Electric power system environment economic dispatching method and terminal based on multiple targets

Abstract

The invention discloses a multi-target-based power system environmental economic dispatching method and terminal, and the method has guiding significance for the optimal dispatching of a new energy power system containing a wind power plant, and can effectively reduce the operation cost of the whole system. The robustness is improved; meanwhile, an environmental economic dispatching model considering the carbon transaction cost and the green certificate transaction cost is established, the negative influence of pollutant emission of a coal-fired unit on the environment can be considered, and the method is a power generation dispatching strategy considering environmental protection and economic benefits; and solving the environmental economy scheduling model based on a firefly-bat hybrid algorithm to obtain an optimal solution set, and fully mining information contained in the optimal solution set by comprehensively applying a density-based clustering algorithm and an analytic hierarchy process to assist an operator in determining a proper scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatch automation, and in particular to a multi-objective-based power system environmental economic dispatch method and terminal. Background Art

[0002] In the new power system, wind energy, as a renewable green energy, is of great significance to alleviating the energy crisis worldwide. Large-scale wind power grid connection is the main mode of developing and utilizing wind energy at present, and it is also an important part of building a strong smart grid. Unlike small wind farms that are connected to the distribution network as distributed power sources and consumed locally, large-scale wind power needs to rely on transmission channels to be delivered to the load center over long distances. The randomness and intermittency of wind speed directly affect the active power output of wind farms, adding new uncertainties to the economic dispatch of traditional power systems.

[0003] In terms of models, traditional power generation dispatch models all use economic benefits as a single optimization goal, without considering the negative impact of pollutant emissions from coal-fired units on the environment. As global environmental pollution problems become increasingly severe, it has become a consensus to increase energy conservation and emission reduction efforts in the power industry. In this context, power generation dispatch strategies that take into account both environmental protection and economic benefits, namely environmental economic dispatch, have received widespread attention. In addition, there is no solution for multi-objective optimization problems that achieves optimal economic and environmental performance at the same time, but rather an optimal solution set, which is not conducive to actual application by operators.

[0004] Therefore, there are currently some optimization scheduling schemes suitable for new power systems, but they basically only consider maximizing economic benefits, without considering the negative impact of pollutant emissions from coal-fired units on the environment. In addition, when using multi-objective optimization algorithms, the calculation is time-consuming and lacks robustness. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a multi-objective environmental economic dispatching method and terminal for power systems, which can effectively reduce the computing cost of the overall system, improve robustness, provide a reference basis for the trade-off between environmental protection and economy, and assist operators in determining the final dispatching plan.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A multi-objective-based environmental economic dispatch method for a power system comprises the following steps:

[0008] S1. Combining the functional relationship between the active output of the wind turbine and the wind speed with the law of wind speed variation, the cumulative probability density function of the active output of the wind turbine is calculated;

[0009] S2. Based on multi-objective opportunity constrained programming, an environmental economic dispatch model including a wind farm is established taking into account the carbon trading-green certificate transaction cost, and the cumulative probability density function of the active output of the wind turbine is used to perform a deterministic transformation on the environmental economic dispatch model;

[0010] S3, solving the transformed environmental economic dispatch model based on the firefly-bat hybrid algorithm to obtain the optimal solution set;

[0011] S4. Clustering the optimal solution set using a density-based clustering algorithm to divide the optimal subsets with similar characteristics, sorting the non-inferior solutions in each category using the analytic hierarchy process, and obtaining an environmental economic scheduling plan based on the sorting results.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A multi-objective-based power system environmental economic dispatch terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the multi-objective-based power system environmental economic dispatch method is implemented.

[0014] The beneficial effects of the present invention are: by combining the wind speed variation law and the functional formula of the wind turbine active output, the cumulative probability density function of the active output can be calculated; then based on the multi-objective opportunity constraints, an environmental economic dispatch model considering the carbon trading and green certificate transaction costs is established; the environmental economic dispatch model is solved based on the firefly bat hybrid algorithm to obtain the optimal solution set, and then the optimal solution set is clustered using a density-based clustering algorithm to obtain the optimal subset, and the environmental economic dispatch scheme is obtained by using the hierarchical analysis method to sort the non-inferior solutions for each optimal subset. In this way, the power system environmental economic dispatch method based on the multi-objective firefly bat hybrid algorithm has guiding significance for the optimal dispatch of the new energy power system containing wind farms, can effectively reduce the operating cost of the overall system, improve robustness, and at the same time consider the negative impact of pollutant emissions from coal-fired units on the environment. It is a power generation dispatch strategy that takes into account both environmental protection and economic benefits; the density-based clustering algorithm and the hierarchical analysis method are comprehensively applied to fully explore the information contained in the optimal solution set to assist operators in determining appropriate dispatching plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of a multi-objective-based environmental economic dispatch method for a power system according to an embodiment of the present invention;

[0016] Figure 2 A schematic diagram of a multi-objective-based environmental economic dispatch terminal for a power system according to an embodiment of the present invention;

[0017] Description of labels:

[0018] 1. A multi-objective-based environmental economic dispatch terminal for power systems; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0019] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0020] Please refer to Figure 1 The embodiment of the present invention provides a multi-objective-based environmental economic dispatch method for a power system, comprising the steps of:

[0021] S1. Combining the functional relationship between the active output of the wind turbine and the wind speed with the law of wind speed variation, the cumulative probability density function of the active output of the wind turbine is calculated;

[0022] S2. Based on multi-objective opportunity constrained programming, an environmental economic dispatch model including a wind farm is established taking into account the carbon trading-green certificate transaction cost, and the cumulative probability density function of the active output of the wind turbine is used to perform a deterministic transformation on the environmental economic dispatch model;

[0023] S3, solving the transformed environmental economic dispatch model based on the firefly-bat hybrid algorithm to obtain the optimal solution set;

[0024] S4. Clustering the optimal solution set using a density-based clustering algorithm to divide the optimal subsets with similar characteristics, sorting the non-inferior solutions in each category using the analytic hierarchy process, and obtaining an environmental economic scheduling plan based on the sorting results.

[0025] From the above description, it can be seen that the beneficial effects of the present invention are: by combining the wind speed variation law and the functional formula of the wind turbine active output, the cumulative probability density function of the active output can be calculated; then based on the multi-objective opportunity constraints, an environmental economic dispatch model considering the carbon trading and green certificate transaction costs is established; the environmental economic dispatch model is solved based on the firefly bat hybrid algorithm to obtain the optimal solution set, and then the optimal solution set is clustered using the density-based clustering algorithm to obtain the optimal subset, and the environmental economic dispatch scheme is obtained by using the hierarchical analysis method to sort the non-inferior solutions for each optimal subset. In this way, the power system environmental economic dispatch method based on the multi-objective firefly bat hybrid algorithm has guiding significance for the optimal dispatch of the new energy power system containing wind farms, which can effectively reduce the operating cost of the overall system and improve robustness. At the same time, it takes into account the negative impact of pollutant emissions from coal-fired units on the environment. It is a power generation dispatch strategy that takes into account both environmental protection and economic benefits; the density-based clustering algorithm and the hierarchical analysis method are comprehensively applied to fully explore the information contained in the optimal solution set to assist operators in determining appropriate dispatching schemes.

[0026] Further, step S1 includes:

[0027] Use the two-parameter Weibull distribution to describe the random variation of wind speed:

[0028] F(v)=1-exp[-(v / c) k ]

[0029] And calculate the probability density function of wind speed:

[0030] f(v)=(k / c)(v / c) k-1 exp[-(v / c) k ]

[0031] In the formula, v represents wind speed, c represents scale parameter, and k represents shape parameter;

[0032] Establish the functional relationship between the active output of the fan and the wind speed:

[0033]

[0034] Where P WT Indicates the active output of the fan, P rate Indicates the rated power of the fan, v in 、v rate 、v out They represent the cut-in wind speed, rated wind speed and cut-out wind speed respectively;

[0035] Combining the random variation law of the wind speed, the probability density function, and the functional relationship between the active output of the wind turbine and the wind speed, the cumulative probability density function of the active output of the wind turbine is calculated:

[0036]

[0037] From the above description, it can be seen that by calculating the cumulative probability density function of the wind turbine active output, the dependence between the wind turbine active output and the wind speed can be more deeply obtained, which is helpful for the establishment of the subsequent scheduling model.

[0038] Further, step S2 includes:

[0039] S21. Establish a carbon trading-green certificate transaction cost model;

[0040] S22. Based on multi-objective opportunity constrained programming, the objective function and constraint conditions of the environmental economic dispatch model including the wind farm are established in combination with the carbon trading-green certificate transaction cost model;

[0041] S23. Deterministically transform the environmental economic dispatch model using the cumulative probability density function of the wind turbine active output.

[0042] From the above description, it can be seen that when establishing an environmental economic dispatch model containing wind farms, the carbon trading-green certificate transaction costs are taken into consideration. Through carbon trading and green certificate trading, surplus carbon emission quotas or green certificates can be converted into actual income, thereby reducing overall operating costs; and the cumulative probability density function of the active output of wind turbines is used to perform a deterministic transformation of the environmental economic dispatch model, which can effectively reduce the computational cost of the overall system and improve robustness.

[0043] Further, step S21 includes:

[0044] Establishing carbon trading costs F c :

[0045]

[0046] In the formula, represents the carbon trading price, E c Represents the carbon emissions of thermal power units, E q1 Indicates the free carbon emission quota of thermal power units, E q2 It means free carbon emission quota for wind farms;

[0047] Establish green certificate transaction cost F S :

[0048] F S =P TCG (kP D -P W )

[0049] Where P TCG represents the green certificate transaction price, k represents the quota ratio of renewable energy power generation to grid-connected power, P D Represents the power system load, P W Indicates wind power output.

[0050] From the above description, it can be seen that the carbon trading cost of the power system is derived based on the principle of the carbon trading mechanism, and the green certificate trading cost is established, so as to take into account the negative impact of pollutant emissions on the environment in the future.

[0051] Further, step S22 includes:

[0052] Establish the objective function of the environmental economic dispatch model including wind farms:

[0053] min[F,E]

[0054] F=F C +F S +F G

[0055]

[0056] In the formula, F represents the operating cost of the power system, F G represents the power generation cost of the power system, P i represents the active output of coal-fired unit i, F Gi (P i ) represents the consumption characteristics of coal-fired unit i; E represents the emission of pollutant gas, α i , β i , γ i , i , i represents the pollutant gas emission coefficient of coal-fired unit i;

[0057] Constraints for establishing an environmental economic dispatch model with wind farms:

[0058] Establish power balance constraints:

[0059]

[0060] Where P L represents the network loss, η1 represents the first confidence level of meeting the load demand;

[0061] Establish a positive spinning reserve capacity constraint:

[0062]

[0063] Where P imax represents the upper limit of active power output of coal-fired unit i, U SR represents the backup requirement of the conventional system, w u represents the demand coefficient of wind farm output for positive spinning reserve, η2 represents the second confidence level for satisfying the positive spinning reserve capacity constraint;

[0064] Create a negative spinning reserve capacity constraint:

[0065]

[0066] Where: P Wmax Indicates the rated output of the wind farm, w d represents the demand coefficient of wind farm output for negative spinning reserve, η3 represents the third confidence level for satisfying the negative spinning reserve capacity constraint;

[0067] Establish output constraints for coal-fired units and wind farms.

[0068] From the above description, it can be seen that setting the objective function with the goal of minimizing the operating cost of the power system and the emission of pollutants, and setting the power balance constraint, the spinning reserve capacity constraint and the unit output constraint at the same time can effectively reduce the operating cost of the overall system, and take into account the negative impact of pollutant emissions on the environment, so as to obtain a suitable environmental and economic scheduling plan.

[0069] Further, step S23 includes:

[0070] According to the cumulative probability density function of the wind turbine active output, the power balance constraint is transformed into:

[0071]

[0072] The positive spinning reserve capacity constraint is transformed into:

[0073]

[0074] The negative spinning reserve capacity constraint is transformed into:

[0075]

[0076] From the above description, we can see that since both the power balance constraint and the positive and negative spinning reserve capacity constraints contain random variables P W , which is in probabilistic form and is not easy to solve, so the above constraints are deterministically transformed according to the cumulative probability density function of the active output of the wind turbine, which can save computing costs.

[0077] Further, step S3 includes:

[0078] Initialize the populations of the firefly algorithm and the bat algorithm, and set the operation parameters of the firefly algorithm and the bat algorithm;

[0079] In each calculation iteration, the firefly algorithm and the bat algorithm are used to update their own individuals, and the updated individuals are merged into a candidate solution set, and the candidate solution set is non-dominated sorted;

[0080] When the number of iterations reaches the preset maximum number or the change in the iteration result is less than the change threshold, the final candidate solution set for non-dominated sorting is output as the optimal solution set.

[0081] From the above description, it can be seen that the firefly algorithm and the bat algorithm are combined to obtain the firefly bat hybrid optimization algorithm, and the optimal solution set is obtained based on this. The advantages of the firefly algorithm and the bat algorithm can be combined to improve the accuracy of the solution.

[0082] Further, step S4 includes:

[0083] S41, automatically determining the number of clusters by density parameters, and clustering the optimal solution set to obtain an optimal subset;

[0084] S42. Determine the weight of each attribute by calculating the standard deviation of the optimal solution in the optimal solution set and the correlation between the attributes, sort each type of optimal subset using the weight of each attribute through the hierarchical analysis method, and obtain the environmental economic scheduling plan according to the sorting result.

[0085] From the above description, it can be seen that the comprehensive application of density-based clustering algorithm and hierarchical analysis method can fully explore the information contained in each optimal subset to assist operators in determining appropriate scheduling solutions.

[0086] Please refer to Figure 2 Another embodiment of the present invention provides an environmental-economic dispatch terminal for a power system based on multiple objectives, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements the various steps of the above-mentioned environmental-economic dispatch method for a power system based on multiple objectives.

[0087] The above-mentioned multi-objective-based power system environmental economic dispatch method and terminal of the present invention are suitable for effectively reducing the calculation cost of the overall system and improving the robustness in the new energy power systematization, and provide a reference basis for the trade-off between environmental protection and economy, and assist the operator to determine the final dispatch plan. The following is an explanation through specific implementation methods:

[0088] Embodiment 1

[0089] Please refer to Figure 1 , a multi-objective-based environmental economic dispatch method for power system, comprising the steps of:

[0090] S1. Combining the functional relationship between the active output of the wind turbine and the wind speed with the law of wind speed variation, the cumulative probability density function of the active output of the wind turbine is calculated.

[0091] S11. Use the two-parameter Weibull distribution to describe the random variation of wind speed:

[0092] F(v)=1-exp[-(v / c) k ]

[0093] And calculate the probability density function of wind speed:

[0094] f(v)=(k / c)(v / c) k-1 exp[-(v / c) k ]

[0095] In the formula, v represents wind speed; c represents scale parameter, unit is m / s; k represents shape parameter;

[0096] S12. Establish the functional relationship between the active output of the wind turbine and the wind speed:

[0097]

[0098] Where P WT Indicates the active output of the fan, P rate Indicates the rated power of the fan, v in 、v rate 、v out Respectively represent the cut-in wind speed, rated wind speed and cut-out wind speed; where P WT is a mixed random variable, that is, in the interval (0,P rate ) and between 0 and P rate The place is discrete.

[0099] Combining the random variation law of the wind speed, the probability density function, and the functional relationship between the active output of the wind turbine and the wind speed, the cumulative probability density function of the active output of the wind turbine is calculated:

[0100]

[0101] S2. Based on multi-objective opportunity constrained programming, an environmental economic dispatch model including wind farms is established by taking into account the carbon trading-green certificate transaction costs, and the cumulative probability density function of the active output of the wind turbine is used to perform deterministic transformation on the environmental economic dispatch model.

[0102] S21. Establish a carbon trading-green certificate transaction cost model:

[0103] S211. When the carbon emissions of a thermal power plant are greater than the free carbon emission quota, it is necessary to purchase carbon emission quotas in the carbon trading market; conversely, it can sell excess free carbon emission quotas in the carbon trading market to obtain certain benefits. According to the principle of the carbon trading mechanism, the carbon trading cost F is established. c for:

[0104]

[0105] In the formula, represents the carbon trading price, E c Represents the carbon emissions of thermal power units, E q1 Indicates the free carbon emission quota of thermal power units, E q2 Represents free carbon emission allowances for wind farms.

[0106] Among them, the carbon emission quota of the power industry is mainly free allocation and paid purchase. The free allocation part adopts the baseline method, that is, the free carbon emission quota is allocated in proportion to the power generation of each thermal power plant. The carbon emission allocation coefficient per unit of electricity can be determined according to the regional power grid baseline emission factor.

[0107] Since wind power and photovoltaic power are random, when wind power and photovoltaic power are connected to the grid, in order to ensure the safety of the grid, it is necessary to increase the spinning reserve capacity of the thermal power units, which increases the operating cost of the thermal power units. Therefore, the carbon emission quota of the thermal power units should be compensated. The free carbon emission quota E of the thermal power units q1 for:

[0108]

[0109] Where N is the number of coal-fired units in the power system; δ is the baseline emission factor of the regional power grid; P i represents the output of thermal power unit i; P W represents wind power output; α represents the reserve coefficient of wind power output; λ represents the carbon quota correction coefficient of wind power generation.

[0110] Free carbon emission quotas for wind farms q2 For: E q2 =δP W

[0111] Since wind power is a clean energy source and does not produce CO2, it is considered that CO2 emissions all come from thermal power units. The carbon emissions of thermal power units are proportional to the active output and can be expressed as:

[0112]

[0113] In the formula, E C Represents the carbon emissions of thermal power units; η i Represents the carbon emission coefficient of the i-th thermal power unit.

[0114] S212. Green certificates are proof of the green attributes of renewable energy electricity. The green certificate trading mechanism is a supporting measure to ensure the effective implementation of the renewable energy quota system. Government agencies certify specific renewable energy power generation and issue tradable certificates with renewable energy power logos. When the actual renewable energy consumption is greater than the green certificate quota ratio, the green certificates generated by the excess renewable energy power generation can be sold to gain benefits; when the actual renewable energy consumption is less than the green certificate quota ratio, green certificates need to be purchased for the insufficient renewable energy consumption. Therefore, the cost of establishing a green certificate transaction is F S :

[0115] F S =P TCG (kP D -P W )

[0116] Where P TCG represents the green certificate transaction price, k represents the quota ratio of renewable energy power generation to grid-connected power, P DRepresents the power system load, P W Indicates wind power output.

[0117] S22. Based on multi-objective opportunity constrained programming, the objective function and constraint conditions of the environmental economic dispatch model including wind farms are established in combination with the carbon trading-green certificate transaction cost model:

[0118] S221. Establish the objective function of the environmental economic dispatch model including wind farms:

[0119] min[F,E]

[0120] F=F C +F S +F G

[0121]

[0122] Where, F represents the operating cost of the power system, in MW; F G represents the power generation cost of the power system; P i represents the active output of coal-fired unit i; F Gi (P i ) represents the consumption characteristics of coal-fired unit i; E represents the emission of pollutant gas; α i , β i , γ i , i , i It represents the pollutant gas emission coefficient of coal-fired unit i, which can be obtained by fitting method based on the harmful gas emission detection data of the power plant.

[0123] Among them, the consumption characteristics of coal-fired units considering the valve point effect are:

[0124] F Gi (P i )=a i +b i P i +c i P i 2 +|e i sin[f i (P imin -P i )]

[0125] In the formula, a i 、b i 、c i 、e i 、f i Both represent the power generation cost coefficient; P imin It represents the lower limit of active power output of coal-fired unit i, in MW.

[0126] S222. Constraints for establishing an environmental economic dispatch model with wind farms:

[0127] Establish power balance constraints:

[0128]

[0129] Where P L Indicates network loss, in MW; Ω(P W ) represents the wind farm output P W function. Since P W is a random variable, so the above formula can be described in the form of probability as:

[0130]

[0131] In the formula, η1 represents the first confidence level of meeting the load demand;

[0132] Establish a positive spinning reserve capacity constraint:

[0133]

[0134] Where P imax represents the upper limit of active power output of coal-fired unit i, U SR represents the backup requirement of the conventional system, w u represents the demand coefficient of wind farm output for positive spinning reserve, η2 represents the second confidence level for satisfying the positive spinning reserve capacity constraint;

[0135] Create a negative spinning reserve capacity constraint:

[0136]

[0137] Where: P Wmax Indicates the rated output of the wind farm, w d represents the demand coefficient of wind farm output for negative spinning reserve, η3 represents the third confidence level for satisfying the negative spinning reserve capacity constraint;

[0138] Establish output constraints for coal-fired units: P imin ≤P i ≤P imax .

[0139] Establish wind farm output constraint: 0≤P W ≤P Wmax .

[0140] S23, using the cumulative probability density function of the wind turbine active output to perform deterministic transformation on the environmental economic dispatch model:

[0141] Since both the power balance constraint and the positive and negative spinning reserve capacity constraints contain random variables P W , which is in probabilistic form and difficult to solve, so the constructed random model needs to be converted into a deterministic optimization model for solution.

[0142] According to the cumulative probability density function of the wind turbine active output, the power balance constraint is transformed into:

[0143]

[0144] The positive spinning reserve capacity constraint is transformed into:

[0145]

[0146] The negative spinning reserve capacity constraint is transformed into:

[0147]

[0148] In summary, the environmental economic dispatch model including wind farms can be expressed as:

[0149]

[0150] Where P represents the control vector composed of the output of coal-fired units; g defines q inequality constraints consisting of non-control variables.

[0151] S3. Solve the transformed environmental economic dispatch model based on the firefly-bat hybrid algorithm to obtain the optimal solution set.

[0152] S31, initializing the populations of the firefly algorithm and the bat algorithm, and setting the operation parameters of the firefly algorithm and the bat algorithm.

[0153] Specifically, the elements in the set are called Pareto optimal solutions or non-inferior solutions. They are defined as follows:

[0154] Definition 1: If decision vectors P1 and P2 are both feasible solutions, then "P1 dominates P2" (i.e., P1 < P2) if and only if the following equation holds:

[0155]

[0156] Definition 2: For any two decision vectors P1 and P2 in the decision space, "P1 constrains P2" if and only if any one of the following conditions holds (i.e., P1 < c P2):

[0157] 1) P1 is a feasible solution, and P2 is not a feasible solution;

[0158] 2) Neither P1 nor P2 is a feasible solution, and the overall constraint violation of P1 is small;

[0159] 3) P1 and P2 are both feasible solutions, and P1<P2.

[0160] S32. In each calculation iteration, the firefly algorithm and the bat algorithm are used to update their own individuals, and the updated individuals are merged into a candidate solution set, and a non-dominated sort is performed on the candidate solution set.

[0161] S321, population initialization, setting the relevant parameters of the firefly and bat algorithms, such as population size, maximum number of iterations, light intensity absorption coefficient, frequency range, pulse emission rate, loudness, etc.; randomly generate the initial population, each individual represents a solution vector P.

[0162] S322, in each iteration of the main loop, the firefly algorithm and the bat algorithm are used to update their own individuals, and then the updated individuals are grouped together:

[0163] S3221. Calculate the light intensity of each firefly according to the objective function value. For multi-objective optimization problems, use the non-dominated sorting method to determine the light intensity; for each firefly i, it is attracted by all fireflies j with higher brightness, and the position update formula is as follows:

[0164]

[0165] In the formula, β0 represents the initial attraction, γ represents the light intensity absorption coefficient, r ij represents the distance between fireflies i and j, and α represents the random perturbation factor.

[0166] S3222. Use the bat algorithm formula to update the position and speed of each bat:

[0167] f i =f min +(f max -f min )·β

[0168]

[0169] In the formula, f i represents frequency, v i represents speed, x i Indicates position, x * represents the current global optimal position, and β represents a random number (between 0 and 1).

[0170] Perform a local search to explore details of the solution space, given a preset pulse firing rate and loudness:

[0171] x new =x current +∈·A t

[0172] In the formula, ∈ represents a random number, A t Indicates the current bat loudness.

[0173] Update the loudness and pulse rate according to the following formula:

[0174]

[0175] r i t+1 =r i t ·[1-exp(-γ·t)]

[0176] In the formula, α and γ represent preset constants, r i represents the pulse emission rate.

[0177] S3223. Merge the updated firefly and bat individuals into a new candidate solution set and perform non-dominated sorting, which is divided into multiple fronts, where the solutions in each front are mutually non-dominated; in each non-dominated front, calculate the crowding distance of each individual to measure the density of individuals in the solution space to ensure the diversity of the solution set; select the next generation of population from the non-dominated front, give priority to individuals with higher non-dominated fronts, and if the number of individuals exceeds the required population size, select individuals based on the crowding distance to maintain the diversity of the population.

[0178] S33. When the number of iterations reaches a preset maximum number or the change in the iteration result is less than a change threshold, the final candidate solution set for non-dominated sorting is output as the optimal solution set.

[0179] Specifically, check whether the termination condition is met (such as reaching the maximum number of iterations or the change in the solution is less than a preset threshold). If the termination condition is met, the algorithm is terminated and the final non-dominated solution set is output as the approximate Pareto frontier of the multi-objective optimization problem, that is, the optimal solution set. Otherwise, return to step S32.

[0180] S4. Clustering the optimal solution set using a density-based clustering algorithm to divide the optimal subsets with similar characteristics, sorting the non-inferior solutions in each category using the analytic hierarchy process, and obtaining an environmental economic scheduling plan based on the sorting results.

[0181] S41. Automatically determine the number of clusters through density parameters, and cluster the optimal solution set to obtain an optimal subset.

[0182] Specifically, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is used to cluster the Pareto optimal set, and the number of clusters is automatically determined by the density parameter. In the Pareto optimal set of the environmental economic dispatch model, there are two extreme solutions, namely, the solution with the minimum power generation cost and the solution with the minimum pollution gas emission, which correspond to the operator's preference for two different goals.

[0183] S42. Determine the weight of each attribute by calculating the standard deviation of the optimal solution in the optimal solution set and the correlation between the attributes, sort each type of optimal subset using the weight of each attribute through the hierarchical analysis method, and obtain the environmental economic scheduling plan according to the sorting result.

[0184] Specifically, CRITIC (objective weighting method) is used to determine the weight of each attribute by calculating the standard deviation and the correlation between attributes, so as to objectively reflect the importance of environmental protection and economic benefits. Using the above attribute weights, the Analytic Hierarchy Process (AHP) is applied to rank each type of non-inferior solution to provide decision-making guidance for operators.

[0185] Embodiment 2

[0186] Please refer to Figure 2 A multi-objective-based environmental economic dispatch terminal 1 for an electric power system comprises a memory 2, a processor 3 and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, each step of a multi-objective-based environmental economic dispatch method for an electric power system in Example 1 is implemented.

[0187] In summary, the present invention provides a method and terminal for environmental economic dispatch of power systems based on multiple objectives. The two-parameter Weibull distribution is selected to describe the random variation law of wind speed, and the probability density function expression of wind power is obtained by combining the piecewise linear output characteristics of wind power. Based on multi-objective opportunity constrained programming, a random optimization model for environmental economic dispatch containing wind farms is established by taking into account the cost of carbon trading-green certificates, and the distribution function of wind power output is used to perform deterministic transformation on the model. The firefly-bat hybrid optimization algorithm is obtained by combining the firefly algorithm and the bat algorithm, and the Pareto optimal subset of the problem is obtained. The Pareto optimal set is clustered using a density-based clustering algorithm to divide the Pareto optimal subsets with similar characteristics, and then the analytic hierarchy process is used in each category to sort the non-inferior solutions to provide decision-making guidance for operators. The proposed new power system environmental economic dispatch model and solution based on the multi-objective firefly-bat hybrid algorithm has guiding significance for the optimal dispatch of new energy power systems containing wind farms. It can effectively reduce the operating cost of the overall system, while considering the negative impact of pollutant emissions from coal-fired units on the environment. It is a power generation dispatch strategy that takes into account both environmental protection and economic benefits. In the optimization stage, the advantages of the firefly algorithm and the bat algorithm are combined to obtain the firefly-bat hybrid optimization algorithm; in the decision-making stage, the density-based clustering algorithm and the hierarchical analysis method are comprehensively applied to fully explore the information contained in the Pareto optimal set, and assist the operator to determine the final dispatch plan.

[0188] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-objective power system environmental economic dispatch method, characterized in that: Includes steps: S1. Combining the functional relationship between the active output of the wind turbine and the wind speed with the law of wind speed variation, the cumulative probability density function of the active output of the wind turbine is calculated; S2. Based on multi-objective opportunity constrained programming, an environmental economic dispatch model including a wind farm is established taking into account the carbon trading-green certificate transaction cost, and the cumulative probability density function of the active output of the wind turbine is used to perform a deterministic transformation on the environmental economic dispatch model; S3, solving the transformed environmental economic dispatch model based on the firefly-bat hybrid algorithm to obtain the optimal solution set; S4. Clustering the optimal solution set using a density-based clustering algorithm to divide the optimal subsets with similar characteristics, sorting the non-inferior solutions in each category using a hierarchical analysis method, and obtaining an environmental economic scheduling plan based on the sorting results.

2. The method for environmental economic dispatch of a power system based on multiple objectives according to claim 1 is characterized in that: Step S1 includes: Use the two-parameter Weibull distribution to describe the random variation of wind speed: F(v)=1-exp[-(v / c) k ] And calculate the probability density function of wind speed: f(v)=(k / c)(v / c) k-1 exp[-(v / c) k ] In the formula, v represents wind speed, c represents scale parameter, and k represents shape parameter; Establish the functional relationship between the active output of the fan and the wind speed: Where P WT Indicates the active output of the fan, P rate Indicates the rated power of the fan, v in 、v rate 、v out They represent the cut-in wind speed, rated wind speed and cut-out wind speed respectively; Combining the random variation law of the wind speed, the probability density function, and the functional relationship between the active output of the wind turbine and the wind speed, the cumulative probability density function of the active output of the wind turbine is calculated:

3. A method for environmental economic dispatch of a power system based on multiple objectives according to claim 2, characterized in that: Step S2 includes: S21. Establish a carbon trading-green certificate transaction cost model; S22. Based on multi-objective opportunity constrained programming, the objective function and constraint conditions of the environmental economic dispatch model including the wind farm are established in combination with the carbon trading-green certificate transaction cost model; S23. Deterministically transform the environmental economic dispatch model using the cumulative probability density function of the wind turbine active output.

4. A method for environmental economic dispatch of a power system based on multiple objectives according to claim 3, characterized in that: Step S21 includes: Establishing carbon trading costs F c : In the formula, represents the carbon trading price, E c Represents the carbon emissions of thermal power units, E q1 Indicates the free carbon emission quota of thermal power units, E q2 It means free carbon emission quota for wind farms; Establish green certificate transaction cost F S : F S =P TCG (kP D -P W ) Where P TCG represents the green certificate transaction price, k represents the quota ratio of renewable energy power generation to grid-connected power, P D Represents the power system load, P W Indicates wind power output.

5. A method for environmental economic dispatch of a power system based on multiple objectives according to claim 4, characterized in that: Step S22 includes: Establish the objective function of the environmental economic dispatch model including wind farms: min[F,E] F=F C +F S +F G Where F represents the operating cost of the power system, F G represents the power generation cost of the power system, P i represents the active output of coal-fired unit i, F Gi (P i ) represents the consumption characteristics of coal-fired unit i; E represents the emission of pollutant gas, α i , β i , γ i , i , i represents the pollutant gas emission coefficient of coal-fired unit i; Constraints for establishing an environmental economic dispatch model with wind farms: Establish power balance constraints: Where P L represents the network loss, η1 represents the first confidence level of meeting the load demand; Establish a positive spinning reserve capacity constraint: Where P imax represents the upper limit of active output of coal-fired unit i, U SR represents the backup requirement of the conventional system, w u represents the demand coefficient of wind farm output for positive spinning reserve, η2 represents the second confidence level for satisfying the positive spinning reserve capacity constraint; Create a negative spinning reserve capacity constraint: Where: P Wmax Indicates the rated output of the wind farm, w d represents the demand coefficient of wind farm output for negative spinning reserve, η3 represents the third confidence level for satisfying the negative spinning reserve capacity constraint; Establish output constraints for coal-fired units and wind farms.

6. A method for environmental economic dispatch of a power system based on multiple objectives according to claim 5, characterized in that: Step S23 includes: According to the cumulative probability density function of the wind turbine active output, the power balance constraint is transformed into: The positive spinning reserve capacity constraint is transformed into: The negative spinning reserve capacity constraint is transformed into:

7. The method for environmental economic dispatch of a power system based on multiple objectives according to claim 1, characterized in that: Step S3 includes: Initialize the populations of the firefly algorithm and the bat algorithm, and set the operation parameters of the firefly algorithm and the bat algorithm; In each calculation iteration, the firefly algorithm and the bat algorithm are used to update their own individuals, and the updated individuals are merged into a candidate solution set, and the candidate solution set is non-dominated sorted; When the number of iterations reaches the preset maximum number or the change in the iteration result is less than the change threshold, the final candidate solution set for non-dominated sorting is output as the optimal solution set.

8. The method for environmental economic dispatch of a power system based on multiple objectives according to claim 1, characterized in that: Step S4 includes: S41, automatically determining the number of clusters by density parameters, and clustering the optimal solution set to obtain an optimal subset; S42. Determine the weight of each attribute by calculating the standard deviation of the optimal solution in the optimal solution set and the correlation between the attributes, sort each type of optimal subset using the weight of each attribute through the hierarchical analysis method, and obtain the environmental economic scheduling plan according to the sorting result.

9. A multi-objective-based power system environmental economic dispatch terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for environmental-economic dispatch of a power system based on multiple objectives as described in any one of claims 1 to 8 is implemented.

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