A Multi-Regional Power System Economic Environment Dispatch Method and System Based on Two-Level Programming

Abstract

This invention proposes a method and system for economic and environmental dispatching of multi-regional power systems based on bi-level programming. With the goal of minimizing the power generation cost of generator sets and minimizing the emission of pollutants, the regional economic and environmental dispatching problem is transformed into bi-level programming, thereby reducing the difficulty of solving the multi-regional economic emission dispatching problem and obtaining a more flexible dispatching strategy.

Description

Technical Field

[0001] This invention belongs to the technical field of power grid dispatching, and in particular relates to a method and system for economic and environmental dispatching of multi-regional power systems based on two-level planning. Background Technology

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

[0003] With the expansion of the power system and the dispersion of load centers, the establishment of a multi-regional power system interconnected with multiple load centers is crucial for the safe and stable operation of the power system. Many countries have granted the power industry more autonomy in its operations, which has effectively contributed to economic operation and emission reduction. Based on these two factors, economic dispatch of multi-regional power systems can provide the power system with higher stability and economy. As the power industry emits the most polluting gases, how to balance environmental and economic benefits while ensuring that the power supply quality is not affected is a major technical problem worthy of research and a key aspect of promoting energy conservation and emission reduction in power enterprises. This problem has been preliminarily studied, and its main research ideas are: (1) simultaneously considering the power generation cost and polluting gas emission of thermal power units, (2) considering different constraints in the multi-regional power generation and dispatch process, and (3) selecting appropriate algorithms for analysis and solution based on meeting the dispatch time requirements.

[0004] However, the expansion of the power grid and the decentralization of load centers inevitably increase the dimensionality of the economic emission dispatch problem in multi-regional power systems, thus consuming significant computational resources and time. Therefore, how to make the optimal dispatch strategy more suitable for the economic emission dispatch problem in multi-regional power systems, and how to improve the convergence speed and accuracy of the optimization algorithm while minimizing operation time, are pressing issues that need to be addressed. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a multi-regional power system economic and environmental dispatching method and system based on bi-level programming. With the goal of minimizing the power generation cost of generator sets and minimizing the emission of pollutants, the regional economic and environmental dispatching problem is transformed into bi-level programming, thereby reducing the difficulty of solving the multi-regional economic emission dispatching problem and obtaining a more flexible dispatching strategy.

[0006] To achieve the above objectives, a first aspect of the present invention provides a multi-regional power system economic environment dispatching method based on two-level planning, comprising:

[0007] Step S101: Based on the operating parameters of the multi-regional power system, establish an upper-level planning agent model that minimizes power generation costs and environmental pollution costs with different planned total outputs in multiple regions as independent variables, and establish a lower-level planning objective with the goal of minimizing regional power generation costs and minimizing regional environmental pollution costs.

[0008] Step S102: Using a game theory algorithm, under the upper-level constraints corresponding to the upper-level planning agent model, solve the upper-level planning agent model to obtain the transmission power corresponding to the optimal value between different regions, and determine the total power generation corresponding to each region.

[0009] Step S103: Using an optimization algorithm, under the lower-level constraints corresponding to the lower-level planning objectives and the total power generation corresponding to each region, solve for the Pareto front data of each region;

[0010] Step S104: Make decisions based on the Pareto front data of each region to obtain the optimal scheduling strategy.

[0011] A second aspect of the present invention provides an economical dispatch system for multi-regional power systems based on two-level planning, comprising:

[0012] A two-layer planning module is established: based on the operating parameters of the multi-regional power system, an upper-level planning proxy model is established with different planned total outputs in multiple regions as independent variables to minimize power generation costs and environmental pollution costs, and a lower-level planning objective is established with minimizing regional power generation costs and minimizing regional environmental pollution costs as objectives.

[0013] Solving the upper-level planning module: Using a game theory algorithm, under the upper-level constraints corresponding to the upper-level planning proxy model, the upper-level planning proxy model is solved to obtain the transmission power corresponding to the optimal value between different regions, and to determine the total power generation corresponding to each region;

[0014] Solving the lower-level planning module: Using an optimization algorithm, under the lower-level constraints corresponding to the lower-level planning objectives and the total power generation corresponding to each region, the Pareto front data for each region is obtained;

[0015] Scheduling output module: Makes decisions based on the Pareto front data of each region to obtain the optimal scheduling strategy.

[0016] A third aspect of the present invention provides a computer device comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, a multi-regional power system economic environment dispatching method based on two-level planning is executed.

[0017] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs an economic environmental dispatching method for a multi-regional power system based on a two-level planning approach.

[0018] The above one or more technical solutions have the following beneficial effects:

[0019] In this invention, the regional economic and environmental scheduling problem is transformed into a two-level programming problem with the goal of minimizing the power generation cost of generator sets and minimizing the emission of pollutants. This reduces the difficulty of solving the multi-regional economic emission scheduling problem and allows for more flexible scheduling strategies.

[0020] In this invention, a game theory algorithm is used to solve the upper-level planning proxy model, which overcomes the contradiction between local search and global optimization that was difficult to balance in past optimization algorithms. The proposed algorithm has significantly improved the convergence degree and convergence speed compared with previous evolutionary algorithms, and can quickly and accurately calculate the electrical energy transmitted between regional transmission lines.

[0021] In this invention, a multinomial regression surrogate model is used as a basis to preprocess the line transmission constraints in the upper-level planning, thereby improving the calculation speed.

[0022] In this invention, line power flow constraints and transmission line limitation constraints are introduced into the constraints, which are combined with the actual operation of the power grid, making the optimization scheduling strategy more practically instructive.

[0023] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0025] Figure 1 This is a flowchart of the economic environment dispatching method for multi-regional power systems based on two-level planning in Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of the emotion accumulation game evolution algorithm in Embodiment 1 of the present invention;

[0027] Figure 3 This is a structural diagram of the simulation object, a test system with forty generators in four regions, used in Embodiment 1 of the present invention.

[0028] Figure 4(a) shows the Pareto optimal frontier generated by the test system of forty generators (load of 10500MW) in region 1 of Embodiment 1 of the present invention, considering all the above constraints.

[0029] Figure 4(b) shows the Pareto optimal frontier generated by the test system of forty generators (load of 10500MW) in region 2 of Embodiment 1 of the present invention, considering all the above constraints.

[0030] Figure 4(c) shows the Pareto optimal frontier generated by the test system of forty generators (load of 10500MW) in region 3 of Embodiment 1 of the present invention, considering all the above constraints.

[0031] Figure 4(d) shows the Pareto optimal frontier generated by the test system of forty generators (load of 10500MW) in region 4 of Embodiment 1 of the present invention, considering all the above constraints. Detailed Implementation

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0034] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0035] Example 1

[0036] like Figure 1 As shown, this embodiment discloses an economical scheduling method for multi-regional power systems based on two-level planning, including:

[0037] Step S101: Based on the operating parameters of the multi-regional power system, establish an upper-level planning agent model that minimizes power generation costs and environmental pollution costs with different planned total outputs in multiple regions as independent variables, and establish a lower-level planning objective with the goal of minimizing regional power generation costs and minimizing regional environmental pollution costs.

[0038] Step S102: Using a game theory algorithm, under the upper-level constraints corresponding to the upper-level planning agent model, solve the upper-level planning agent model to obtain the transmission power corresponding to the optimal value between different regions, and determine the total power generation corresponding to each region.

[0039] Step S103: Using an optimization algorithm, under the lower-level constraints corresponding to the lower-level planning objectives and the total power generation corresponding to each region, solve for the Pareto front data of each region;

[0040] Step S104: Make decisions based on the Pareto front data of each region to obtain the optimal scheduling strategy.

[0041] In step S101 of this embodiment, the obtained power grid operating parameters include: 1. Node parameters, mainly including the distribution of PQ, PV and reference nodes; 2. Active load during the dispatch period; 3. Node voltage amplitude, phase angle, and the maximum and minimum voltage that the node can withstand; 4. Active power output of generator nodes and the maximum and minimum active power that the node can withstand; 5. Maximum active power output allowed by each generator; 6. Branch parameters: branch resistance, reactance, per-unit susceptance, allowable capacity of long (short) distance transmission branches, and the maximum and minimum phase angle allowed by the branch; 7. Maximum allowable transmission capacity of transmission lines; 8. Regional hot standby requirements.

[0042] Specifically, the established upper-level planning objectives are expressed as follows:

[0043]

[0044] Where, ∑ i＝1：N C min (P i ) represents a proxy model for the minimum fuel consumption of the entire region given a fixed amount of electrical energy transmitted on all transmission lines, where i represents the i-th region, ∑ i＝1：N E min (P i ) represents a proxy model for the minimum emissions across the entire region, given that the electrical energy transmitted on all transmission lines is constant. k (P i ) and h k (P i ) represent the equality and inequality constraints involved, respectively, and M1 and M2 are the number of equality and inequality constraints, respectively.

[0045] Establish upper-level constraints corresponding to the upper-level planning objectives in advance, including transmission line security constraints and regional hot standby transfer constraints.

[0046] Specifically, transmission line safety constraints:

[0047]

[0048] Regional hot standby transfer constraints:

[0049]

[0050] Among them, T ip This represents the transfer power of the tie line between region i and region p, which affects the actual load demand of region i and region p. S represents the maximum power that can be safely transmitted between the i-th region and the p-th region; pj S is the hot reserve power of the j-th generator in the p-th region; p，req This is the required thermal reserve power for the p-th region, RC ip It represents the heat reserve power transferred between the i-th and q-th regions.

[0051] The lower-level planning objectives are represented as follows:

[0052]

[0053] Where F(P) and E(P) represent optimization objective 1 and optimization objective 2, respectively, g i (P) and h j (P) represents the equality and inequality constraints involved, and N1 and N2 represent the number of equality and inequality constraints, respectively.

[0054] Among them, the lower-level planning optimization objective 1 is the fuel cost:

[0055]

[0056] The second optimization objective for the lower-level planning objectives is the emission of pollutants:

[0057]

[0058] Among them, a ij b ij c ij d ij e ij Let α be the fuel cost coefficient of the j-th generator in the i-th region. ij β ij γ ij ε ij , λ ij Let N and N' be the pollutant emission coefficients of the j-th generator in the i-th region; g P represents the number of regions and generators participating in the scheduling, respectively; ij Let be the active power output of the j-th generator in the i-th region.

[0059] Pre-establish the lower-level constraints corresponding to the lower-level planning objectives, including generator active power upper and lower limit constraints, power balance constraints, node voltage amplitude constraints, and line power flow constraints.

[0060] Specifically, the upper and lower limits of generator active power are constrained as follows:

[0061]

[0062] Power balance constraints:

[0063]

[0064] Node voltage amplitude constraints:

[0065]

[0066] Power flow constraints:

[0067]

[0068] in, P k and P represents the lower limit, upper limit, and lower limit of the active power of the generator at node k. d and P loss These represent regional load and regional line network loss, respectively. and These represent the minimum and maximum allowable node voltages, respectively. S tr This represents the power flow between node t and node r; This represents the maximum allowed power flow between node t and node r;

[0069] In this embodiment, the objective function of the upper-level program is generated based on an improved polynomial regression surrogate model.

[0070] Specifically: S1011: Assume the total output of all generators in the i-th region is:

[0071]

[0072] Where n represents the number of different total outputs that can be selected in the i-th region, and i represents the i-th region;

[0073] S1012: For n different total outputs in S1011, the candidate solution with the minimum fuel consumption in the region is denoted as:

[0074]

[0075] in, This indicates that region i has the j-th type of output. Candidate solutions for minimum fuel consumption under the output condition, and Represented as Where m represents the total number of generators in region i;

[0076] S1013: From the series of candidate solutions for minimum fuel consumption determined in S1012, the i-th region can be determined. The corresponding series of minimum fuel consumption values ​​are In the formula This represents the minimum fuel consumption for the nth output in the i-th region;

[0077] S1014: For n different total outputs in S1011, the candidate solution for the minimum regional emission is denoted as:

[0078]

[0079] in, This indicates that region i has the j-th type of output. Candidate solutions for minimum emissions under the output condition, and Represented as Where m represents the total number of generators in region i;

[0080] S1015: For the series of candidate solutions with minimum emissions determined in S1014, the i-th region can be determined. The corresponding series of minimum emission values ​​are In the formula This represents the minimum emission under the nth power output in the i-th region;

[0081] S1016: Fitting and Obtain the minimum emissions corresponding to different outputs and the improved polynomial surrogate models for different outputs;

[0082] S1017: Fitting and Obtain the minimum emissions corresponding to different outputs and the improved polynomial surrogate model for different outputs.

[0083] The improved polynomial proxy model is expressed as follows:

[0084] Y = θ0 + θ1x + θ2x 2 +θ3x 3 +...+θ n x k (14)

[0085] Where x is Y is or θ0,θ1,…,θ n This represents the parameters of the polynomial proxy model, where k is the weight of the highest-order term in the polynomial proxy model.

[0086] The weight k of the highest-order term in formula (14) is determined by the consistency between the formal distance of the true solution and the pattern distance of the fitted solution.

[0087] Specifically, the pattern distance is expressed as:

[0088]

[0089] Where S represents a sequence, S m Let d represent the m-th element in sequence S, where D is the pattern distance sequence. m-1 S represents m and S m-1 The pattern distance.

[0090] The weight k of the highest-order term in the fuel consumption-regional output polynomial surrogate model is determined using pattern distance:

[0091] S10161: Calculation mode distance Specifically:

[0092]

[0093] S10162: Set k=1, fit the data. and A fuel consumption-regional output polynomial proxy model was obtained;

[0094] Specifically:

[0095]

[0096] S10163: Calculate the fitted value of fuel consumption using a fitted fuel consumption-regional output polynomial surrogate model.

[0097] S10164: Calculation mode distance

[0098]

[0099] S10165: Calculation and The number of identical elements Num cost ;

[0100] S10166: Calculate Num cost If the value is greater than or equal to 0.95n, output this polynomial surrogate model; if it is less than 0.95n, return to step S10162 and set k = k + 1.

[0101] The weight k of the highest-order term in the emission-regional output polynomial surrogate model is determined using the model distance:

[0102] S10171: Calculation mode distance Specifically:

[0103]

[0104] S10172: Set k=1, fit and We obtain the emission-regional output polynomial surrogate model;

[0105] Specifically:

[0106] S10173: Calculate fitted values ​​of emissions using a fitted surrogate model.

[0107] S10174: Calculation mode distance Specifically

[0108]

[0109] S10175: Calculation and The number of identical elements Num emission ;

[0110] S10176: Calculate Num emission If the value is greater than or equal to 0.95n, output this polynomial surrogate model; if it is less than 0.95n, return to step S10172 and set k = k + 1.

[0111] After determining the emission-regional power output polynomial proxy model and the fuel consumption-regional power output polynomial proxy model, the objective function of the upper-level planning is set as follows:

[0112]

[0113] Furthermore, P i It can be represented as:

[0114]

[0115] Where P i,load Let the load demand be for the i-th region, then the objective function can be rewritten as:

[0116]

[0117] This objective function is named the upper-level planning objective function.

[0118] Because of P in each region i,load Since it is a definite constant, the decision variable of the objective function is only the transmitted electrical energy on each transmission line, and it is constrained by:

[0119]

[0120]

[0121] Among these, due to the existence of heat transfer reserves between regions, the power constraint of line transmission further transforms into:

[0122]

[0123] In step S102 of this embodiment, a game theory algorithm is used to solve the upper-level planning objective under the upper-level constraints corresponding to the upper-level planning objective, to obtain the transmission power corresponding to the optimal value between different regions, and then to determine the actual total power generation corresponding to each region.

[0124] like Figure 2 As shown, the upper-level planning optimization is performed using the emotion accumulation game evolution algorithm. The emotion accumulation game evolution algorithm is as follows:

[0125] S1021: Initialize the population:

[0126] X(0)=X min +rand(1)×(X max -X min (16)

[0127] Here, the power output of each region, i.e., the electrical energy transmitted on the transmission line, is set as X(0), where X(0) represents the initial position of the population, X min X represents the lower limit of electrical energy transmission of a transmission line. max The upper limit of the electrical energy transmitted by the transmission line is represented by , rand(1) represents a random number between 0 and 1, and a population P is generated with each individual in P being a randomly generated individual X(0);

[0128] S1022: Population Division:

[0129] The original population P is randomly divided into two subpopulations of the same size, P1 and P2.

[0130] The individuals in the population (i.e., the electrical energy on all transmission lines) are fed into the upper-level planning objective function to calculate the fitness value of each individual.

[0131] Subpopulations P1 and P2 are sorted according to their fitness values.

[0132] The top 50% of individuals with the best fitness values ​​in subpopulation P1 were used to form a population. The remaining 50% of the individuals in subpopulation P1 constitute

[0133] The top 50% of individuals with the best fitness values ​​in subpopulation P2 were used to form the population. The remaining 50% of the individuals in subpopulation P2 constitute the population.

[0134] mix and To obtain subpopulation P exploit ;

[0135] mix and To obtain subpopulation P explore ;

[0136] Subpopulation P exploit and subpopulation P explore These are the two sides in the game.

[0137] S1023: Record the current optimal solution and the historical optimal solution;

[0138] P represents exploit The current optimal solution for the population;

[0139] P represents exploit The historical optimal solution of the population;

[0140] P represents explore The current optimal solution for the population;

[0141] P represents explore The historical optimal solution for the population.

[0142] S1024: Calculate the payoff matrix for both players in the game, where there are two alternative actions: explore and exploit. The payoff equations are divided into four types: F1(t), F2(t), F3(t), and F4(t), specifically:

[0143] The alternative action plan is represented as follows:

[0144] X(t+1)=X(t)+0.2×X(t)×Levy(d) (17)

[0145] In the formula, X(t+1) represents the updated position of the individual, X(t) represents the current position of the individual, d represents the dimension of the candidate solution, Levy(d) represents the column-dimensional flight of dimension d, and t is the current iteration number;

[0146] The exploit alternative course of action is represented as follows:

[0147]

[0148] In the formula X other S1(t) is the position of a random individual in the current population other than X(t), T is the highest iteration number, and the expressions for S1(t) and S2(t) are:

[0149]

[0150] C1(t) represents a sinusoidal chaotic sequence, specifically:

[0151]

[0152] F1(t), F2(t), F3(t), and F4(t) are as follows:

[0153]

[0154] In the formula, F1(t) represents P explore The profit and loss function of the explore action plan is executed at the t-th iteration, where F2(t) represents P. exploit The profit / loss function of the exploit action plan executed at the t-th iteration, F3(t) represents P. exploit The profit and loss function of the explore action plan is executed at the t-th iteration, where F4(t) represents P. explore The profit and loss function of the exploit action is executed at the t-th iteration. P represents exploit The optimal value at the t-th iteration. P represents explore The optimal value at the t-th iteration.

[0155] S1025: Calculate P explore and P exploit The emotional accumulation factors are specifically:

[0156]

[0157] In the formula, λ emotion (t) is the emotion accumulation factor at the t-th iteration, λ emotion (t-1) is the emotion accumulation factor at the (t-1)th iteration, λ envy (t) is the jealousy factor at the t-th iteration, λ confidence F(t) represents the confidence factor at the t-th iteration, and F(t) represents the population P. explore or P exploitLet f(t) be the profit / loss value of the other population at the t-th iteration, and λ be the profit / loss value of the other population at the t-th iteration. emotion (0) is the initial value of the emotional accumulation factor and is set to 0.1, λ envy (0) is the initial value of the jealousy factor and is set to 0.05, λ confidence (0) is the initial value of the confidence factor and is set to 0.05.

[0158] S1026: Determine the alternative action plan for the next selection of the population based on the sentiment accumulation factor, specifically:

[0159] Calculate P explore Cumulative sentiment factor at the i-th iteration

[0160] Calculate P exploit Cumulative sentiment factor at the i-th iteration

[0161] Generate two random numbers between 0 and 1, denoted as a and b;

[0162] if Then P explore Select the alternative action plan exploit to execute, and... Set the initial value to 0.1, otherwise P explore Select an alternative course of action and explore it;

[0163] if Then P exploit Select the alternative action plan explore and execute it, and... Set the initial value to 0.1, otherwise P exploit Select an alternative course of action and exploit it.

[0164] S1027: Repeat S1023-S1026 until the iteration ends.

[0165] S1028: Confirm and The optimal value is determined by taking the electrical energy transmitted on the transmission line corresponding to the optimal value as the optimal solution and outputting it.

[0166] In step S103 of this embodiment, the individual corresponding to the optimal solution obtained by the upper-level planning represents the electrical energy transmitted on all interconnect lines. By adding the electrical energy on the transmission lines of each region to the load demand of that region, the actual power generation demand of each region in the lower-level model can be obtained.

[0167] After obtaining the actual power generation demand for each region, the Pareto front for each region is calculated using a multi-objective particle swarm optimization algorithm. The decision corresponding to the result on the Pareto front is the non-dominated solution for each region, specifically:

[0168] Several unit output configurations that meet the regional load requirements are randomly generated and compiled into the initial positions of the particle swarm, resulting in an initial population. Each particle in the population represents a unit output configuration.

[0169] Ensure that the sum of the output of all individuals in the population is equal to the actual power generation demand of each region, and satisfy the upper and lower limits of generator active power, power balance constraints, node voltage amplitude constraints, and line power flow constraints.

[0170] The population is fed into the lower-level programming optimization objective function (i.e., the lower-level programming fuel consumption function and the lower-level programming pollution emission function) to obtain the fuel consumption value and pollution emission value of each individual in the population.

[0171] The initial elite solution set is obtained based on the dominance relationship between the initial population's fuel consumption and pollution emission values;

[0172] The iteration begins by calculating the congestion distance of each particle in the elite solution set within the constructed lower-level planning fuel consumption function and pollution emission function, and generating the globally optimal solution set, specifically:

[0173] dist[ii] = dist cost [ii]+dist emission [ii]

[0174] Where dist[ii] represents the congestion distance of particle ii in the lower-level planning fuel consumption function and the lower-level planning pollution emission function, dist cost [ii] represents the crowding distance of particle ii under the lower-level planning fuel consumption function, dist emission [ii] represents the crowding distance of particle ii under the lower-level planning pollution emission function;

[0175] The global optimal solution is randomly selected from the global optimal solution set. The velocity and position of the particle swarm are updated based on the multi-objective particle swarm algorithm. The fitness value of the particles is calculated using the constructed multi-objective function and constraints.

[0176] Update the individual optimal value of each particle and set a small probability of accepting inferior solutions to obtain the updated new population;

[0177] After merging the new population and the elite solution set, update the elite solution set according to the dominance relationship of the merged population. When the number of particles in the updated elite solution set is greater than that in the initial population, sort the particles in the current elite solution set in order of increasing crowding distance, delete the particles with small crowding distance in the current elite solution set, so that the number of particles in the current elite solution set is the same as that in the initial population.

[0178] Once the set number of iterations is reached, the iteration ends, the elite solution set is derived, the Pareto front is plotted, and non-dominated solutions are obtained. Each non-dominated solution is the optimal solution for the generators in that region to satisfy the actual power generation of that region.

[0179] In step S104 of this embodiment, the non-dominated solution for each region has been obtained in the previous step. All of these solutions are optimal solutions, but the scheduling decision can only select one set from all the current solutions. This requires the power sector to formulate corresponding scheduling strategies based on the actual situation.

[0180] This embodiment presents a discrimination method based on membership function, in which the non-dominated solution with the largest membership value is selected as the basis for scheduling decision in this time period.

[0181] For the two optimization objectives of environmental and economic operation, for each objective function, the membership function value corresponding to its non-dominated solution is calculated as follows:

[0182]

[0183] In the formula, F i,k For the k-th solution of the i-th optimization objective, and These are the minimum and maximum values ​​of the i-th optimization objective, respectively.

[0184] For each individual nondominated solution, μ i,k Regularization yields μ j The method is as follows:

[0185]

[0186] Where N1 = 2 is the number of optimization objectives, and M is the number of non-dominated solutions.

[0187] Final solution μ j To obtain the non-dominated solution corresponding to the maximum value, which serves as the basis for scheduling decisions in the current time period.

[0188] In this embodiment Figure 3 The simulation results are as follows Figures 4(a)-4(d) As shown, the extreme value solutions of the two optimization objectives are shown in Table 1 (fuel cost is in RMB / h, and pollutant emission is in ton / h).

[0189] Table 1. Solution to extreme values ​​of fuel cost

[0190]

[0191]

[0192]

[0193] In Figure 4, for Figure 3 In the simulation example, the Pareto optimal front for each region obtained in this embodiment consists of 20 non-dominated solutions. This is a set of compromise solutions formed for the two optimization objectives of environment and economy. All non-dominated solutions are the optimal solutions for this scheduling period. The extreme value solution in Figure 4 is (892377.11, 179352.42). The size and breadth of the extreme value solutions directly determine the advancement of the optimization method.

[0194] To compare the advancement of the method in this embodiment, several representative algorithms were selected for comparison in Table 1.

[0195] In Table 1, the minimum fuel cost obtained using the method of this invention is ¥892,377.11 / h, which is ¥7,427.73 / h, ¥8,038.35 / h, and ¥977.26 / h lower than the other three methods, respectively. The other extreme value, i.e., the pollutant emission of 177,386.3 tons / h, is also the lowest among all methods. Therefore, this embodiment is more advanced than other existing methods.

[0196] The experimental data above demonstrates that, compared to previous economic environment dispatching methods, this embodiment achieves superior performance while meeting basic power supply requirements. It also effectively reduces power generation costs and pollutant emissions. Taking the extreme point in Table 1 as an example, if this solution is used as the dispatching basis, assuming the load remains stable at 10500MW, comparing the method of this invention with the NSOS algorithm, this embodiment can save approximately 192,920 yuan in fossil fuel costs per day, and approximately 70,415,946 yuan per year.

[0197] Example 2

[0198] This embodiment provides a multi-regional power system economic environment dispatching system based on two-level planning, including:

[0199] A two-level planning model module is established: based on the operating parameters of the multi-regional power system, an upper-level planning proxy model is established with different planned total outputs in multiple regions as independent variables to minimize power generation costs and environmental pollution costs, and a lower-level planning objective is established with minimizing regional power generation costs and minimizing regional environmental pollution costs as objectives.

[0200] Solving the upper-level planning module: Using a game theory algorithm, under the upper-level constraints corresponding to the upper-level planning proxy model, the upper-level planning proxy model is solved to obtain the transmission power corresponding to the optimal value between different regions, and to determine the total power generation corresponding to each region;

[0201] Solving the lower-level planning module: Using an optimization algorithm, under the lower-level constraints corresponding to the lower-level planning objectives and the total power generation corresponding to each region, the Pareto front data for each region is obtained;

[0202] Scheduling output module: Makes decisions based on the Pareto front data of each region to obtain the optimal scheduling strategy.

[0203] Example 3

[0204] The purpose of this embodiment is to provide a computing device, including a processor, a memory, and a bus. The memory stores machine-readable instructions that can be executed by the processor. When the computer device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor in the manner described above.

[0205] Example 4

[0206] The purpose of this embodiment is to provide a computer-readable storage medium.

[0207] A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the above-described method.

[0208] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0209] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0210] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-regional power system economic environment dispatching method based on two-level planning, characterized in that, include: Step S101: Based on the operating parameters of the multi-regional power system, establish an upper-level planning proxy model that minimizes power generation cost and environmental pollution cost with different planned total output of the multi-region as independent variables, and establish a lower-level planning objective with minimizing regional power generation cost and minimizing regional environmental pollution cost as objectives; wherein, the weight of the highest-order term of the polynomial regression proxy model of fuel consumption-regional output in the upper-level planning is determined by the pattern distance. Step S102: Using a game theory algorithm, under the upper-level constraints corresponding to the upper-level planning agent model, solve the upper-level planning agent model to obtain the transmission power corresponding to the optimal value between different regions, and determine the total power generation corresponding to each region. Specifically, the electrical energy transmitted on the transmission line is considered as individuals in a population, and the population is randomly divided into two subpopulations of equal size. and ; The population individuals are fed into the objective function of the upper-level planning agent model to calculate the fitness value of each individual; Subpopulation The first preset percentage of individuals and subpopulations with medium fitness The first preset percentage of individuals with medium fitness constitute the subpopulation , sub-population The second preset percentage of individuals and subpopulations after medium fitness The second preset percentage of individuals after medium fitness constitutes the subpopulation. ; Subpopulation Subpopulation As two sides in the game; Subpopulation calculated using the profit and loss equation Subpopulation The emotional accumulation factor is used to determine the alternative action plan for the next selection of the population. This process is iterated continuously, and the optimal value corresponding to the electrical energy transmitted on the transmission line is output. Specifically: Calculate subpopulation In the Accumulated emotional factors at the next iteration ; Calculate subpopulation In the Accumulated emotional factors at the next iteration ; subpopulation Harmony population The calculation of the emotional accumulation factor is as follows: In the formula, For the first The cumulative emotional factor at each iteration For the first The cumulative emotional factor at each iteration For the first Jealousy factor at the next iteration For the first Confidence factor during the next iteration For population or No. Profit and loss value at the next iteration For another population in the first Profit and loss value at the next iteration This is the initial value for the emotional accumulation factor. Let be the initial value of the jealousy factor. This represents the initial value of the confidence factor; Generate two random numbers between 0 and 1, denoted as . and ; if ,but Choose alternative action plan Execute, and Set the initial value to 0.1, otherwise... Choose alternative action plan implement; if ,but Choose alternative action plan Execute, and Set the initial value to 0.1, otherwise... Choose alternative action plan implement; Until the iteration ends; Sure The historical optimal solution of the population and The historical optimal solution of the population The optimal value in the equation is the electrical energy transmitted on the transmission line corresponding to the optimal value, which is then output as the optimal solution. Step S103: Using an optimization algorithm, under the lower-level constraints corresponding to the lower-level planning objectives and the total power generation corresponding to each region, solve for the Pareto front data of each region; Step S104: Make decisions based on the Pareto front data of each region to obtain the optimal scheduling strategy.

2. The multi-regional power system economic environment dispatching method based on two-level planning as described in claim 1, characterized in that, The upper-level constraints include transmission line security constraints and regional hot standby transfer constraints; the lower-level constraints include power balance constraints, node voltage magnitude constraints, and line power flow constraints.

3. The multi-regional power system economic environment dispatching method based on two-level planning as described in claim 1, characterized in that, In step S101, establishing the upper-level planning agent model specifically includes: By fitting the minimum fuel consumption corresponding to different planned total output of a certain region and the total output of all generators in the region, a multinomial regression surrogate model of fuel consumption - regional output in the upper-level planning is obtained. By fitting the minimum emissions corresponding to different planned total outputs for a certain region and the total output of all generators in that region, a multinomial regression surrogate model of emissions-regional output in the upper-level planning is obtained.

4. The multi-regional power system economic environment dispatching method based on two-level planning as described in claim 3, characterized in that, In step S101, the weight of the highest-order term in the polynomial regression surrogate model of fuel consumption-regional output in the upper-level planning is determined using pattern distance, specifically as follows: Step S10161: Calculate the first mode distance with the minimum fuel consumption under different planned total outputs for a certain region; Step S10162: Setting By fitting the minimum fuel consumption corresponding to different planned total output of a certain region and the total output of all generators in the region, a first fuel consumption-region output polynomial surrogate model is obtained. Step S10163: Calculate the fitted value of fuel consumption using the first fuel consumption-regional output polynomial proxy model; Step S10164: Calculate the second mode distance of the fitted value of fuel consumption; Step S10165: Calculate the number of elements with the same distance between the first mode and the second mode; Step S10166: If the ratio of the number of identical elements obtained in step S10165 to the number of regions n is not less than a first preset value, then output the first fuel consumption-region output polynomial proxy model; if it is less than the first preset value, then return to step S10162 and... .

5. The multi-regional power system economic environment dispatching method based on two-level planning as described in claim 3, characterized in that, In step S101, the weight of the highest-order term in the polynomial regression surrogate model of emissions-regional output in the upper-level planning is determined using pattern distance, specifically as follows: Step S10171: Calculate the third mode distance of the minimum emission under different planned total output in a certain area; Step S10172: Setting The first emission-regional output polynomial surrogate model is obtained by fitting the minimum emission corresponding to different planned total output of a certain region and the total output of all generators in the region. Step S10173: Calculate the fitted value of the emission using the first emission-regional output polynomial surrogate model; Step S10174: Calculate the fourth mode distance of the fitted values ​​of emissions; Step S10175: Calculate the number of elements whose distances to the third and fourth modes are the same; Step S10176: If the ratio of the number of identical elements obtained in step S10175 to the number of regions n is not less than a second preset value, then output the first emission-regional output polynomial proxy model; if it is less than the second preset value, return to step S10172 and then... .

6. The multi-regional power system economic environment dispatching method based on two-level planning as described in claim 1, characterized in that, In step S103, the Pareto front for each region is calculated using a multi-objective particle swarm optimization algorithm. The decision corresponding to the result on the Pareto front is the non-dominated solution for each region, specifically: Several unit output configurations that meet the regional load requirements are randomly generated as the initial positions of the particle swarm, resulting in an initial population. Each particle in the population represents a unit output configuration. Each individual in the population satisfies the condition that the sum of its output equals the total power generation corresponding to each region and satisfies the condition that the lower-level constraints are met. Substituting the individuals in the population into the lower-level planning objectives, we obtain the fuel consumption value and pollution emission value of each individual in the population. The initial elite solution set is obtained based on the dominance relationship between the initial population's fuel consumption and pollution emission values; Calculate the congestion distance of each particle in the elite solution set in the constructed lower-level planning fuel consumption function and lower-level planning pollution emission function to generate the global optimal solution set; The global optimal solution is randomly selected from the global optimal solution set. The particle swarm velocity and position are updated based on the multi-objective particle swarm algorithm. The fitness value of the particles is calculated using the constructed lower-level objective function and lower-level constraints. Update the individual optimal value of each particle and set a small probability of accepting inferior solutions to obtain the updated new population; After merging the new population and the elite solution set, update the elite solution set according to the dominance relationship of the merged population. When the number of particles in the updated elite solution set is greater than that in the initial population, sort the particles in the current elite solution set in order of increasing crowding distance, delete the particles with small crowding distance in the current elite solution set, so that the number of particles in the current elite solution set is the same as that in the initial population. Once the set number of iterations is reached, the iteration ends, the elite solution set is obtained, the Pareto front is plotted, and non-dominated solutions are obtained. Each non-dominated solution obtained is the optimal solution for the generators in that region to satisfy the actual power generation of that region.

7. The multi-regional power system economic environment dispatching method based on two-level planning as described in claim 1, characterized in that, In step S104, specifically: For the two optimization objectives of environment and economic operation in the lower-level planning objectives, calculate the membership function values ​​corresponding to their non-dominated solutions respectively; For each individual non-dominated solution, the calculated membership function value is regularized; The non-dominated solution corresponding to the maximum value after regularization is used as the scheduling basis for the current time period.

8. A multi-regional power system economic environment dispatching system based on two-level planning, characterized in that, include: A two-level planning module is established: based on the operating parameters of the multi-regional power system, an upper-level planning proxy model is established with different planned total outputs in multiple regions as independent variables to minimize power generation costs and environmental pollution costs, and a lower-level planning objective is established with minimizing regional power generation costs and minimizing regional environmental pollution costs as objectives; wherein, the pattern distance is used to determine the weight of the highest-order term of the polynomial regression proxy model of fuel consumption-regional output in the upper-level planning. Solving the upper-level planning module: Using a game theory algorithm, under the upper-level constraints corresponding to the upper-level planning proxy model, the upper-level planning proxy model is solved to obtain the transmission power corresponding to the optimal value between different regions, and to determine the total power generation corresponding to each region; Specifically, the electrical energy transmitted on the transmission line is considered as individuals in a population, and the population is randomly divided into two subpopulations of equal size. and ; The population individuals are fed into the objective function of the upper-level planning agent model to calculate the fitness value of each individual; Subpopulation The first preset percentage of individuals and subpopulations with medium fitness The first preset percentage of individuals with medium fitness constitute the subpopulation , sub-population The second preset percentage of individuals and subpopulations after medium fitness The second preset percentage of individuals after medium fitness constitutes the subpopulation. ; Subpopulation Subpopulation As two sides in the game; Subpopulation calculated using the profit and loss equation Subpopulation The emotional accumulation factor is used to determine the alternative action plan for the next selection of the population. This process is iterated continuously, and the optimal value corresponding to the electrical energy transmitted on the transmission line is output. Specifically: Calculate subpopulation In the Accumulated emotional factors at the next iteration ; Calculate subpopulation In the Accumulated emotional factors at the next iteration ; subpopulation Harmony population The calculation of the emotional accumulation factor is as follows: In the formula, For the first The cumulative emotional factor at each iteration For the first The cumulative emotional factor at each iteration For the first Jealousy factor at the next iteration For the first Confidence factor during the next iteration For population or No. Profit and loss value at the next iteration For another population in the first Profit and loss value at the next iteration This is the initial value for the emotional accumulation factor. Let be the initial value of the jealousy factor. This represents the initial value of the confidence factor; Generate two random numbers between 0 and 1, denoted as . and ; if ,but Choose alternative action plan Execute, and Set the initial value to 0.1, otherwise... Choose alternative action plan implement; if ,but Choose alternative action plan Execute, and Set the initial value to 0.1, otherwise... Choose alternative action plan implement; Until the iteration ends; Sure The historical optimal solution of the population and The historical optimal solution of the population The optimal value in the equation is the electrical energy transmitted on the transmission line corresponding to the optimal value, which is then output as the optimal solution. Solving the lower-level planning module: Using an optimization algorithm, under the lower-level constraints corresponding to the lower-level planning objectives and the total power generation corresponding to each region, the Pareto front data for each region is obtained; Scheduling output module: Makes decisions based on the Pareto front data of each region to obtain the optimal scheduling strategy.

9. A computer device, characterized in that, include: The system includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the system performs the economic dispatch method for multi-regional power systems based on bi-level planning as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the economic and environmental dispatching method for multi-regional power systems based on bi-level planning as described in any one of claims 1 to 7.

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