Enhanced transient search optimization method for electric power economic emission scheduling

Abstract

The invention aims to provide an enhanced transient search optimization method for electric power economic emission scheduling in order to solve the problems of an existing calculation method for electric power economic emission scheduling. According to the method, for complex constraint conditions of a generator set, a penalty function method is adopted to solve a constraint penalty value, an extreme value method is adopted to construct a fuel cost and pollutant emission scheduling problem model according to a price penalty factor, and an enhanced transient search optimization help algorithm jumps out of local optimum. And the optimal electric power economic emission scheduling scheme which is more economic and smaller in pollution emission and meets the constraint conditions can be solved more quickly.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching, and in particular to an enhanced transient search optimization method for power economic emission dispatching. Background Art

[0002] With rising electricity demand in recent years, harmful emissions from thermal power plants have increased exponentially. This not only damages the ecological environment and hinders national economic development, but also places pressure on power suppliers to comply with environmental regulations. In this context, reducing fuel costs and harmful emissions is crucial for power suppliers to remain competitive. Economic emissions dispatch aims to minimize the fuel and pollutant emissions costs of generators while meeting power demand and adhering to complex constraints such as generator operating restrictions, no-go zones, ramp limits, and power losses. However, due to its highly nonlinear, non-convex, discontinuous, non-differentiable nature, and valve point effects, the objective function has multiple local minima, making it extremely difficult to solve.

[0003] Traditional mathematical methods rely on initial starting points and often fail to converge to feasible solutions, making it difficult to achieve satisfactory results within a reasonable timeframe. Metaheuristic algorithms offer advantages in solving economic emissions scheduling problems due to their flexible mechanisms, efficient solutions, and ease of operation. However, conventional algorithms, such as whale optimization, sine and cosine, and atomic search algorithms, suffer from slow convergence, poor accuracy, and a tendency to fall into local optima. The resulting solutions are often suboptimal and lack competitiveness.

[0004] Existing scheduling technologies include the following: an improved particle swarm optimization algorithm for combined heat and power (CHP) economic emission scheduling (invention patent application publication number: CN 115630746 A), which uses the improved particle swarm optimization algorithm to solve the final objective function of the CHP system while ignoring the handling of constraints and objective functions; a cuckoo and bat hybrid optimization method for power economic emission scheduling (invention patent application publication number: CN 114066281 A), which uses a weighted summation method to transform fuel costs and pollution emissions into a single objective function and establishes a power economic emission scheduling model, but does not provide specific steps for setting weights; and a hybrid kernel search and slime mold optimization method for power economic emission scheduling (invention patent authorization announcement number: CN114066282 B), which uses a hybrid kernel search and slime mold optimization algorithm to find the optimal active power output of the generator set, focusing on algorithm integration and lacking guidance for constraint handling. Summary of the Invention

[0005] The present invention addresses the problems existing in existing calculation methods for power economic emission scheduling by providing an enhanced transient search optimization method for power economic emission scheduling. This method applies a penalty function approach to solve the complex constraints of power generators, employs an extreme value method to determine the constraint penalty value, and constructs a model for the fuel cost and pollutant emission scheduling problem using price penalty factors. Furthermore, enhanced transient search optimization is employed to help the algorithm escape local optima, allowing it to more quickly determine the optimal power economic emission scheduling solution that meets the constraints and is more economical and emits less pollutants.

[0006] To achieve the above-mentioned object, the present invention provides an enhanced transient search optimization method for power economic emission scheduling, comprising the following steps:

[0007] S1. Obtain the configuration parameters of the economic emission dispatch system, including the output range of the generator set, user load demand, fuel cost coefficient, valve point effect coefficient, pollution emission coefficient, network loss coefficient, climbing limit and no-drive zone limit;

[0008] S2. There are N scheduling schemes, each with D generators. According to formula (1), the output P of the i-th generator in a scheduling scheme is initialized using Logistic chaos. i , i=1,2,...,D-1,D:

[0009] (1)

[0010] (2)

[0011] Where Φ is the branch parameter of Logistic chaos, rand is a random number uniformly distributed between [0,1], and P i max and P i min are the upper and lower limits of the output of the i-th generator set, respectively, and finally form the generator set output dispatch matrix X as shown in formula (3):

[0012] (3)

[0013] S3. The constraints are converted into equality and inequality constraints, and the penalty function method shown in formula (4) is used to calculate the constraint penalty value:

[0014] (4)

[0015] Where ε represents the allowable error, N h and N g Represent the number of equality and inequality constraints respectively;

[0016] The equality constraints are:

[0017] (5)

[0018] Where, P d is the user load demand, P l is the loss of the transmission line, calculated by formula (6):

[0019] (6)

[0020] Where B ij is the element of the i-th row and j-th column of the network loss coefficient, B 0i is the i-th element of the network loss coefficient, B 00 is the network loss coefficient constant, D is the number of generator sets, P i and P j are the outputs of the i-th and j-th generator sets respectively;

[0021] The inequality constraints are as follows:

[0022] (7)

[0023] (8)

[0024] (9)

[0025] Where Z k is the number of prohibited zones for the i-th generator set, Z i,k min and Z i,k max are the lower limit and upper limit of the kth restricted zone of the i-th generator set; R i min and R i max are the lower and upper limits of the ramp rate of the i-th generator set;

[0026] S4. Use the extreme value method price penalty factor to convert pollutant emissions into emission costs, and combine the fuel cost and the constraint penalty value to form the total economic emission cost. The fuel cost including the valve point effect is:

[0027] (10)

[0028] Where a i , b i and c i is the fuel cost coefficient of the i-th generator set, d i and e i is the valve point effect coefficient of the i-th generator set;

[0029] Pollutant emissions are expressed as:

[0030] (11)

[0031] Where, α i , β i ,,γ i ,,η i and δ i is the pollutant emission coefficient of the i-th generator set;

[0032] The form of the total economic emission cost formed by using the extreme value method price penalty factor and constraint penalty value is as follows:

[0033] (12)

[0034] Where ζ is the penalty coefficient, Pf i is the price penalty factor of the i-th generator set solved by the extreme value method, which is calculated by the following formula:

[0035] (13)

[0036] S5. Enhance transient search optimization with optimal constraint guidance and Lévy flight to find the optimal generator dispatch solution;

[0037] The steps include:

[0038] Step S5.1. Obtain the scheduling solution Pp corresponding to the optimal constraint penalty value and the scheduling solution P corresponding to the minimum economic emission total cost from the calculated constraint penalty values ​​and economic emission total costs of the N scheduling solutions. g , as follows:

[0039] (14)

[0040] Step S5.2. Calculate the Lévy flight random number as follows:

[0041] (15)

[0042] Where λ is the shape parameter of the Lévy flight step length distribution, μ and ν are random numbers that conform to the standard normal distribution;

[0043] Step S5.3 Use the optimal constraint solution P p Guidance and Lévy flight update generator group dispatch solution P n , as follows:

[0044] (16)

[0045] Where, T is the maximum number of calculations;

[0046] S6. The current optimal solution when the maximum number of calculations is satisfied is the optimal scheduling solution for the generator set.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] The present invention adopts a penalty function method to solve the constraint penalty value for complex constraints of generator sets, such as generator set output limit, transmission line loss, climbing limit and no-driving zone, uses the extreme value method price penalty factor to construct a fuel cost and pollutant emission scheduling problem model, and adopts an enhanced transient search optimization to help the algorithm escape the local optimum and more quickly solve the optimal power economic emission scheduling solution that meets the constraints and is more economical and has lower pollution emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0050] The following describes the implementation of the present invention in detail with reference to the accompanying drawings. However, these drawings do not limit the present invention and are merely examples. The advantages of the present invention will become clearer and easier to understand through the illustrations.

[0051] The present invention provides an enhanced transient search optimization method for power economic emission scheduling, such as Figure 1 As shown, the following steps are included:

[0052] S1. Obtain the configuration parameters of the economic emission dispatch system, including the output range of the generator set, user load demand, fuel cost coefficient, valve point effect coefficient, pollution emission coefficient, network loss coefficient, climbing limit and no-drive zone limit;

[0053] S2. There are N scheduling schemes, each with D generators. According to formula (1), the output P of the i-th generator in a scheduling scheme is initialized using Logistic chaos. i , i=1,2,...,D-1,D:

[0054] (1)

[0055] (2)

[0056] Where Φ is the branch parameter of Logistic chaos, which is 4 here, rand is a random number uniformly distributed between [0,1], and P i max and P i min are the upper and lower limits of the output of the i-th generator set, respectively, and finally form the generator set output dispatch matrix X as shown in formula (3):

[0057] (3)

[0058] S3. The constraints are converted into equality and inequality constraints, and the penalty function method shown in formula (4) is used to calculate the constraint penalty value:

[0059] (4)

[0060] Where ε represents the allowable error, which is usually set to 0.0001, and N h and N g Represent the number of equality and inequality constraints respectively;

[0061] The equality constraints are:

[0062] (5)

[0063] Where, P d is the user load demand, P l is the loss of the transmission line, calculated by formula (6):

[0064] (6)

[0065] Where B ij is the network loss coefficient;

[0066] The inequality constraints are as follows:

[0067] (7)

[0068] (8)

[0069] (9)

[0070] Where Z k is the number of prohibited zones for the i-th generator set, Z i,k min and Z i,k max are the lower limit and upper limit of the kth restricted zone of the i-th generator set; R i min and R i max are the lower and upper limits of the ramp rate of the i-th generator set;

[0071] S4. Use the extreme value method price penalty factor to convert pollutant emissions into emission costs, and combine the fuel cost and the constraint penalty value to form the total economic emission cost. The fuel cost including the valve point effect is:

[0072] (10)

[0073] Where a i , b i and c i is the fuel cost coefficient of the i-th generator set, d i and e i is the valve point effect coefficient of the i-th generator set;

[0074] Pollutant emissions are expressed as:

[0075] (11)

[0076] Where, α i , β i ,,γ i ,,η i and δ i is the pollutant emission coefficient of the i-th generator set;

[0077] The form of the total economic emission cost formed by using the extreme value method price penalty factor and constraint penalty value is as follows:

[0078] (12)

[0079] Where ζ is the penalty coefficient, which is usually set to 100000, Pf i is the price penalty factor of the i-th generator set solved by the extreme value method, which is calculated by the following formula:

[0080] (13)

[0081] S5. Enhance transient search optimization with optimal constraint guidance and Lévy flight to find the optimal generator dispatch solution;

[0082] Step S5.1. Obtain the scheduling solution Pp corresponding to the optimal constraint penalty value and the scheduling solution P corresponding to the minimum economic emission total cost from the calculated constraint penalty values ​​and economic emission total costs of the N scheduling solutions. g , as follows:

[0083] (14)

[0084] Step S5.2. Calculate the Lévy flight random number as follows:

[0085] (15)

[0086] Where λ is the shape parameter of the Lévy flight step length distribution, usually set to 1.5, and μ and ν are random numbers that conform to the standard normal distribution;

[0087] Step S5.3 Use the optimal constraint solution P pGuidance and Lévy flight update generator group dispatch solution P n , as follows:

[0088] (16)

[0089] Where T is the maximum number of calculations; rand is a random number uniformly distributed between [0,1];

[0090] The pseudo code of this enhanced transient search optimization for power economic emission dispatch is as follows:

[0091]

[0092] S6. The current optimal solution when the maximum number of calculations is satisfied is the optimal scheduling solution for the generator set.

[0093] Other details not described belong to the prior art.

Claims

1. An enhanced transient search optimization method for power economic emission scheduling, characterized in that: The following steps are involved: S1. Obtain configuration parameters of the economic emission scheduling system; S2. There are N scheduling schemes, each with D generators. According to formula (1), the output P of the i-th generator in a scheduling scheme is initialized using Logistic chaos. i , i=1,2,...,D-1,D: （1） （2） Where Φ is the branch parameter of Logistic chaos, rand is a random number uniformly distributed between [0,1], and P i max and P i min are the upper and lower limits of the output of the i-th generator set, respectively, and finally form the generator set output dispatch matrix X as shown in formula (3): （3） S3. The constraints are converted into equality and inequality constraints, and the penalty function method shown in formula (4) is used to calculate the constraint penalty value: （4） Where ε represents the allowable error, N h and N g Represent the number of equality and inequality constraints respectively; The equality constraints are: （5） Where, P d is the user load demand, P l is the transmission line loss, calculated by formula (6): （6） Where B ij is the element of the i-th row and j-th column of the network loss coefficient, B 0i is the i-th element of the network loss coefficient, B 00 is the network loss coefficient constant, D is the number of generator sets, P i and P j are the outputs of the i-th and j-th generator sets respectively; The inequality constraints are: （7） （8） （9） Where Z k is the number of prohibited zones for the i-th generator set, Z i,k min and Z i,k max are the lower and upper limits of the kth restricted zone of the i-th generator set; R i min and R i max are the lower and upper limits of the ramp rate of the i-th generator set; S4. Use the extreme value method price penalty factor to convert pollutant emissions into emission costs, and combine the fuel cost and the constraint penalty value to form the total economic emission cost. The fuel cost including the valve point effect is: （10） Where a i , b i and c i is the fuel cost coefficient of the i-th generator set, d i and e i is the valve point effect coefficient of the i-th generator set; Pollutant emissions are expressed as: （11） Where, α i , β i , γ i , η i and δ i is the pollutant emission coefficient of the i-th generator set; The form of the total economic emission cost formed by using the extreme value method price penalty factor and constraint penalty value is as follows: （12） Where ζ is the penalty coefficient, Pf i is the price penalty factor of the i-th generator set solved by the extreme value method, which is calculated by the following formula: （13） S5. Enhance transient search optimization with optimal constraint guidance and Lévy flight to find the optimal generator dispatch solution; S6. The current optimal solution when the maximum number of calculations is satisfied is the optimal scheduling solution for the generator set.

2. The enhanced transient search optimization method for power economic emission scheduling according to claim 1 is characterized in that: The configuration parameters of the economic emission scheduling system include the output range of the generator set, user load demand, fuel cost coefficient, valve point effect coefficient, pollution emission coefficient, network loss coefficient, climbing limit and no-drive zone limit.

3. The enhanced transient search optimization method for power economic emission scheduling according to claim 1 is characterized in that: The specific method of step S5 is: Step S5.

1. Obtain the scheduling solution Pp corresponding to the optimal constraint penalty value and the scheduling solution P corresponding to the minimum economic emission total cost from the calculated constraint penalty values ​​and economic emission total costs of the N scheduling solutions. g , as follows: （14） Step S5.

2. Calculate the Lévy flight random number as follows: （15） Where λ is the shape parameter of the Lévy flight step length distribution, μ and ν are random numbers that conform to the standard normal distribution; Step S5.3 Use the optimal constraint solution P p Guidance and Lévy flight update generator group dispatch solution P n , as follows: （16） Where T is the maximum number of calculations.

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