponding to each scheduling scheme in A. i This forms a new M*(N-1) scheduling scheme matrix A′, as shown in formula (9); S4. Using Newton's iteration method and the power flow equation constraints, determine A and A', and the active power output P of the Nth generator group. N The following are the iterative solution steps: S4.1 Based on the active power output P of N-1 generator sets i Find the initial active power output of the Nth generator unit: in, This represents the initial active power output of the Nth generator unit. S4.2 Based on the active power output P of N generator sets i Calculate the active power loss of the system. in S4.3 Based on user load P D The active power output P of N-1 generator sets i The system's active power loss Find the new active power output of the Nth generator unit. 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 generator group. N The penalty function value for exceeding the generator set's constraints. The specific form of the penalty function is as follows: Where λ is the penalty factor. S6. Calculate the sum of fuel costs and pollution emissions of N generator sets in scheduling schemes A and A′, and combine this sum with the penalty function value to form the fitness function value y. i and y′ i Then normalize to [0,1]; The specific process is as follows: S7. Implement the enhanced crowned porcupine method to update the generator unit's active power output P. i : Step S7.1: Calculate the latest position of the crowned porcupine, as shown in formulas (15) and (16); Visual-auditory behavior: Odor-Physical Behavior: Step S7.2: Calculate the approximate optimal scheduling scheme P best The specific formula is shown in (16); Step S7.3: Find the approximate global optimum P using the visual-auditory behavior and olfactory-physical behavior of the hooded porcupine method. best The optimal position is used as the initial position for the flight disturbance strategy. Update P. i The specific formulas are shown in (18) and (19); Dstep=F×X d1 -X d2 (19) Step S7.4: Calculate the active power output P of the generator set. new The specific formula is shown in (20); P new =P i -rd (20) Where r is the decreasing factor, decreasing from 1 to 0; d is the spacing. Step S7.5: Calculate the active power output P of the generator set. gbest The specific formula is shown in (21); Where rand is a random number in the range [0,1]. S8. If the maximum number of iterations is reached, output the optimal generator output P. gbest Otherwise, switch to S3; Optimal generator output P gbest This is the optimal economic emission dispatch scheme for new energy power.