Micro-grid optimization scheduling method and device, storage medium and electronic equipment

Abstract

The invention discloses a micro-grid optimization scheduling method and device, a storage medium and electronic equipment, and belongs to the technical field of micro-grid scheduling. The micro-grid optimization scheduling method comprises the following steps: establishing a micro-grid system simulation model; and on the basis of generator operation constraint conditions and system power constraint conditions, with the minimum operation cost of the micro-grid system as an optimization target, optimizing the active power of each generator in the micro-grid system by adopting an improved whale optimization algorithm in combination with the micro-grid system simulation model. According to the embodiment of the invention, based on the improved whale optimization algorithm, rapid and accurate microgrid optimization scheduling solution can be realized.

Description

Technical Field

[0001] The present invention relates to the field of microgrid scheduling technology, and in particular to a microgrid optimization scheduling method, device, storage medium and electronic equipment. Background Art

[0002] The ELD problem (Economic Load Dispatch) is a nonlinear optimization problem. In order to minimize the total operating cost, the load needs to be allocated to each operating unit according to various constraints. Traditional solutions are derived from classical mathematical algorithms, such as linear programming algorithms and dynamic programming methods. However, linear programming algorithms are limited to solving linear problems, so it is necessary to linearize the objective function and constraints of the problem model in advance, which will affect the accuracy of the calculation results. The dynamic programming method requires a lot of calculations. When the variable dimension increases, the size of the state space grows exponentially, resulting in a sharp increase in computational complexity and storage requirements, making it difficult to handle multi-dimensional optimization problems. Summary of the invention

[0003] The present invention provides a microgrid optimization scheduling method, device, storage medium and electronic device, which realize fast and accurate microgrid optimization scheduling solution based on an improved whale optimization algorithm.

[0004] In a first aspect, an embodiment of the present invention provides a microgrid optimization scheduling method, including:

[0005] Establish a microgrid system simulation model;

[0006] Based on the generator operation constraints and system power constraints, with the lowest operation cost of the microgrid system as the optimization goal, the improved whale optimization algorithm is used in combination with the microgrid system simulation model to optimize the active power of each generator in the microgrid system.

[0007] Optionally, the objective function for optimizing the active power of each generator in the microgrid system is:

[0008]

[0009] Where C(P) is the operating cost of the microgrid system, n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, and P i is the active power of the i-th generator, F i (P i ) is when the active power of the i-th generator is P i The energy consumption per unit time when i (P i )=a i P i2 +b i P i +c i , a i 、b i and c i Represents the power generation consumption parameters of the i-th generator.

[0010] Optionally, the generator operation constraint condition is:

[0011] P i min ≤P i ≤P i max , i=1, 2, …, n;

[0012] Where n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, P i is the active power of the i-th generator, P i min and P i max are the minimum and maximum active power of the i-th generator respectively.

[0013] Optionally, the system power constraint condition includes: at least one of a power balance constraint and a line power constraint;

[0014] The power balance constraint is: Where n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, P i is the active power of the i-th generator, P L is the total load in the microgrid system, P S is the total net loss of the microgrid system;

[0015] The line power constraint is: P j min ≤P j ≤P j max , j = 1, 2, ..., m;

[0016] Where m is the number of lines in the microgrid system, m is a positive integer greater than or equal to 2, P j is the power transmitted on the jth line, P j min and P j max are the lower and upper limits of the transmission power on the jth line respectively.

[0017] Optionally, an improved whale optimization algorithm is used in combination with the microgrid system simulation model to optimize the active power of each generator in the microgrid system, including:

[0018] Generate an initial whale population, initialize the position of each individual whale in the initial whale population, record the number of iterations as 1, and perform an iterative calculation using the initial whale population as the target population; wherein the position of the individual whale is the value of the optimization variable, and the optimization variable includes the active power of each of the generators;

[0019] Determine whether the number of iterations reaches the preset number;

[0020] If yes, the optimization process ends, and the active power of each generator in the microgrid system is obtained according to the prey position of this iteration;

[0021] If not, the positions of the individual whales are updated according to the positions of the individual whales in this iteration, the positions of the prey in this iteration, the probability factor, the coefficient vector of this iteration and the weight coefficient of this iteration to generate an updated population, the number of iterations is increased by 1, and the updated population is used as the target population for an iterative calculation;

[0022] Wherein, the iterative calculation includes:

[0023] Substituting the position of each whale individual in the target population into the microgrid system simulation model; for any of the whale individuals, calculating the fitness value of the whale individual when it is determined that the operation constraint condition and the system power constraint condition are satisfied according to the position of the whale individual and the output of the microgrid system simulation model;

[0024] According to the fitness value of each of the whale individuals, the optimal fitness value of each of the whale individuals in the target population is searched, and the position of the whale individual with the optimal fitness value is used as the prey position of this iteration.

[0025] Optionally, updating the position of each whale individual according to the position of each whale individual in this iteration, the position of the prey in this iteration, the probability factor, the coefficient vector of this iteration and the weight coefficient of this iteration to generate an updated population includes:

[0026] When the absolute value of the coefficient vector of this iteration is less than 1 and the probability factor is less than 0.5, the circling hunting phase is entered, and for any of the individual whales, the position of the individual whale is updated using the following formula:

[0027] X(t+1)=w(t)×Xp(t)-A×D;

[0028] Wherein, t represents the number of iterations of this iteration; X(t+1) is the updated position of the individual whale; w(t) is the weight coefficient of this iteration; Xp(t) is the prey position of this iteration; A is the coefficient vector of this iteration, A=2a(t)×r1-a(t), Max_iter is the preset number of times; D = |C×Xp(t)-X(t)|, C = 2×r2, r2 is a random number between [0,1];

[0029] When the absolute value of the coefficient vector of this iteration is less than 1 and the probability factor is greater than or equal to 0.5, the spiral update phase is entered, and for any of the individual whales, the position of the individual whale is updated using the following formula:

[0030] X(t+1)=w(t)×Xp(t)+D×e bl ×cos(2πl);

[0031] Where b is a constant used to limit the shape of the logarithmic spiral, and l is a random number between [-1,1];

[0032] When the absolute value of the coefficient vector of this iteration is greater than or equal to 1, the prey search phase is entered, and for any of the individual whales, the position of the individual whale is updated using the following formula:

[0033] X(t+1)=r3×(X'(t)-X(t))+r4×(X'(t)-X(t));

[0034] Among them, r3 and r4 are both random numbers between [0,1], X(t) is the position of the whale individual in this iteration; X'(t) is the position of the whale individual randomly selected from the target population of this iteration.

[0035] Optionally, the weight coefficient of this iteration is:

[0036] In a second aspect, an embodiment of the present invention provides a microgrid optimization scheduling device, comprising:

[0037] A model building module is used to build a microgrid system simulation model;

[0038] The optimization module is used to optimize the active power of each generator in the microgrid system based on the generator operation constraints and the system power constraints, with the lowest operation cost of the microgrid system as the optimization goal, using the improved whale optimization algorithm, combined with the microgrid system simulation model.

[0039] In a third aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0040] at least one processor; and a memory communicatively coupled to the at least one processor;

[0041] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid optimization scheduling method provided in any embodiment of the present invention.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the microgrid optimization scheduling method provided by any embodiment of the present invention when executed.

[0043] In the microgrid optimization scheduling method provided in the embodiment of the present invention, an improved whale optimization algorithm is adopted, combined with a microgrid system simulation model, to optimize the active power of each generator in the microgrid system. Based on the advantages of the improved whale algorithm in solving non-convex, nonlinear constraints and high-dimensional optimization problems, the overall convergence speed can be accelerated and local optimality can be prevented, thereby using fewer computing resources to quickly and accurately solve the microgrid optimization scheduling problem, so that the use cost of the microgrid system is lower and the best economic benefit is achieved.

[0044] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 It is a flow chart of a microgrid optimization scheduling method provided by an embodiment of the present invention;

[0047] Figure 2 is a comparison result of different algorithms provided by an embodiment of the present invention when the total load is 500MW;

[0048] Figure 3 is a comparison result of different algorithms provided by an embodiment of the present invention when the total load is 850MW;

[0049] Figure 4 It is a structural schematic diagram of a microgrid optimization scheduling device provided by an embodiment of the present invention;

[0050] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0053] An embodiment of the present invention provides a microgrid optimization scheduling method, which is suitable for solving the economic load scheduling problem of generators in a microgrid system with multiple generators. The method can be executed by a microgrid optimization scheduling device, which can be implemented in the form of hardware and / or software, and the microgrid optimization scheduling device can be configured in an electronic device.

[0054] Figure 1 FIG. 1 is a flow chart of a microgrid optimization scheduling method provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0055] S110. Establish a microgrid system simulation model.

[0056] Specifically, any commonly used modeling tool can be used to establish a microgrid system simulation model according to the specific structure of the microgrid to be optimized. The microgrid system simulation model can be used to simulate and analyze the operating characteristics of the microgrid, such as studying and verifying the performance and behavior of the microgrid under different operating conditions. The microgrid system simulation model can include multiple components, such as: generators, energy storage devices, loads and related lines, etc.; it can also include a controller that can realize P / Q control or U / f control, etc., which is used to adjust the operating state of the microgrid and analyze the operating state of the microgrid system according to the power and voltage of key nodes.

[0057] S120. Based on the generator operation constraints and system power constraints, with the lowest operation cost of the microgrid system as the optimization goal, the improved whale optimization algorithm is used in combination with the microgrid system simulation model to optimize the active power of each generator in the microgrid system.

[0058] Among them, the generator operation constraints include, for example, limiting the active power / voltage output by each generator to be within its allowable output range; the system power constraints include, for example, limiting the balance between system power generation and power consumption, limiting the power / voltage of each line / node to be within its allowable operating range, etc.

[0059] Improved whale optimization algorithm (Adaptive Weight Optimization Algorithm, ADWOA) is an improved adaptive weight optimization algorithm. In this algorithm, by adaptively adjusting the weight, it can better adapt to the dynamic changes of the system, and is very suitable for solving the optimal scheduling of microgrids. By introducing an adaptive weight strategy, it can better adjust the global search ability and local development ability, while maintaining population diversity, improve the convergence speed, find the optimal solution faster, and avoid falling into the local optimum. The algorithm has a simple structure, is easy to implement, and requires a small amount of calculation. It is suitable for various continuous and discrete optimization problems. It also has a good optimization effect for multi-dimensional variables, can effectively track the optimization target, significantly reduce fluctuations, and enhance the stability of power grid operation. Specifically, the optimization variable is the active power of each generator in the microgrid system, and the optimization target is to minimize the operating cost of the microgrid system. The objective function can be formed based on the relationship between the operating cost of the microgrid system and the active power of each generator. The input of the microgrid system simulation model is, for example, the active power of each generator. In each iteration of the optimization process, each set of variable values ​​can be brought into the microgrid system simulation model, so that the microgrid system simulation model can be simulated to obtain parameters such as power and voltage on each line and key node. Then, based on the operation results of the microgrid system simulation model, it is judged whether each set of variable values ​​meets the generator operation constraints and system power constraints, so as to decide whether to retain the set of variables, until the variable combination that can make the objective function value the lowest is finally obtained as the optimization result.

[0060] In the microgrid optimization scheduling method provided in the embodiment of the present invention, an improved whale optimization algorithm is adopted, combined with a microgrid system simulation model, to optimize the active power of each generator in the microgrid system. Based on the advantages of the improved whale algorithm in solving non-convex, nonlinear constraints and high-dimensional optimization problems, the overall convergence speed can be accelerated and local optimality can be prevented, thereby using fewer computing resources to quickly and accurately solve the microgrid optimization scheduling problem, so that the use cost of the microgrid system is lower and the best economic benefit is achieved.

[0061] Based on the above implementations, optionally, the objective function for optimizing the active power of each generator in the microgrid system is: Where C(P) is the operating cost of the microgrid system; n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2; i is a positive integer between 1 and n; P i is the active power of the i-th generator; F i (P i ) is when the active power of the i-th generator is P i The energy consumption per unit time. i (P i )=a i P i 2 +b i P i +c i , a i 、b i and c i represents the power generation consumption parameter of the i-th generator. For each generator, its power generation consumption parameter is a constant. Then, in the end, the objective function of the ELD problem is: The optimization variables are P1 to P n The optimization goal is to minimize the objective function.

[0062] Based on the above embodiments, optionally, economic load distribution is mainly subject to generator operation constraints and power constraints.

[0063] Specifically, the generator operation constraints are: P i min ≤P i ≤P i max , i = 1, 2, ..., n; where n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, P i is the active power of the i-th generator, P i min and P i max are the minimum and maximum active power of the i-th generator respectively.

[0064] The system power constraint condition includes: at least one of a power balance constraint and a line power constraint.

[0065] Among them, the power balance constraint is: Where n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, P i is the active power of the i-th generator, P L is the total load in the microgrid system, PS is the total net loss of the microgrid system. It can be understood that P L and P S All of them can be obtained based on the operation results of the microgrid system simulation model.

[0066] The line power constraint is: P j min ≤P j ≤P j max , j = 1, 2, ..., m; where m is the number of lines in the microgrid system, m is a positive integer greater than or equal to 2, j is a positive integer between 1 and m, P j is the power transmitted on the jth line, P j min and P j max are the lower and upper limits of the transmission power on the jth line respectively.

[0067] In summary, the embodiment of the present invention is equivalent to expressing the microgrid optimization scheduling problem as an n-dimensional nonlinear programming problem with multiple equality and inequality constraints by mathematical methods, so as to solve it by using the improved whale algorithm. The specific process of the improved whale algorithm is exemplified below.

[0068] The following is a brief description of the whale optimization algorithm. The whale optimization algorithm is an intelligent algorithm based on humpback whale predation. Its bionic mechanism is to simulate the bubble net feeding behavior of whales and divide the algorithm into three stages: prey encirclement, spiral update and prey search. The whale first gradually obtains relevant information about the prey through search. Then the whale approaches the prey by circling and circling, and finally captures the prey. Specifically, in the circling prey stage, when hunting, the whale may feel the approach of the prey without knowing its exact location; at this time, the whale needs to communicate continuously within the group so that they can move together to the position where the whale is closest to the prey (the current optimal solution); at the same time, this whale randomly tries to approach the prey, thereby indirectly preventing other whales from approaching the prey. In the spiral update stage, the algorithm simulates the behavior of the whale spiraling smoothly to approach the prey; when the whale is circling, it must first calculate the distance to the prey (the current optimal solution), and then gradually circle towards the prey. During the prey search phase, whales use the absolute value of the coefficient vector to determine whether they are searching for prey or around prey; when the absolute value of the coefficient vector is > 1, whales cannot obtain any valid information about the prey; therefore, whales will randomly search for signs of prey.

[0069] However, since the basic whale optimization algorithm is prone to falling into local optimality and premature convergence, an embodiment of the present invention proposes an improved whale algorithm with an adaptive weight update strategy and a differential evolution random mutation strategy. Specifically, each individual whale must complete the prey encirclement, spiral update and prey search stages. When the individual surrounds, searches or circles, the position is updated using an adaptive weight update strategy, and then the position is updated again using a randomly changing differential evolution strategy; the optimal position is then selected from the updated results to accelerate the convergence of the population and effectively prevent the population from falling into a local optimality. By combining these two population strategies, the algorithm achieves a greater degree of optimization in search accuracy and speed.

[0070] Specifically, the following strategy is used when the position needs to be updated in each iteration:

[0071] When the absolute value of the coefficient vector of this iteration is less than 1 and the probability factor is less than 0.5, the circling hunting stage is entered. For any individual whale, the position of the individual whale is updated using the following formula: X(t+1)=w(t)×Xp(t)-A×D; where t represents the number of iterations of this iteration; X(t+1) is the updated position of the individual whale; w(t) is the weight coefficient of this iteration; Xp(t) is the prey position of this iteration (the prey position corresponds to the optimal global solution of the problem, that is, the optimal position of each individual whale in the population); A is the coefficient vector of this iteration, A=2a(t)×r1-a(t), a(t) is the control parameter, Max_iter is the preset number (i.e., the maximum number of iterations); as the number of iterations increases, the control parameter gradually decreases from 2 to 0; D = |C×Xp(t)-X(t)|, C = 2×r2, r2 is a random number between [0,1]. It should be noted that in order to achieve synchronization between the contraction and wrapping mechanism and the spiral update position, a probability factor (denoted as p) is introduced to determine the position update method executed by the whale, and the probability factor p is a random number in the interval [0,1].

[0072] When the absolute value of the coefficient vector of this iteration is less than 1 and the probability factor is greater than or equal to 0.5, the spiral update phase begins. For any individual whale, the position of the individual whale is updated using the following formula: X(t+1)=w(t)×Xp(t)+D×e bl ×cos(2πl); where b is a constant used to limit the shape of the logarithmic spiral, and l is a random number between [-1,1].

[0073] When the absolute value of the coefficient vector of this iteration is greater than or equal to 1, the prey search phase begins. For any individual whale, the position of the individual whale is updated using the following formula: X(t+1)=r3×(X'(t)-X(t))+r4×(X'(t)-X(t)); where r3 and r4 are random numbers between [0,1], X(t) is the position of the individual whale in this iteration; and X'(t) is the position of a whale randomly selected from the target population of this iteration.

[0074] Among them, the weight coefficient of this iteration is:

[0075] In order to verify the optimization effect of the improved whale algorithm, the inventor selected 11 single-peak basis functions and 8 multi-peak basis functions in the current test standard CEC2010 to verify the computing power of the ADWOA algorithm; the selected test functions are: Sphere function, Tablet function, StepFun function, SchwefelsP222 function, SchwefelsP221 function, Schaffer function, Rosenbrock function, Quadric function, High Conditioned Elliptic function, Discus function, BentCigar function, Ackley function, Griewank function, Rastrigrin function, RastrigrinNon function, Penalized1 function, Penalized2 function, Dminima function and Schwefels function. In addition, for comparison, the inventor also simulated some other intelligent algorithms, including: Whale Optimization Algorithm (WOA), Particle Swarm Optimization Algorithm (PSO), Augmented Lagrangian Particle Swarm Optimization Algorithm (ALPSO) and Global Optimal Prediction Based Adaptive Mutation Particle Swarm Optimization Algorithm (GPAMPSO) and other intelligent algorithms. In this simulation experiment, the parameters are set as follows: the dimension is 30, the population size is 30, and the maximum number of iterations is 1000. In order to overcome the randomness of the intelligent algorithm, each algorithm is executed independently 20 times.

[0076] The experiment shows that the ADWOA algorithm is superior to other intelligent algorithms in terms of convergence value and convergence speed. The test results of unimodal and multimodal functions show that the mean and variance of the ADWOA algorithm are significantly smaller than those of other intelligent algorithms, indicating that the optimal value obtained by the algorithm is highly stable and robust, and can be effectively implemented to prevent premature convergence. In order to further evaluate the performance of the metaheuristic algorithm and prove that the ADWOA algorithm is significantly better than other intelligent algorithms, a non-parametric statistical test was also performed using the Wilcoxon rank sum test with a significance level of α=5%. It is generally believed that when P<5%, the difference is significant, and when P≥5%, there is no significant difference. The test results show that the P value of ADWOA is less than 5% compared with any of the above-mentioned intelligent algorithms, indicating that the search ability of ADWOA is significantly enhanced.

[0077] On the basis of the above-mentioned implementation modes, optionally, when applied to the microgrid optimization scheduling method, the improved whale optimization algorithm is used in combination with the microgrid system simulation model to optimize the active power of each generator in the microgrid system, which specifically includes the following steps:

[0078] 1) Generate an initial whale population, initialize the position of each whale individual in the initial whale population, record the number of iterations as 1, and use the initial whale population as the target population for an iterative calculation.

[0079] Among them, the position of the individual whale is the value of the optimization variable, and the optimization variable includes the active power of each generator.

[0080] 2) Determine whether the number of iterations reaches the preset number; if so, end the optimization process and obtain the active power of each generator in the microgrid system according to the prey position of this iteration; if not, update the position of each whale individual according to the position of each whale individual in this iteration, the prey position of this iteration, the probability factor, the coefficient vector of this iteration and the weight coefficient of this iteration to generate an updated population, add 1 to the number of iterations, and use the updated population as the target population for an iterative calculation.

[0081] The specific process of iterative calculation includes:

[0082] 1) Substitute the position of each whale individual in the target population into the microgrid system simulation model; for any whale individual, calculate the fitness value of the whale individual when the operation constraints and system power constraints are met based on the position of the whale individual and the output of the microgrid system simulation model.

[0083] The fitness function is equal to the objective function. The fitness value of a whale individual is the objective function value obtained by substituting the optimization variable corresponding to the whale individual into the objective function, that is, the operating cost required when the active power of each generator corresponding to the whale individual is running.

[0084] 2) According to the fitness value of each whale individual, search for the optimal fitness value of each whale individual in the target population, and use the position of the whale individual with the optimal fitness value as the prey position of this iteration.

[0085] Among them, the fitness value of the whale individual with the lowest fitness value in the target population is the optimal fitness value.

[0086] In summary, an embodiment of the present invention provides a microgrid optimization scheduling method based on ADWOA. The algorithm can solve non-convex, nonlinear constrained, and high-dimensional optimization problems. When an individual surrounds prey or performs spiral updates, an adaptive update weight strategy is used to update the position of the individual. Then, it is updated again using a random difference mutation strategy. The optimal position is then selected based on the updated result, which speeds up the overall convergence speed and prevents it from falling into a local optimum.

[0087] To verify the feasibility of the above microgrid optimization scheduling method, the embodiment of the present invention uses and analyzes a 3-machine 6-node system calculation example, that is, a 6-node microgrid system including three generators. Table 1 shows the consumption characteristics and active power limit value of each generator.

[0088] Table 1

[0089] i <![CDATA[a i ]]> <![CDATA[b i ]]> <![CDATA[c i ]]> <![CDATA[P i min / MW]]> <![CDATA[P i max / MW]]> 1 0.00156 7.92 561 100 600 2 0.00194 7.85 310 100 400 3 0.00482 7.97 78 50 200

[0090] Assume the following corresponding parameter values: population size n = 30, preset number of iterations Max_iter = 100. During the analysis, the following two cases are independently displayed: Case 1: The total load P carried by the generator L =500MW; Case 2: Total load P carried by the generator L =850MW.

[0091] In order to determine the effectiveness of the ADWOA algorithm in practical applications, the GPAMPSO, ALPSO, WOA and PSO algorithms are compared with the ADWOA algorithm. The optimization results of case 1 are shown in Table 2, and the optimization results of case 2 are shown in Table 3; Figure 2 and Figure 3 Describes how the fitness value changes with the number of iterations. It can be understood that Figure 2 and Figure 3 The fitness value in represents the optimal running cost calculated at each iteration.

[0092] Table 2

[0093] algorithm <![CDATA[P1 / MW]]> <![CDATA[P2 / MW]]> <![CDATA[P3 / MW]]> Running cost / $ ADWOA 297.3 100 102.7 5140 WOA 200.1 99.99 200 5182 PSO 198.5 201.5 99.9 5266 GPAMPSO 296.1 100 103.9 5167 ALPSO 199.8 173.9 126.2 5251

[0094] Table 3

[0095] algorithm <![CDATA[P1 / MW]]> <![CDATA[P2 / MW]]> <![CDATA[P3 / MW]]> Running cost / $ ADWOA 400 400 50 8233 WOA 406.2 393.8 50 8352 PSO 501.3 238.1 110.5 8457 GPAMPSO 396 323.4 130.7 8372 ALPSO 397.8 301.9 150.2 8370

[0096] Combined with Table 2, Table 3, Figure 2 and Figure 3 It can be seen that the WOA, PSO, ALPSO and GPAMPSO algorithms are all prone to fall into the local optimal state, resulting in relatively high operating costs, while the ADWOA algorithm is more robust and stable and can achieve lower costs.

[0097] According to the above specific embodiments, when the ADWOA algorithm is applied to the optimization problem of economic load distribution of a microgrid system with 3 machines and 6 nodes, the simulation results of actual complex problems further show that compared with other intelligent algorithms, ADWOA has better performance, which can reduce the cost of using the ELD problem and achieve the best economic benefits.

[0098] An embodiment of the present invention further provides a microgrid optimization scheduling device, which is used to execute the microgrid optimization scheduling method provided by any embodiment of the present invention, and has corresponding beneficial effects. Figure 4 Schematic diagram of a microgrid optimization dispatching device provided by an embodiment of the present invention. Figure 4 The microgrid optimization dispatching device includes: a model building module 341 and an optimization module 342. The model building module 341 is used to establish a microgrid system simulation model. The optimization module 342 is used to optimize the active power of each generator in the microgrid system based on the generator operation constraints and the system power constraints, with the lowest operation cost of the microgrid system as the optimization goal, using the improved whale optimization algorithm, and combining the microgrid system simulation model.

[0099] On the basis of the above-mentioned embodiments, optionally, the optimization module 342 is specifically used to: generate an initial whale population, initialize the position of each individual whale in the initial whale population, record the number of iterations as 1, and perform an iterative calculation on the initial whale population as the target population; wherein the position of the individual whale is the value of the optimization variable, and the optimization variable includes the active power of each generator; determine whether the number of iterations reaches the preset number; if so, end the optimization process, and obtain the active power of each generator in the microgrid system according to the prey position of this iteration; if not, update the position of each individual whale according to the position of each individual whale in this iteration, the prey position of this iteration, the probability factor, the coefficient vector of this iteration and the weight coefficient of this iteration to generate an updated population, add 1 to the number of iterations, and perform an iterative calculation on the updated population as the target population.

[0100] The iterative calculation includes:

[0101] 1) Substitute the position of each whale individual in the target population into the microgrid system simulation model; for any whale individual, calculate the fitness value of the whale individual when the operation constraints and system power constraints are met based on the position of the whale individual and the output of the microgrid system simulation model.

[0102] 2) According to the fitness value of each whale individual, search for the optimal fitness value of each whale individual in the target population, and use the position of the whale individual with the optimal fitness value as the prey position of this iteration.

[0103] An embodiment of the present invention further provides an electronic device for executing the microgrid optimization scheduling method provided by any embodiment of the present invention, and has corresponding beneficial effects. Exemplarily, the electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid optimization scheduling method provided by any embodiment of the present invention.

[0104] An embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions. The computer instructions are used to enable a processor to implement the microgrid optimization scheduling method provided by any embodiment of the present invention when executed, and have corresponding beneficial effects.

[0105] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0106] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a microgrid optimization scheduling method.

[0109] In some embodiments, the microgrid optimization scheduling method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the microgrid optimization scheduling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the microgrid optimization scheduling method in any other appropriate manner (e.g., by means of firmware).

[0110] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0112] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0114] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0115] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0116] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0117] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A microgrid optimization scheduling method, characterized in that: include: Establish a microgrid system simulation model; Based on the generator operation constraints and system power constraints, with the lowest operation cost of the microgrid system as the optimization goal, the improved whale optimization algorithm is used in combination with the microgrid system simulation model to optimize the active power of each generator in the microgrid system.

2. The microgrid optimization scheduling method according to claim 1, characterized in that: The objective function for optimizing the active power of each generator in the microgrid system is: Where C(P) is the operating cost of the microgrid system, n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, and P i is the active power of the i-th generator, F i (P i ) is when the active power of the i-th generator is P i The energy consumption per unit time when i (P i )=a i P i 2 +b i P i +c i , a i 、b i and c i Represents the power generation consumption parameters of the i-th generator.

3. The microgrid optimization scheduling method according to claim 1, characterized in that: The generator operation constraints are: P i min ≤P i ≤P i max ,i=1、2、…、n; Where n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, P i is the active power of the i-th generator, P i min and P i max are the minimum and maximum active power of the i-th generator respectively.

4. The microgrid optimization scheduling method according to claim 1, characterized in that: The system power constraint condition includes: at least one of a power balance constraint and a line power constraint; The power balance constraint is: Where n is the number of generators in the microgrid system, n is a positive integer greater than or equal to 2, P i is the active power of the i-th generator, P L is the total load in the microgrid system, P S is the total net loss of the microgrid system; The line power constraint is: P j min ≤P j ≤P j max , j = 1, 2, ..., m; Where m is the number of lines in the microgrid system, m is a positive integer greater than or equal to 2, P j is the power transmitted on the jth line, P j min and P j max are the lower and upper limits of the transmission power on the jth line respectively.

5. The microgrid optimization scheduling method according to claim 1, characterized in that: The improved whale optimization algorithm is used in combination with the microgrid system simulation model to optimize the active power of each generator in the microgrid system, including: Generate an initial whale population, initialize the position of each individual whale in the initial whale population, record the number of iterations as 1, and perform an iterative calculation using the initial whale population as the target population; wherein the position of the individual whale is the value of the optimization variable, and the optimization variable includes the active power of each of the generators; Determine whether the number of iterations reaches the preset number; If yes, the optimization process ends, and the active power of each generator in the microgrid system is obtained according to the prey position of this iteration; If not, the positions of the individual whales are updated according to the positions of the individual whales in this iteration, the positions of the prey in this iteration, the probability factor, the coefficient vector of this iteration and the weight coefficient of this iteration to generate an updated population, the number of iterations is increased by 1, and the updated population is used as the target population for an iterative calculation; Wherein, the iterative calculation includes: Substituting the position of each whale individual in the target population into the microgrid system simulation model; for any of the whale individuals, calculating the fitness value of the whale individual when it is determined that the operation constraint condition and the system power constraint condition are satisfied according to the position of the whale individual and the output of the microgrid system simulation model; According to the fitness value of each of the whale individuals, the optimal fitness value of each of the whale individuals in the target population is searched, and the position of the whale individual with the optimal fitness value is used as the prey position of this iteration.

6. The microgrid optimization scheduling method according to claim 5, characterized in that: According to the position of each individual whale in this iteration, the position of the prey in this iteration, the probability factor, the coefficient vector of this iteration and the weight coefficient of this iteration, the position of each individual whale is updated to generate an updated population, including: When the absolute value of the coefficient vector of this iteration is less than 1 and the probability factor is less than 0.5, the circling hunting phase is entered, and for any of the individual whales, the position of the individual whale is updated using the following formula: X(t+1)=w(t)×Xp(t)-A×D; Wherein, t represents the number of iterations of this iteration; X(t+1) is the updated position of the individual whale; w(t) is the weight coefficient of this iteration; Xp(t) is the prey position of this iteration; A is the coefficient vector of this iteration, A=2a(t)×r1-a(t), Max_iter is the preset number of times; D = |C×Xp(t)-X(t)|, C = 2×r2, r2 is a random number between [0,1]; When the absolute value of the coefficient vector of this iteration is less than 1 and the probability factor is greater than or equal to 0.5, the spiral update phase is entered, and for any of the individual whales, the position of the individual whale is updated using the following formula: X(t+1)=w(t)×Xp(t)+D×e bl ×cos(2πl); Where b is a constant used to limit the shape of the logarithmic spiral, and l is a random number between [-1,1]; When the absolute value of the coefficient vector of this iteration is greater than or equal to 1, the prey search phase is entered, and for any of the individual whales, the position of the individual whale is updated using the following formula: X(t+1)=r3×(X'(t)-X(t))+r4×(X'(t)-X(t)); Among them, r3 and r4 are both random numbers between [0,1], X(t) is the position of the whale individual in this iteration; X'(t) is the position of the whale individual randomly selected from the target population of this iteration.

7. The microgrid optimization scheduling method according to claim 6, characterized in that: The weight coefficient of this iteration is:

8. A microgrid optimization dispatching device, characterized in that: include: A model building module is used to build a microgrid system simulation model; The optimization module is used to optimize the active power of each generator in the microgrid system based on the generator operation constraints and the system power constraints, with the lowest operation cost of the microgrid system as the optimization goal, using the improved whale optimization algorithm, combined with the microgrid system simulation model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid optimization scheduling method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the microgrid optimization scheduling method according to any one of claims 1 to 7 when executed.

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