A multi-objective-based power system environment economic dispatch method and terminal

Abstract

The application discloses a kind of based on multi-objective power system environmental economic dispatching method and terminal, based on multi-objective firefly bat hybrid algorithm power system environmental economic dispatching method has guiding significance to the optimal dispatching of new energy power system containing wind farm, can effectively reduce the operating cost of overall system, improve robustness;While establishing the environmental economic dispatching model considering carbon trading and green certificate trading cost, can consider the negative influence caused to environment by pollutant emission of coal-fired unit, it is a kind of generation dispatching strategy giving consideration to environmental protection and economic benefit;Based on firefly bat hybrid algorithm, environmental economic dispatching model is solved to obtain optimal solution set, and density-based clustering algorithm and analytic hierarchy process are comprehensively applied to fully mine the information contained in optimal solution set, to assist operating personnel to determine suitable dispatching scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatch automation, and particularly relates to a multi-target-based power system environment economic dispatch method and terminal. BACKGROUND

[0002] In a new power system, wind energy, as a renewable green energy, has great significance for alleviating the energy crisis worldwide. Large-scale wind power grid connection is the main mode of developing and utilizing wind energy at present, and is an important part of building a strong smart grid. Unlike the characteristics of small wind farms as distributed power sources connected to distribution networks and consumed locally, large-scale wind power needs to be transmitted over long distances to load centers through transmission channels. The randomness and intermittency of wind speed directly affect the active power output of the wind farm, adding new uncertainty factors to the economic dispatch of traditional power systems.

[0003] In terms of models, traditional generation dispatch models all take economic benefits as a single optimization target and do not consider the negative impact of pollutant emissions of coal-fired units on the environment. With the increasingly serious global environmental pollution problem, it has become a consensus to increase the energy-saving and emission-reducing efforts of the power industry. Under this background, the generation dispatch strategy that takes into account both environmental protection and economic benefits, i.e., environment economic dispatch, has received widespread attention. In addition, there is no optimal solution for the multi-objective optimization problem that makes economic efficiency and environmental protection optimal at the same time, which is not conducive to the practical application of operating personnel.

[0004] Therefore, there are currently some optimization dispatch schemes suitable for new power systems, but they basically only consider maximizing economic benefits and do not consider the negative impact of pollutant emissions of coal-fired units on the environment, and the use of multi-objective optimization algorithms is time-consuming and has insufficient robustness. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a multi-target-based power system environment economic dispatch method and terminal, which can effectively reduce the overall system calculation cost, improve robustness, and provide a reference basis for the trade-off between environmental protection and economic efficiency to assist operating personnel in determining the final dispatch scheme.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is:

[0007] A multi-target-based power system environment economic dispatch method, comprising the steps of:

[0008] S1, combining the functional relationship between wind turbine active power output and wind speed with the wind speed variation law to calculate the cumulative probability density function of wind turbine active power output;

[0009] S2, based on multi-objective chance-constrained programming, an environmental and economic dispatching model of a wind farm is established by taking into account the carbon trading and green certificate trading costs, and the cumulative probability density function of the active power output of the wind turbine is used to determine the environmental and economic dispatching model;

[0010] S3, the transformed environmental and economic dispatching model is solved based on a firefly-bat hybrid algorithm to obtain an optimal solution set;

[0011] S4, the optimal solution set is clustered using a density-based clustering algorithm to divide the optimal subsets with similar characteristics, and the non-inferior solutions in each class are sorted using an analytic hierarchy process, and an environmental and economic dispatching scheme is obtained according to the sorting result.

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

[0013] A multi-objective-based environmental and economic dispatching terminal for a power system comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements each step of the multi-objective-based environmental and economic dispatching method for a power system.

[0014] The present application has the advantages that: the cumulative probability density function of the active power output can be calculated by combining the wind speed variation law and the function of the active power output of the wind turbine; then, based on multi-objective chance-constrained programming, an environmental and economic dispatching model is established by taking into account the carbon trading and green certificate trading costs; the environmental and economic dispatching model is solved based on a firefly-bat hybrid algorithm to obtain an optimal solution set, and then the optimal solution set is clustered using a density-based clustering algorithm to obtain optimal subsets, and an environmental and economic dispatching scheme is obtained by sorting the non-inferior solutions in each optimal subset using an analytic hierarchy process. In this way, the multi-objective-based firefly-bat hybrid algorithm for the environmental and economic dispatching of a power system has a guiding significance for the optimal dispatching of a new energy power system containing a wind farm, can effectively reduce the operating cost of the overall system, improve the robustness, and take into account the negative impact of pollutant emissions of coal-fired units on the environment, and is a power dispatching strategy that takes into account environmental protection and economic benefits; the density-based clustering algorithm and the analytic hierarchy process are comprehensively applied to fully exploit the information contained in the optimal solution set to assist the operating personnel in determining a suitable dispatching scheme. BRIEF DESCRIPTION OF DRAWINGS

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

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

[0017] Label Description:

[0018] 1. A multi-objective-based power system environmental and economic dispatch terminal; 2. a memory; 3. a processor. DETAILED DESCRIPTION

[0019] To illustrate the technical content of the present application, the purposes and effects achieved are described in detail below in conjunction with the embodiments and the accompanying drawings.

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

[0021] S1, the function relationship between the active power output of the wind turbine and the wind speed is combined with the wind speed variation law to calculate the cumulative probability density function of the active power output of the wind turbine;

[0022] S2, based on multi-objective chance-constrained programming, an environmental and economic dispatch model containing a wind farm is established by taking into account the carbon trading-green certificate trading cost, and the cumulative probability density function of the active power output of the wind turbine is used to determine the environmental and economic dispatch model;

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

[0024] S4, the optimal solution set is clustered using a density-based clustering algorithm to divide an optimal subset with similar characteristics, and the non-inferior solutions in each class are sorted using the analytic hierarchy process, and an environmental and economic dispatch scheme is obtained according to the sorting result.

[0025] From the above description, the beneficial effects of the present application are that by combining the wind speed variation law and the function of the active power output of the wind turbine, the cumulative probability density function of the active power output can be calculated; then based on multi-objective chance-constrained programming, an environmental and economic dispatch model considering the carbon trading and green certificate trading cost is established; based on the firefly-bat hybrid algorithm, the environmental and economic dispatch model is solved to obtain an optimal solution set, and then the optimal solution set is clustered using a density-based clustering algorithm to obtain an optimal subset, and by using the analytic hierarchy process to sort the non-inferior solutions in each optimal subset, an environmental and economic dispatch scheme is obtained. In this way, the multi-objective-based firefly-bat hybrid algorithm power system environmental and economic dispatch method has guiding significance for the optimal dispatching of new energy power systems containing wind farms, can effectively reduce the operating cost of the overall system, improve the robustness, and at the same time consider the negative impact of pollutant emissions of coal-fired units on the environment, is a power dispatching strategy that takes into account environmental protection and economic benefits; the density-based clustering algorithm and the analytic hierarchy process are comprehensively applied to fully exploit the information contained in the optimal solution set to assist operating personnel in determining a suitable dispatching scheme.

[0026] Further, the step S1 comprises:

[0027] The random variation law of the wind speed is described using a two-parameter Weibull distribution:

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

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

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

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

[0032] A function relationship between the active power of the wind turbine and the wind speed is established:

[0033]

[0034] In the formula, P WT represents the active power of the wind turbine, P rate represents the rated power of the wind turbine, v in , v rate , and v out respectively represent the cut-in wind speed, the rated wind speed, and the cut-out wind speed;

[0035] Combined with the random variation law, the probability density function of the wind speed, and the function relationship between the active power of the wind turbine and the wind speed, the cumulative probability density function of the active power of the wind turbine is calculated:

[0036]

[0037] As can be known from the above description, by calculating the cumulative probability density function of the active power of the wind turbine, the dependence relationship between the active power of the wind turbine and the wind speed can be more deeply obtained, and this is helpful for subsequent establishment of a dispatching model.

[0038] Further, the step S2 comprises:

[0039] S21, establishing a carbon transaction-green certificate transaction cost model;

[0040] S22, based on a multi-objective chance-constrained programming, combining the carbon transaction-green certificate transaction cost model to establish a target function and a constraint condition of an environmental and economic dispatching model containing a wind farm;

[0041] S23, using the cumulative probability density function of the active power of the wind turbine to perform deterministic transformation on the environmental and economic dispatching model.

[0042] From the above description, when establishing the environment economic dispatching model containing the wind power plant, the carbon transaction-green certificate transaction cost is considered, the surplus carbon emission quota or green certificate can be converted into actual income through carbon transaction and green certificate transaction, so as to reduce the overall operation cost; and the cumulative probability density function of the active power output of the wind turbine is used to determine the environment economic dispatching model, which can effectively reduce the calculation cost of the overall system and improve the robustness.

[0043] Further, step S21 comprises:

[0044] The carbon transaction cost F c is established.

[0045]

[0046] In the formula, P represents the carbon transaction price, E c represents the carbon emission of the thermal power unit, E q1 represents the free carbon emission quota of the thermal power unit, E q2 represents the free carbon emission quota of the wind power plant.

[0047] The green certificate transaction cost F S is established.

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

[0049] In the formula, P TCG represents the green certificate transaction price, k represents the quota ratio of renewable energy power generation to on-grid power, P D represents the load of the power system, and P W represents the wind power output.

[0050] From the above description, the carbon transaction cost of the power system is obtained based on the principle of the carbon transaction mechanism, and the green certificate transaction cost is established, so as to consider the negative impact of pollutant emission on the environment in the subsequent process.

[0051] Further, step S22 comprises:

[0052] The objective function of the environment economic dispatching model containing the wind power plant is established.

[0053] min[F,E]

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

[0055]

[0056] where F represents the operating cost of the power system, F G represents the generation cost of the power system, P i represents the active power output of the coal-fired unit i, F Gi (P i ) represents the consumption characteristic of the coal-fired unit i; E represents the pollution gas emission, α i , β i , γ i , ζ i , λ i represents the pollution gas emission coefficient of the coal-fired unit i;

[0057] The constraint condition for establishing the environmental and economic dispatching model containing the wind power plant is:

[0058] The power balance constraint is established:

[0059]

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

[0061] The positive spinning reserve capacity constraint is established:

[0062]

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

[0064] The negative spinning reserve capacity constraint is established:

[0065]

[0066] where: P Wmax represents the rated output of the wind power plant, w d represents the demand coefficient of the wind power plant output on the negative spinning reserve, η3 represents the third confidence level for meeting the negative spinning reserve capacity constraint;

[0067] The coal-fired unit output constraint and the wind power plant output constraint are established.

[0068] It can be known from the above description that the objective function is set with the minimum operation cost and the minimum pollution gas emission of the power system as the target, power balance constraints, spinning reserve capacity constraints and unit output constraints are set, the operation cost of the overall system can be effectively reduced, and the negative influence of the pollution emission on the environment is considered so as to obtain a suitable environmental and economic dispatching scheme.

[0069] Further, the step S23 comprises:

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

[0071]

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

[0073]

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

[0075]

[0076] It can be known from the above description that the power balance constraint and the positive and negative spinning reserve capacity constraints all contain the random variable P W , which is not easy to solve in the form of probability, therefore, according to the cumulative probability density function of the active power output of the wind turbine, the above constraints are determined and converted, and the operation cost can be saved.

[0077] Further, the step S3 comprises:

[0078] The population of the glowworm swarm optimization algorithm and the bat algorithm is initialized, and the operation parameters of the glowworm swarm optimization algorithm and the bat algorithm are set;

[0079] In each calculation iteration, the individuals of the glowworm swarm optimization algorithm and the bat algorithm are updated respectively, and the updated individuals are combined into a candidate solution set, and the candidate solution set is non-dominantly sorted;

[0080] When the number of iterations reaches a preset maximum number or the change of the iteration result is less than a change threshold, the final candidate solution set which is non-dominantly sorted is output as an optimal solution set.

[0081] It can be known from the above description that the glowworm bat hybrid optimization algorithm is obtained by combining the glowworm swarm optimization algorithm and the bat algorithm, and the optimal solution set is obtained accordingly, the advantages of the glowworm swarm optimization algorithm and the bat algorithm can be comprehensively utilized for solving, and the solving accuracy can be improved.

[0082] Further, the step S4 comprises:

[0083] S41, automatically determine the number of clusters through a density parameter, and cluster 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 attributes, sort each optimal subset of the optimal subset through the analytic hierarchy process using the weight of each attribute, and obtain an environmental economic dispatching scheme according to the sorting result.

[0085] From the above description, it can be known that the comprehensive application of the density-based clustering algorithm and the analytic hierarchy process can fully mine the information contained in each optimal subset, so as to assist the operation personnel to determine a suitable dispatching scheme.

[0086] Please refer to Figure 2 Another embodiment of the present application provides a multi-target-based environmental and economic dispatching terminal of a power system, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and each step of the above-mentioned multi-target-based environmental and economic dispatching method of a power system is realized when the processor executes the computer program.

[0087] The above-mentioned multi-target-based environmental and economic dispatching method and terminal of a power system are suitable for new energy power system, can effectively reduce the calculation cost of the whole system, improve the robustness, and provide a reference basis for the trade-off between environmental protection and economy, and assist the operation personnel to determine the final dispatching scheme. The following will be described through specific embodiments:

[0088] Embodiment one

[0089] Please refer to Figure 1 A multi-target-based environmental and economic dispatching method of a power system comprises the following steps:

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

[0091] S11, the random variation law of the wind speed is described by using a two-parameter Weibull distribution:

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

[0093] and the probability density function of the wind speed is calculated:

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

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

[0096] S12, a function relationship between the active power of the fan and the wind speed is established:

[0097]

[0098] In the formula, P WT represents the active power of the fan, P rate represents the rated power of the fan, v in , v rate , and v out respectively represent the cut-in wind speed, the rated wind speed, and the cut-out wind speed; wherein, P WT is a mixed random variable, that is, continuous in the interval (0, P rate ), and discrete at 0 and P rate .

[0099] According to the random variation law of the wind speed, the probability density function, and the function relationship between the active power of the fan and the wind speed, the cumulative probability density function of the active power of the fan is calculated:

[0100]

[0101] S2, based on multi-objective chance-constrained programming, an environmental and economic dispatching model of a wind farm is established by considering the carbon trading-green certificate trading cost, and the environmental and economic dispatching model is deterministically transformed by using the cumulative probability density function of the active power of the fan.

[0102] S21, a carbon trading-green certificate trading cost model is established:

[0103] S211, when the carbon emission of the thermal power plant is greater than the free carbon emission quota, the carbon emission quota needs to be purchased in the carbon trading market; otherwise, the excess free carbon emission quota can be sold in the carbon trading market to obtain certain benefits. According to the principle of carbon trading mechanism, the carbon trading cost F c is:

[0104]

[0105] In the formula, C represents the carbon trading price, E c represents the carbon emission of the thermal power unit, E q1 represents the free carbon emission quota of the thermal power unit, and E q2 represents the free carbon emission quota of the wind farm.

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

[0107] Because of the randomness of wind power and photovoltaic power, when wind power and photovoltaic power are connected to the power grid, in order to ensure the safety of the power grid, the spinning reserve capacity of the thermal power unit needs to be increased, which increases the operating cost of the thermal power unit, so the carbon emission quota of the thermal power unit should be compensated, and the free carbon emission quota E of the thermal power unit is q1 For:

[0108]

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

[0110] The free carbon emission quota E of the wind farm is q2 E q2 = δP W

[0111] Because wind power is a clean energy and does not produce CO2, the emission of CO2 is considered to come from thermal power units, and the carbon emission of the thermal power unit is proportional to the active power output, which can be represented as:

[0112]

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

[0114] S212, green certificate is the proof of green attribute of renewable energy power, and green certificate trading mechanism is a supporting measure to ensure the effective implementation of renewable energy quota system. The government agency certifies specific renewable energy power and issues tradable certificates with renewable energy power identification. When the actual renewable energy consumption is greater than the green certificate quota ratio, the green certificate generated by the excess renewable energy power can be sold to obtain benefits; when the actual renewable energy consumption is less than the green certificate quota ratio, the insufficient renewable energy consumption needs to purchase green certificates. Therefore, the green certificate trading cost F S is established:

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

[0116] In the formula, P TCG represents the green certificate trading price, k represents the quota ratio of renewable energy power to on-grid power, and P DP represents the load of the power system W P represents the wind power output.

[0117] S22, based on the multi-objective chance-constrained programming, combining the carbon trading-green certificate trading cost model, the objective function and constraint condition of the environmental economic dispatching model containing wind farms are established:

[0118] S221, the objective function of the environmental economic dispatching model containing wind farms is established:

[0119] min[F,E]

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

[0121]

[0122] In the formula, F represents the operation cost of the power system, and the unit is MW; F G represents the generation cost of the power system; P i represents the active power output of the coal-fired unit i; F Gi (P i ) represents the consumption characteristic of the coal-fired unit i; E represents the pollution gas emission; α i , β i , γ i , ζ i , λ i represent the pollution gas emission coefficient of the coal-fired unit i, which can be obtained by fitting method according to the harmful gas emission detection data of the power plant.

[0123] Wherein, the consumption characteristic of the coal-fired unit considering the valve point effect is:

[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 all represent the generation cost coefficient; P imin represents the lower limit of the active power output of the coal-fired unit i, and the unit is MW.

[0126] S222, establish constraint conditions of the environmental and economic dispatching model containing the wind farm:

[0127] Establish power balance constraint:

[0128]

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

[0130]

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

[0132] Establish positive rotating reserve capacity constraint:

[0133]

[0134] In the formula, P imax represents the upper limit of active power output of the coal-fired unit i, U SR represents the reserve requirement of the conventional system, w u represents the demand coefficient of wind farm output on positive rotating reserve, and η2 represents the second confidence level meeting the positive rotating reserve capacity constraint;

[0135] Establish negative rotating reserve capacity constraint:

[0136]

[0137] In the formula: P Wmax represents the rated output of the wind farm, w d represents the demand coefficient of wind farm output on negative rotating reserve, and η3 represents the third confidence level meeting the negative rotating reserve capacity constraint;

[0138] Establish coal-fired unit output constraint: P imin ≤ P i ≤ P imax .

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

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

[0141] Since both the power balance constraint and the positive and negative spinning reserve capacity constraint contain random variable P W which is not easy to solve in the form of probability, so the established stochastic model needs to be converted into a deterministic optimization model for solving.

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

[0143]

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

[0145]

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

[0147]

[0148] In summary, the environmental and economic dispatching model containing a wind farm can be expressed as:

[0149]

[0150] In the formula, P represents the control vector of the output composition of the coal-fired unit; g defines q inequality constraints composed of non-control variables.

[0151] S3, based on the firefly-bat hybrid algorithm, the environmental and economic dispatching model is solved to obtain the optimal solution set.

[0152] S31, the population of the firefly algorithm and the bat algorithm is initialized, and the operation parameters of the firefly algorithm and the bat algorithm are set.

[0153] Specifically, the elements in the set are called Pareto optimal solutions or non-inferior solutions. There are the following definitions:

[0154] Definition 1: If decision vectors P1 and P2 are both feasible solutions, then "P1 dominates P2" (i.e. P1

[0155]

[0156] Definition 2: For any two decision vectors P1 and P2 in the decision space, "P1 constraint dominates P2" (i.e. P1 c P2) when and only when any of the following conditions is met:

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

[0158] 2) P1 and P2 are both not feasible solutions, and the overall constraint violation degree of P1 is smaller.

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

[0160] S32, update each individual using the glowworm algorithm and the bat algorithm respectively at each calculation iteration, and combine the updated individuals into a candidate solution set, and perform non-dominated sorting on the candidate solution set.

[0161] S321, population initialization, set the related parameters of glowworm and bat algorithm, such as population size, maximum iteration number, light intensity absorption coefficient, frequency range, pulse emission rate, loudness, etc. Randomly generate initial population, each individual represents a solution vector P.

[0162] S322, in each iteration of the main loop, update each individual using the glowworm algorithm and the bat algorithm respectively, and then combine the updated individuals together:

[0163] S3221, calculate the light intensity of each glowworm according to the objective function value. For multi-objective optimization problem, use non-dominated sorting method to determine the light intensity; for each glowworm i it is attracted by all brighter glowworms j, the position update formula is as follows:

[0164]

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

[0166] S3222, update the position and speed of each bat using the formula of bat algorithm:

[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 represents position, x * represents the current global optimal position, and β represents a random number (0 to 1).

[0170] According to the preset pulse emission rate and loudness, perform local search to explore the details of the solution space:

[0171] x new = x current + ∈·A t

[0172] wherein ∈ represents a random number, A t represents the loudness of the current bat.

[0173] The loudness and the pulse emission rate are updated according to the following formula:

[0174]

[0175] r i t+1 = r i t · [1 - exp (-γ·t)]

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

[0177] S3223, merge the updated fireflies and bats into a new candidate solution set and perform non-dominated sorting, and divide into multiple frontiers, wherein the solutions in each frontier are non-dominated with each other; in each non-dominated frontier, the crowding distance of each individual is calculated, thereby measuring the density of the individual in the solution space, to ensure the diversity of the solution set; select the next generation population from the non-dominated frontiers, preferentially select individuals in the higher non-dominated frontiers, and if the number of individuals exceeds the required population size, select individuals according to 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 of the iteration result is less than a change threshold, output the final candidate solution set subjected to non-dominated sorting as the optimal solution set.

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

[0180] S4, use a density-based clustering algorithm to cluster the optimal solution set, divide out optimal subsets with similar characteristics, use the analytic hierarchy process to sort the non-inferior solutions in each class, and obtain the environmental and economic dispatching scheme according to the sorting result.

[0181] S41, automatically determine the number of clusters through a density parameter, and cluster the optimal solution set to obtain optimal subsets.

[0182] Specifically, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to cluster the Pareto optimal set, and the number of clusters is automatically determined by a density parameter. In the Pareto optimal set of the environmental economic dispatch model, there are two extreme solutions, which are the solutions of minimum generation cost and minimum pollution gas emission, corresponding to the preferences of the operating personnel for two different objectives.

[0183] In S42, the weights of the attributes are determined by calculating the standard deviation and the correlation between the attributes of the optimal solutions in the optimal solution set, and each class of optimal subset is sorted by using the weights of the attributes by the analytic hierarchy process (AHP), and an environmental economic dispatch scheme is obtained according to a sorting result.

[0184] Specifically, the CRITIC (CRITIC, Critical to Importance) is used to determine the weights of the attributes by calculating the standard deviation and the correlation between the attributes, so as to objectively reflect the importance of environmental protection and economic benefit. The above attribute weights are used, and the analytic hierarchy process (AHP) is applied to sort each class of non-inferior solution, thereby providing decision guidance for the operating personnel.

[0185] Embodiment Two

[0186] Please refer to Figure 2 A terminal 1 for environmental economic dispatch of a power system based on multiple objectives, comprising a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3, wherein the processor 3 implements each step of the method for environmental economic dispatch of a power system based on multiple objectives in embodiment one when executing the computer program.

[0187] In summary, the application provides a kind of power system environmental economic dispatching method and terminal based on multi-objective, selects two-parameter Weibull distribution to describe the random variation law of wind speed, obtains the expression of wind power probability density function by combining the piecewise linear output characteristics of wind power, establishes the environmental economic dispatching stochastic optimization model of wind farm based on multi-objective chance constrained programming, and utilizes the distribution function of wind power output to transform the model into certainty. The firefly bat hybrid optimization algorithm is obtained by combining firefly algorithm and bat algorithm, and the Pareto optimal subset of the problem is obtained. The density-based clustering algorithm is used to cluster the Pareto optimal set, and the Pareto optimal subset with similar characteristics is divided. Then, the analytic hierarchy process is applied in each class to sort the non-inferior solution, which provides decision guidance for the operator. The new power system environmental economic dispatching model and solution based on multi-objective firefly bat hybrid algorithm have guiding significance for the optimization dispatching of new energy power system containing wind farm, can effectively reduce the operation cost of the whole system, and consider the negative impact of pollutant emission of coal-fired units on the environment, which is a power generation dispatching strategy considering environmental protection and economic benefit. In the optimization stage, the advantages of firefly algorithm and bat algorithm are combined to obtain the firefly bat hybrid optimization algorithm. In the decision-making stage, the density-based clustering algorithm and the analytic hierarchy process are comprehensively applied to fully tap the information contained in the Pareto optimal set, which helps the operator to determine the final dispatching scheme.

[0188] The above is only an embodiment of the application, and does not limit the patent scope of the application, and any equivalent transformation or direct or indirect application in related technical fields based on the content of the application specification and drawings is also included in the patent protection scope of the application.

Claims

1. A multi-objective-based environmental economic dispatch method for power systems, characterized in that, Including the following steps: S1. Combine the functional relationship between the active power output of the wind turbine and the wind speed with the wind speed variation law to calculate the cumulative probability density function of the active power output of the wind turbine. S2. Based on multi-objective opportunity-constrained programming, an environmental economic dispatch model including wind farms is established, taking into account the carbon trading-green certificate trading costs. The environmental economic dispatch model is then transformed deterministically using the cumulative probability density function of the active power output of the wind turbines. S3. Solve the transformed environmental economic scheduling model based on the firefly-bat hybrid algorithm to obtain the optimal solution set; S4. Use a density-based clustering algorithm to cluster the optimal solution set, divide it into optimal subsets with similar characteristics, use the analytic hierarchy process (AHP) to sort the non-dominated solutions in each cluster, and obtain the environmental economic scheduling scheme based on the sorting results. Step S1 includes: The two-parameter Weibull distribution is used to describe the random variation of wind speed: The probability density function of wind speed was then calculated. In the formula, v Indicates wind speed. c Indicates the scale parameter. k Indicates shape parameters; Establish the functional relationship between the active power output of the wind turbine and the wind speed: In the formula, P WT This indicates that the wind turbine has generated power. P rate Indicates the rated power of the fan. v in , v rate , v out These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. Based on the random variation pattern of wind speed, probability density function, and the functional relationship between the active power output of the wind turbine and wind speed, the cumulative probability density function of the active power output of the wind turbine is calculated as follows: ； Step S2 includes: S21. Establish a carbon trading-green certificate trading cost model; S22. Based on multi-objective opportunity-constrained programming, and in conjunction with the carbon trading-green certificate trading cost model, establish the objective function and constraints of the environmental economic dispatch model including wind farms; S23. The environmental economic dispatch model is deterministically transformed using the cumulative probability density function of the active power output of the wind turbine. Step S3 includes: Initialize the populations for the Firefly Algorithm and the Bat Algorithm, and set the computational parameters for the Firefly Algorithm and the Bat Algorithm; During each computational iteration, the individual is updated using the firefly algorithm and the bat algorithm respectively, and the updated individuals are merged into a candidate solution set, which is then sorted non-dominated. 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. Step S4 includes: S41. The number of clusters is automatically determined by the density parameter, and the optimal solution set is clustered to obtain the optimal subset; S42. The weight of each attribute is determined by calculating the standard deviation of the optimal solution in the optimal solution set and the correlation between attributes. The weight of each attribute is used to rank each optimal subset by the analytic hierarchy process. The environmental economic scheduling scheme is obtained based on the ranking result.

2. The multi-objective power system environmental economic dispatch method according to claim 1, characterized in that, Step S21 includes: Establish carbon trading costs F c : In the formula, Indicates the carbon trading price, E c This indicates the carbon emissions of thermal power units. E q1 This indicates that thermal power units receive free carbon emission allowances. E q2 This indicates that the wind farm has free carbon emission allowances. Establish green certificate transaction costs F S : In the formula, P TCG Indicates the price of green certificates. k This indicates the proportion of renewable energy generation in the total electricity supplied to the grid. P D Indicates the load of the power system. P W This indicates wind power output.

3. The multi-objective power system environmental economic dispatch method according to claim 2, characterized in that, Step S22 includes: The objective function for establishing an environmental economic dispatch model including wind farms is: In the formula, F This represents the operating cost of the power system. F G Indicates the cost of electricity generation in the power system. P i Indicates coal-fired power unit i Those who have made contributions F Gi ( P i ) indicates coal-fired power unit i Consumption characteristics; E Indicates the amount of pollutant gas emitted. α i , β i , γ i , ζ i , λ i Indicates coal-fired power unit i The pollutant gas emission coefficient; Constraints for establishing an environmental economic dispatch model including wind farms: Establish power balance constraints: In the formula, P L Indicates network loss. η 1 represents the first confidence level for meeting load requirements; Establish positive spinning reserve capacity constraints: In the formula, P imax Indicates coal-fired power unit i The upper limit of effective output, U SR This indicates the backup requirements of the conventional system. w u This represents the demand coefficient for wind farm output relative to its positive rotation reserve. η 2 indicates the second confidence level that satisfies the positive spinning reserve capacity constraint; Establish negative spin-off reserve capacity constraints: In the formula: P Wmax This indicates the rated output of the wind farm. w d This represents the demand factor for negative spinning reserve of wind farm output. η 3 indicates the third confidence level for satisfying the negative spinning reserve capacity constraint; Establish output constraints for coal-fired power units and wind farms.

4. The multi-objective power system environmental economic dispatch method according to claim 3, characterized in that, Step S23 includes: Based on the cumulative probability density function of the active power output of the wind turbine, the power balance constraint is transformed into: Transform the positive spinning reserve capacity constraint into: Transform the negative spin-off reserve capacity constraint into: 。 5. A multi-objective 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, it implements the multi-objective power system environmental economic dispatch method according to any one of claims 1 to 4.

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