Dynamic economic emission scheduling optimization method and system based on variable adaptive optimization

Abstract

The invention discloses a dynamic economic emission scheduling optimization method and system based on variable adaptive optimization, and belongs to the technical field of emission scheduling optimization. Comprising the following steps: constructing a dynamic economic emission scheduling optimization model by taking fuel cost and pollution emission minimization as a target; solving the dynamic economic emission scheduling optimization model through an improved multi-objective evolutionary algorithm, and determining a dynamic economic emission scheduling optimization scheme; the improved multi-objective evolutionary algorithm utilizes a variable adaptive optimization strategy to update a main population and an archived population, and utilizes a population reconstruction strategy to reconstruct the main population. Variables can be optimized from multiple angles, the optimization difficulty of coupling variables is reduced, the convergence speed of the population is increased, the population is effectively guided to converge towards the constrained Pareto frontier, and the feasibility is enhanced; the problem that an existing algorithm is slow in convergence and prone to falling into local optimum is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of emission scheduling optimization, and in particular to a dynamic economic emission scheduling optimization method and system based on variable adaptive optimization. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] By optimizing both the cost of power generation and the level of emissions, economic emission dispatch based on traditional emission dispatch methods has become a key strategy for reducing emissions in the power industry. Compared with upgrading power generation equipment or switching to renewable energy, economic emission dispatch is mainly achieved by adjusting the existing power dispatch strategy, making it easier for the power industry to reduce emissions.

[0004] In recent years, methods for optimizing emissions in economic dispatch have been continuously developed, but most of them are still focused on static models. These models do not fully consider the interaction between different dispatch time intervals, making it difficult to achieve global optimization. Dynamic economic emission dispatch, as an advanced form of economic emission dispatch, better meets the actual short-term dispatch needs, and is therefore more worthy of study.

[0005] However, dynamic economic emission scheduling is essentially a large-scale constrained multi-objective optimization problem, involving conflicts between objectives and various constraints. It has the characteristics of high dimensionality, strong coupling, nonlinearity and non-convexity. It is necessary to take into account the actual grid losses, changes in load demand and the impact of prohibited operating areas. These factors make the dynamic economic emission scheduling problem more complicated.

[0006] With the rapid development and widespread application of artificial intelligence, the existing technology uses mathematical modeling of dynamic economic emission scheduling problems and applies evolutionary algorithms to optimize them. Although these methods have reduced the burden of manual methods to a certain extent, dynamic economic emission scheduling is a problem with high-dimensional decision variables and multiple constraints, and the variable coupling relationship between different scheduling intervals is very complex, which leads to slow convergence of evolutionary algorithms and easy to fall into local optimality, and cannot provide a better optimization solution. Summary of the invention

[0007] In order to address the deficiencies of the prior art, the present invention provides a dynamic economic emission scheduling optimization method, system, electronic device, computer-readable storage medium and computer program product based on variable adaptive optimization, which utilizes variable adaptive optimization strategy to optimize variables from multiple angles, reduces the optimization difficulty of coupled variables and accelerates the convergence speed of the population; utilizes population reconstruction strategy to construct a new initial population to effectively guide the population to converge to the constrained Pareto frontier, enhances feasibility, and enables the population to provide more scheduling solutions.

[0008] In a first aspect, the present invention provides a dynamic economic emission scheduling optimization method based on variable adaptive optimization;

[0009] A dynamic economic emission scheduling optimization method based on variable adaptive optimization, comprising:

[0010] With the goal of minimizing fuel costs and pollution emissions, a dynamic economic emission scheduling optimization model is constructed;

[0011] Solving the dynamic economic emission scheduling optimization model by improving the multi-objective evolutionary algorithm to determine the dynamic economic emission scheduling optimization plan;

[0012] The improved multi-objective evolutionary algorithm uses a variable adaptive optimization strategy to update the main population and the archived population, and uses a population reconstruction strategy to reconstruct the main population.

[0013] In a second aspect, the present invention provides a dynamic economic emission scheduling optimization system based on variable adaptive optimization;

[0014] A dynamic economic emission scheduling optimization system based on variable adaptive optimization, comprising:

[0015] The model building module is configured to: construct a dynamic economic emission scheduling optimization model with the goal of minimizing fuel costs and pollution emissions;

[0016] The dynamic economic emission scheduling optimization module is configured to: solve the dynamic economic emission scheduling optimization model by improving the multi-objective evolutionary algorithm to determine the dynamic economic emission scheduling optimization scheme;

[0017] The improved multi-objective evolutionary algorithm uses a variable adaptive optimization strategy to update the main population and the archived population, and uses a population reconstruction strategy to reconstruct the main population.

[0018] In a third aspect, the present invention provides an electronic device;

[0019] An electronic device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned dynamic economic emission scheduling optimization method based on variable adaptive optimization.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium;

[0021] A computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the dynamic economic emission scheduling optimization method based on variable adaptive optimization as claimed in the claim.

[0022] In a fifth aspect, the present invention provides a computer program product;

[0023] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned dynamic economic emission scheduling optimization method based on variable adaptive optimization.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The technical solution provided by the present invention optimizes decision variables from multiple angles through a variable adaptive optimization strategy, can decouple complex decision variables, reduce the optimization difficulty of coupled variables and accelerate the convergence speed of the population.

[0026] 2. The technical solution provided by the present invention uses a population reconstruction strategy to construct a new initial population, improves the global search capability of the population, can quickly approach the constrained Pareto frontier, enhance feasibility and diversity, and enable the population to provide more dynamic economic emission scheduling optimization solutions.

[0027] 3. The technical solution provided by the present invention is highly robust and can be applied to a variety of dynamic economic scheduling problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0029] Figure 1 A schematic diagram of a flow chart provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0030] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0031] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0033] Embodiment 1

[0034] Since dynamic economic emission scheduling has high-dimensional decision variables and multiple constraints, the existing multi-objective evolutionary algorithm for optimizing it has the problem of slow algorithm convergence and easy to fall into local optimality, which affects the superiority of the dynamic economic emission scheduling optimization scheme; therefore, the present invention provides a dynamic economic emission scheduling optimization method based on variable adaptive optimization.

[0035] Next, combine Figure 1 , a dynamic economic emission scheduling optimization method based on variable adaptive optimization disclosed in this embodiment is described in detail. The dynamic economic emission scheduling optimization method based on variable adaptive optimization includes the following steps:

[0036] S1. With the goal of minimizing fuel costs and pollution emissions, a dynamic economic emission scheduling optimization model is constructed.

[0037] In this embodiment, the preset constraints of the dynamic economic emission scheduling optimization model are capacity constraints, ramp rate constraints and power balance constraints, and the objective function is expressed as follows:

[0038]

[0039] Among them, F(x) represents the total fuel cost of N power generation units within T scheduling time intervals, E(x) represents the total pollution emissions of N power generation units in the entire scheduling period, and x i,t represents the output active power of the i-th generating unit in the scheduling time interval t, x i,min represents the lower limit of the output of the active power of the i-th generating unit in the scheduling time interval t, A i , B i , C i , D i 、E i , H i and G i Represents the correlation coefficient.

[0040] The specific constraints are as follows:

[0041]

[0042] The first formula represents the capacity constraint of the power generation unit, x i,min and x i,max They represent the minimum and maximum output of the i-th power generation unit respectively; the second formula is the ramp rate constraint of the power generation unit, U Ri and D RiThey represent the ramp-up and ramp-down rate limits of the i-th generating unit, respectively, and ΔT is the length of each scheduling time interval. The third formula is the actual power balance constraint, x LD,t and x PL,t They represent the load demand and power loss in the time interval t respectively.

[0043] S2. Solve the dynamic economic emission scheduling optimization model by improving the multi-objective evolutionary algorithm and determine the dynamic economic emission scheduling optimization plan.

[0044] In order to improve the convergence speed of solving the dynamic economic emission scheduling problem, reduce the optimization difficulty and enhance the feasibility, in this embodiment, the improved multi-objective evolutionary algorithm uses a variable adaptive optimization strategy to update the main population and the archived population, and uses a population reconstruction strategy to reconstruct the main population.

[0045] As an implementation mode, step S2 specifically includes:

[0046] S201. Initialize a main population P of size NP and an empty archive population A.

[0047] S202, using the variable clustering method to divide the decision variables into convergence related variables x c and the diversity variable x d The specific process is as follows:

[0048] S2021. Randomly select nSel candidate solutions from the main population P, perform nPer perturbations on all decision variables of each of the nSel candidate solutions, and generate perturbed solutions.

[0049] Specifically, when one decision variable is perturbed, the other decision variables remain unchanged.

[0050] In this embodiment, the decision variable refers to the output of active power of the device.

[0051] S2022. Normalize the sample solutions, and use the perturbation solutions generated by each decision variable to perform fitting to generate a straight line L. In this way, the number of straight lines corresponding to each variable is nSel.

[0052] S2023. Calculate the angles between these straight lines and the normal line of the hyperplane respectively. Similarly, each variable obtains nSel angles. The normal line represents the convergence direction, and the angle value is used to measure the contribution of the variable to convergence and diversity. The larger the angle, the greater the contribution of the variable to diversity, and the smaller the angle, the greater the contribution to convergence.

[0053] S2024, using the K-means clustering method, the decision variables are divided into two categories according to the angle average of each variable, namely, the convergence related variables x c and the diversity variable xd .

[0054] Specifically, first, two angle values ​​are selected as the initial cluster centers, the difference between the remaining angle and the two cluster centers is calculated, and the decision variable is assigned to the cluster center with the smallest interpolation value. For each cluster, its cluster center is recalculated, that is, the mean of all data points, and finally the assignment and update steps are repeated until the cluster center no longer changes, that is, the variable with the smaller angle mean value is related to convergence, and the other variable is related to diversity.

[0055] S203, using variable interaction analysis to transform the convergence related variables x c Further divided into s subgroups.

[0056] Specifically, according to the given multi-objective optimization problem (MOP): minF(x) = (f1(x), f2(x), …, f M (x) T , if there exists x, a1, a2, b1, b2 and at least one f s (x)(1≤s≤M) satisfies the following conditions, then the variable is equal to x i and x j are interrelated:

[0057] f s (x)|x i =a2,x j =b1 <f s (x)|x i =a1,x j =b1

[0058] ^f s (x)|x i =a2,x j =b2>f s (x)|x i =a1,x j =b2

[0059] in, D is the number of decision variables.

[0060] S204, using variable adaptive optimization strategy to convergence related variables x c and the diversity variable x d Optimize and continuously update the main population P and archived population A. The specific process is as follows:

[0061] S2041, fixed diversity variable x d , for the convergence-related variable x c Each subgroup in performs the following operations in turn:

[0062] Only the decision variables of each subgroup are used to generate the offspring O, the main population P and the offspring O are merged and updated, and the next generation population P is output.

[0063] S2042, all convergence related variables x c After one round of optimization, the offspring O is obtained all , O all After merging with archived population A, update is performed and archived population A is output. It is determined whether the updated main population meets the switching condition. If so, S2043 is executed; if not, S2041 is executed.

[0064] The switching condition is that the change between the average value of the updated main population on each target and the last T generations is less than the given threshold δ, which is expressed as follows:

[0065]

[0066] Where C represents the main population, x is an individual in C; f i '(x) represents the normalized value of individual x on the i-th target; is the average value of the main population C on the i-th target; T and Tg represent the evolutionary generations respectively.

[0067] In this step, the convergence related variable x c The optimization aims to explore the entire target space and quickly approach the unconstrained Pareto frontier, so in S2042 only individuals in the first non-dominated layer are included in the main population.

[0068] S2043, fixed convergence related variables x c , using only the diversity variable x d Generate offspring O, merge populations P and O, update them, and output the next generation population P. At the same time, merge O and A, update them, and output the archived population A.

[0069] S2044: Determine whether the archived population meets the switching condition. If so, the variable adaptive optimization phase ends. Otherwise, execute step S2043.

[0070] In this step, the switching conditions are expressed as follows:

[0071]

[0072] Where C represents the archive population, and x is an individual in C; Represents the normalized value of individual x on the i-th target; is the average value of the archive population C at the i-th target; T and Tg represent the evolutionary generations respectively.

[0073] Among them, the optimization diversity variable x d The goal is to maximize the exploration of the unconstrained Pareto frontier to achieve a more uniform distribution of the population. Therefore, in this step, all individuals in the population are included in C.

[0074] S205, construct a new main population P' using the population reconstruction strategy. Specifically including:

[0075] S2051. Calculate the ideal point of the archived population A, which is composed of the minimum value of population A on each target, and then calculate the distance d from the individual in the archived population A to the ideal point.

[0076] S2052. Generate NP reference vectors uniformly in the target space.

[0077] S2053. For each individual in the archived population A, the angles between the straight line formed by the individual and the ideal point and NP reference vectors are calculated respectively, and each individual is associated with the reference vector with the smallest angle.

[0078] S2054. For each reference vector of an associated individual, calculate the median value d of the distance d of the associated individual m , the distance is greater than d m The individuals are selected and the rest are deleted to form a new main population P'.

[0079] S206, optimize the decision variables as a whole and update P' using ε constraint processing technology. Specifically including:

[0080] S2061. Calculate the constraint violation value ε according to the currently consumed evaluation times, expressed as follows:

[0081]

[0082] Among them, FE is the number of evaluations currently consumed, MaxFE is the maximum number of evaluations set, pp is the parameter that controls the rate of decrease of ε, which is set to 0.5, and ε0 is equal to the maximum constraint violation value in the population P'.

[0083] S2062. Use all decision variables to generate offspring O, merge O and P' into R, calculate the constraint violation values ​​of all individuals in R, select individuals with values ​​less than ε, called R1, and the remaining individuals are called R2.

[0084] S2063. If R1 is empty, use the constraint dominance criterion to sort R and select the next generation population.

[0085] Specifically, when sorting, the constraint dominance criterion first determines whether the solution meets all constraints, and gives priority to solutions that meet all constraints, emphasizing the feasibility of the solution. If two solutions both meet the constraints, they are compared through the Pareto dominance relationship. For solutions that do not meet the constraints, their constraint violation degrees are compared, and the one with the lower violation degree is better, guiding the solution to approach the feasible domain.

[0086] If R1 is not empty and is smaller than the set population size NP, R2 is sorted using the constraint dominance criterion to supplement the next generation population; if R1 is greater than or equal to the set population size NP, R1 is sorted using the constraint processing technology based on the multi-objective criterion to select the next generation population. This criterion converts multiple constraints into one or more new objective functions and compares the advantages and disadvantages using the Pareto dominance relationship.

[0087] S207, judging whether the set maximum number of evaluation times has been reached, if so, the algorithm terminates and outputs P', that is, a variety of dynamic economic emission scheduling optimization schemes, otherwise jump to S206.

[0088] Next, the performance of the method described in this embodiment on the dynamic economic emission scheduling problem is further demonstrated with reference to examples.

[0089] Three classic dynamic economic emission dispatch optimization cases are selected as optimization targets, namely DEED1, DEED2 and DEED3, which have 144, 240 and 336 devices respectively. The output of active power of these devices can be changed during the optimization process, that is, the number of their decision variables is 144, 240 and 336 respectively.

[0090] The algorithm parameters are set as follows: the population size P is set to 100, and the maximum number of evaluations is set to 1.0×10 for each of the three problems. 6 , 7.5×10 6 and 2.0×10 7 .

[0091] The optimization method described in the embodiment is compared with four advanced evolutionary algorithms (LMEA, LMOCSO, DPSEA, POCEA) that have been used for dynamic economic emission scheduling. The obtained hypervolume index (HV) data are shown in Table 1. Among them, the larger the HV value, the better the algorithm effect, and the more optimal solutions the algorithm can provide. If the algorithm cannot find a feasible solution during operation, the result is represented by "NaN". Analysis of the data in Table 1 shows that the method described in this embodiment achieves optimal performance in all three cases, which is better than all the comparison algorithms. This is attributed to the proposed variable adaptive optimization strategy and the archive-assisted population reconstruction method, which accelerates the convergence speed of the population, is conducive to obtaining the optimal population, and provides more optional scheduling solutions. In actual production operations, users can fully combine these solutions according to their own conditions to design the most satisfactory production scheduling planning guide.

[0092] Table 1

[0093]

[0094] Embodiment 2

[0095] This embodiment discloses a dynamic economic emission scheduling optimization system based on variable adaptive optimization, including:

[0096] The model building module is configured to: construct a dynamic economic emission scheduling optimization model with the goal of minimizing fuel costs and pollution emissions;

[0097] The dynamic economic emission scheduling optimization module is configured to: solve the dynamic economic emission scheduling optimization model by improving the multi-objective evolutionary algorithm to determine the dynamic economic emission scheduling optimization scheme;

[0098] The improved multi-objective evolutionary algorithm uses a variable adaptive optimization strategy to update the main population and the archived population, and uses a population reconstruction strategy to reconstruct the main population.

[0099] It should be noted that the above-mentioned model building module and dynamic economic emission scheduling optimization module correspond to the steps in Embodiment 1, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0100] Embodiment 3

[0101] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned dynamic economic emission scheduling optimization method based on variable adaptive optimization are completed.

[0102] Embodiment 4

[0103] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned dynamic economic emission scheduling optimization method based on variable adaptive optimization are completed.

[0104] Embodiment 5

[0105] Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned dynamic economic emission scheduling optimization method based on variable adaptive optimization.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic economic emission scheduling optimization method based on variable adaptive optimization, characterized in that: include: With the goal of minimizing fuel costs and pollution emissions, a dynamic economic emission scheduling optimization model is constructed; Solving the dynamic economic emission scheduling optimization model by improving the multi-objective evolutionary algorithm to determine the dynamic economic emission scheduling optimization plan; The improved multi-objective evolutionary algorithm uses a variable adaptive optimization strategy to update the main population and the archived population, and uses a population reconstruction strategy to reconstruct the main population.

2. The dynamic economic emission scheduling optimization method based on variable adaptive optimization according to claim 1 is characterized in that: The method of updating the main population and the archived population by using the variable adaptive optimization strategy is specifically: optimizing the convergence-related variables and the diversity variables by using the variable adaptive optimization strategy, and continuously updating the main population and the archived population until the switching condition is met.

3. The dynamic economic emission scheduling optimization method based on variable adaptive optimization according to claim 2 is characterized in that: The optimization of convergence-related variables and diversity variables by using variable adaptive optimization strategy includes: The diversity variables are fixed, and the variables in each subgroup of the convergence-related variables are used to generate offspring, and the offspring are merged with the main population to update the main population; All the obtained offspring are merged with the archived population to update the archived population.

4. The dynamic economic emission scheduling optimization method based on variable adaptive optimization according to claim 2 is characterized in that: The switching condition is that the average value of the population on each target and the change between offspring are less than a given threshold.

5. The dynamic economic emission scheduling optimization method based on variable adaptive optimization according to claim 1, characterized in that: The method of reconstructing the main population by using the population reconstruction strategy includes: Determine the ideal point of the archived population, calculate the distance between individuals in the archived population and the ideal point, and generate multiple reference vectors in the target space; Calculate the angle between the line connecting each individual and the ideal point and the reference vector, and associate the individual with the reference vector with the smallest angle; The median value of the distance between the reference vector and all associated individuals is calculated and used as a threshold to filter individuals and reconstruct the main population.

6. The dynamic economic emission scheduling optimization method based on variable adaptive optimization according to claim 1, characterized in that: The solving of the dynamic economic emission scheduling optimization model by using an improved multi-objective evolutionary algorithm includes: in the process of solving the dynamic economic emission scheduling optimization model according to the improved multi-objective evolutionary algorithm, using ε constraints to update the reconstructed main population.

7. Dynamic economic emission scheduling optimization system based on variable adaptive optimization, characterized by: include: The model building module is configured to: construct a dynamic economic emission scheduling optimization model with the goal of minimizing fuel costs and pollution emissions; The dynamic economic emission scheduling optimization module is configured to: solve the dynamic economic emission scheduling optimization model by improving the multi-objective evolutionary algorithm to determine the dynamic economic emission scheduling optimization scheme; The improved multi-objective evolutionary algorithm uses a variable adaptive optimization strategy to update the main population and the archived population, and uses a population reconstruction strategy to reconstruct the main population.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the dynamic economic emission scheduling optimization method based on variable adaptive optimization as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the dynamic economic emission scheduling optimization method based on variable adaptive optimization as described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the dynamic economic emission scheduling optimization method based on variable adaptive optimization as described in any one of claims 1 to 6 are implemented.

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