Industrial park comprehensive energy intelligent management and control method and device

Abstract

The present invention provides a method and device for intelligent comprehensive energy management and control of industrial parks, the method comprising: obtaining a variety of energy station management and control schemes; constructing an objective function with the goal of maximizing the benefits of the energy station; determining the constraints of the objective function according to the construction cost matrix of the energy station and the energy shortage matrix; using a swarm intelligence algorithm to calculate the value of the objective function under each energy station management and control scheme according to the constraints, and taking the energy station management and control scheme corresponding to the maximum value of the objective function as the optimal energy station management and control scheme. The method proposed in the present invention is aimed at the problem that the existing industrial parks fail to balance the construction cost and energy demand during the construction of the comprehensive energy management and control center site. By comprehensively considering the construction cost of the energy management and control center and the shortage of different energy sources, an optimal energy center site management and control scheme is obtained, which reduces the construction cost of the energy management and control center and guarantees the needs of park users for various energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy management and control of industrial parks, and in particular to an integrated energy intelligent management and control method and device for industrial parks. Background Art

[0002] As an innovative energy management model that has emerged in recent years, the integrated energy system can transform gas and various renewable resources into water, heat, electricity and other forms of resources required by residents on the demand side, and realize coordinated planning and efficient operation among various heterogeneous energy subsystems. More specifically, in the integrated energy system of the industrial park, multiple energy sources are deeply coupled to complete the refined management and application of the fixed quota and quantity of water, electricity, heat and gas, so that the energy production, transmission, distribution, conversion and storage in the park can achieve the optimal energy management and energy operation scheduling, realize the two-way flow of multiple energy sources and the energy complementarity between multiple types of energy, which can efficiently meet the different needs of residents in the park for water, heat, electricity, gas and other energy sources. This energy utilization method of the integrated energy system can make full use of renewable energy, reduce the use of fossil energy and energy costs, and improve the absorption capacity of renewable energy.

[0003] The construction costs of control centers for different types of energy are different, and the urgency of residents in different areas' needs for different energy sources is also different. The current serious problem is that the relationship between the construction cost of the energy control center and energy demand has not been well considered during the construction of the existing comprehensive energy control center in industrial parks. The above problems will make it impossible for the energy control center, which has been built at a high cost, to achieve maximum efficiency, and the park users will not be able to obtain the urgently needed energy well. Therefore, in order to avoid unnecessary waste of manpower and material costs and meet the energy needs of park users, it is urgent to improve the traditional comprehensive energy control methods of industrial parks.

[0004] The existing methods applied to the integrated energy intelligent management and control of industrial parks mainly include the use of classic meta-heuristic algorithms such as genetic algorithms and particle swarm algorithms to solve a management and control solution that meets the requirements. For example, a method for integrated energy intelligent management and control based on particle swarm algorithm searches for the solution space of the integrated energy management and control problem by changing the speed and position of the particle swarm. However, the particle swarm algorithm has the problems of unstable convergence and low solution accuracy, which greatly affects the reliability of the management and control solution. Summary of the invention

[0005] The present invention provides an industrial park comprehensive energy intelligent management and control method and device, which are used to solve the defect that the energy management and control method of the industrial park in the prior art fails to balance the relationship between the construction cost of the control center site and the user's demand for energy, resulting in unnecessary waste of manpower and material resources, and realize the efficient operation of the energy system.

[0006] The present invention provides an industrial park comprehensive energy intelligent management and control method, comprising:

[0007] Obtain various energy station management and control solutions;

[0008] Taking the maximum profit of the energy station as the goal, construct the objective function;

[0009] According to the energy station construction cost matrix and energy shortage matrix, determine the constraint conditions of the objective function;

[0010] A swarm intelligence algorithm is used to calculate the value of the objective function under each energy station control scheme according to the constraints, and the energy station control scheme corresponding to the maximum value of the objective function is taken as the optimal energy station control scheme.

[0011] According to a comprehensive energy intelligent management and control method for an industrial park provided by the present invention, the swarm intelligence algorithm is used to calculate the value of the objective function under each energy station management and control scheme according to the constraint conditions, and the energy station management and control scheme corresponding to the maximum value of the objective function is used as the optimal energy station management and control scheme, including:

[0012] An improved spotted hyena optimization algorithm is adopted, and the fitness value of the spotted hyena individual is corresponded to the constraint condition, and the multiple energy station control schemes are corresponded to multiple spotted hyena individual positions. The best spotted hyena individual position is obtained as the prey position by optimizing the spotted hyena individual position, and the optimal energy station control scheme is obtained. The optimal energy station control scheme corresponds to the prey position.

[0013] According to a method for intelligent management and control of comprehensive energy in an industrial park provided by the present invention, the optimization of the individual positions of spotted hyenas includes:

[0014] The adaptively updated sway factor and control factor are used to optimize the individual position of spotted hyenas.

[0015] According to an industrial park comprehensive energy intelligent management and control method provided by the present invention, the optimization of the individual positions of the spotted hyenas also includes:

[0016] The G individuals with the best fitness values ​​in the spotted hyena population of the last iteration are retained in the elite pool, and the top G individuals are selected from the elite pool and put into the initial population of the current iteration, where G is a positive integer.

[0017] According to the integrated energy intelligent management and control method for an industrial park provided by the present invention, the spotted hyena population size in the improved spotted hyena optimization algorithm is 60, and the maximum number of iterations is 100.

[0018] According to an industrial park comprehensive energy intelligent management and control method provided by the present invention, the number of energy stations is greater than or equal to 8, and the number of energy types is greater than or equal to 4.

[0019] The present invention also provides an industrial park comprehensive energy intelligent management and control device, comprising:

[0020] Acquisition module, used to obtain various energy station management and control solutions;

[0021] A construction module is used to construct an objective function with the goal of maximizing the benefits of the energy station;

[0022] A determination module is used to determine the constraint conditions of the objective function according to the construction cost matrix of the energy station and the energy shortage matrix;

[0023] The selection module is used to use a swarm intelligence algorithm to calculate the value of the objective function under each energy station control plan according to the constraints, and take the energy station control plan corresponding to the maximum value of the objective function as the optimal energy station control plan.

[0024] According to an industrial park comprehensive energy intelligent management and control device provided by the present invention, the selection module includes:

[0025] An improved spotted hyena optimization algorithm is adopted, and the fitness value of the spotted hyena individual is corresponded to the constraint condition, and the multiple energy station control schemes are corresponded to multiple spotted hyena individual positions. The best spotted hyena individual position is obtained as the prey position by optimizing the spotted hyena individual position, and the optimal energy station control scheme is obtained. The optimal energy station control scheme corresponds to the prey position.

[0026] According to an industrial park integrated energy intelligent management and control device provided by the present invention, the optimization through the individual positions of spotted hyenas in the selection module includes:

[0027] The adaptively updated sway factor and control factor are used to optimize the individual position of spotted hyenas.

[0028] According to an industrial park comprehensive energy intelligent management and control device provided by the present invention, the selection module also includes an elite strategy submodule, which is used to retain the G individuals with the best fitness values ​​in the spotted hyena population of the last iteration into the elite pool, and select the best top G individuals from the elite pool and put them into the initial population of the current iteration, where G is a positive integer.

[0029] The present invention provides an industrial park comprehensive energy intelligent management and control method and device, which can effectively reduce the construction cost of the control center site and improve the user's energy use satisfaction by comprehensively considering the construction cost of different types of energy control center sites and the urgency of user demand for different types of energy, and has good promotion prospects. The method aims to balance construction costs and energy demand, selects energy sites that meet user needs and have the lowest cost for different areas of the industrial park for construction, and has the advantages of clear steps, stable performance, simple operation, and high solution quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 This is one of the flow diagrams of the industrial park energy site management and control method provided by an embodiment of the present invention;

[0032] Figure 2 This is the second flow chart of the industrial park energy site management and control method provided by the embodiment of the present invention;

[0033] Figure 3 It is one of the overall benefit value comparison curves of the method of the embodiment of the present invention, the simulated annealing algorithm and the particle swarm algorithm;

[0034] Figure 4 This is the second graph comparing the overall benefits of the method according to the embodiment of the present invention with the simulated annealing algorithm and the particle swarm algorithm.

[0035] Figure 5 It is a structural schematic diagram of an industrial park energy site management and control system provided by an embodiment of the present invention;

[0036] Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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 are within the scope of protection of the present invention.

[0038] In an embodiment of the present invention, according to the elastic selection parameters of the current benefit value of the integrated energy site control scheme of the industrial park, it is planned to build an integrated energy control center site in the industrial park that meets the interests of all parties to the greatest extent. Before the integrated energy center site control is carried out, the specific energy control center site construction location has been determined, and the question that needs to be considered is what type of energy control center site to build. In the integrated energy intelligent control method of the industrial park, it mainly involves two factors, namely the construction cost of different types of energy control center sites and the urgency of users' needs for different energy sources. Generally speaking, the lower the cost of the established energy control center site and the more user needs it can meet, the better the effect of the scheme. Due to different regional terrain factors, the cost of building the same type of energy control center site in different regions is also different. Therefore, the present invention organizes and normalizes the construction costs of various types of energy control center sites in different regions in advance, and normalizes and stores the urgency of energy needs of users in different regions as constraints, and then uses the group intelligence algorithm to solve the set objective function.

[0039] Combine the following Figure 1-Figure 4 The present invention describes an industrial park integrated energy intelligent management and control method, including:

[0040] Step 101: Obtain multiple energy station management and control solutions;

[0041] Step 102, constructing an objective function with the goal of maximizing the revenue of the energy station;

[0042] Step 103: Determine the constraint conditions of the objective function according to the energy station construction cost matrix and the energy shortage degree matrix;

[0043] Step 104: Use a swarm intelligence algorithm to calculate the value of the objective function under each energy station control scheme according to the constraint conditions, and take the energy station control scheme corresponding to the maximum value of the objective function as the optimal energy station control scheme.

[0044] It should be noted that the energy station can be an energy control center site, and the maximum benefit of the energy station means that the construction of the industrial park is in the best interests of all parties. The construction cost matrix of the energy station is obtained by evaluating the construction costs of different energy control centers in the industrial park, and the energy shortage degree matrix is ​​determined by field research and based on the needs of residents, such as in the form of a questionnaire.

[0045] The industrial park comprehensive energy intelligent management and control method of the embodiment of the present invention aims to solve the problem that existing industrial parks fail to strike a good balance between construction costs and energy demands during the construction of comprehensive energy management and control center sites. By comprehensively considering the construction cost of the energy management and control center and the scarcity of different energy sources, an optimal energy center site management plan is obtained, which reduces the construction cost of the energy management and control center and ensures the needs of park users for various energy sources.

[0046] In at least one embodiment of the present invention, the method of using a swarm intelligence algorithm to calculate the value of the objective function under each energy station control scheme according to the constraint conditions, and taking the energy station control scheme corresponding to the maximum value of the objective function as the optimal energy station control scheme, includes:

[0047] An improved spotted hyena optimization algorithm is adopted, and the fitness value of the spotted hyena individual is corresponded to the constraint condition, and the multiple energy station control schemes are corresponded to multiple spotted hyena individual positions. The best spotted hyena individual position is obtained as the prey position by optimizing the spotted hyena individual position, and the optimal energy station control scheme is obtained. The optimal energy station control scheme corresponds to the prey position.

[0048] The industrial park comprehensive energy intelligent management and control method of the embodiment of the present invention introduces elite strategy and adaptive mechanism to improve the traditional spotted hyena optimization algorithm, effectively improving the computational efficiency of the algorithm, accelerating the convergence speed of the algorithm, and improving the quality of the obtained comprehensive energy management and control solution.

[0049] It should be noted that if Figure 2 As shown in the figure, the complete algorithm flow of the improved spotted hyena optimization algorithm includes the following steps:

[0050] Step 210: Real number encoding is performed on the spotted hyena individual D in the improved spotted hyena optimization algorithm, and a decimal sequence is used in the encoding to represent a feasible integrated energy intelligent management and control method for an industrial park;

[0051] It should be noted that, assuming that there are four types of comprehensive energy that need to be managed in the industrial park, namely water, heat, electricity and gas, there are 8 energy management and control center sites that need to be built in the divided areas. In addition, the number 1 represents water energy, the number 2 represents heat energy, the number 3 represents electricity, and the number 4 represents gas energy; then a feasible coding sequence is [2,3,1,1,2,3,4,2], which means that a heat energy management and control center is planned to be built at the first designated location, and an electricity energy management and control center is planned to be built at the second designated location, and the subsequent sequences are similar.

[0052] Step 220, initializing the parameters of the improved spotted hyena optimization algorithm, including the size N of the spotted hyena population, the location P of the spotted hyena population, the number S of energy management and control centers in the industrial park integrated energy system, the swing factor B, the convergence factor E, the control factor z, the maximum number of iterations MaxIt, and the current number of iterations It;

[0053] Set the size of the spotted hyena population to N = 60, the maximum number of iterations MaxIt = 100, the position of the spotted hyena population is randomly generated, B is initially a random value between [0, 2], E is initially a random value between [0, 10], and z is initially 5;

[0054] Step 230, obtaining the spotted hyena individual with the highest fitness value and the initial best spotted hyena individual position corresponding to the spotted hyena individual with the highest fitness value;

[0055] Step 240: The individual spotted hyena updates its current position as the prey moves. The current position of the individual spotted hyena includes the distance between the prey and the spotted hyena and the position vector of the prey. The updating formulas are shown in equations (1) and (2):

[0056]

[0057]

[0058] in represents the distance between the prey and the spotted hyena, It represents the current number of iterations, and represents the coefficient vector, represents the position vector of the prey;

[0059] Step 250, the prey hunting stage; the individual position of the best spotted hyena is determined to be the location of the prey, and the search individuals of other spotted hyenas form a group, all moving towards the position of the better individual, and retaining the best value updated at present. The update formulas in the prey hunting stage are shown in equations (3)-(6):

[0060]

[0061]

[0062]

[0063]

[0064] in, is the initial optimal individual position of spotted hyena, is the random position of other spotted hyenas, N defines the number of spotted hyenas, The value range of the vector is [0.5,1], S is the number of solutions, Represents the aggregation of N optimal solutions;

[0065] Step 260, attacking prey stage: After completing the process of chasing the prey, the spotted hyena population begins to attack the prey. The formula for attacking the prey is shown in formula (7):

[0066]

[0067] Step 270, check whether the updated individual position of the spotted hyena is out of bounds, if so, make corresponding adjustments, and then recalculate the fitness value of each individual in the spotted hyena population; the out of bounds refers to whether the individual change range exceeds the constraint condition;

[0068] Step 280: Use the elite strategy to keep the G individuals with the best fitness value in the current spotted hyena population into the elite pool, and select the best top G individuals from the elite pool and put them into the initial population of the next iteration process;

[0069] Step 290, determine whether the number of iterations It is greater than the maximum number of iterations MaxIt. If so, terminate the spotted hyena optimization algorithm and output the sequence carried by the globally optimal spotted hyena individual as the optimal solution for comprehensive energy management of the industrial park; otherwise, It=It+1, and return to step S230.

[0070] It should be noted that step 230 specifically includes:

[0071] Step 231: Calculate the fitness value of the control scheme sequence carried by the spotted hyena individual according to the fitness function; in the problem of comprehensive energy intelligent control in the industrial park, the fitness function of the improved spotted hyena optimization algorithm is set to:

[0072] fitness=W*P * ;

[0073] Where W is the construction cost matrix of different types of energy control centers, and P is the scarcity matrix of different types of energy;

[0074] Step 232, sorting the spotted hyena population according to the size of fitness;

[0075] Step 233: record the spotted hyena individual with the best fitness value.

[0076] It should be noted that step 280 specifically includes:

[0077] Step 281, calculating the fitness value of the updated spotted hyena population according to the fitness function;

[0078] Step 282, arranging the spotted hyena individuals in ascending order according to fitness;

[0079] Step 283, in order to avoid the phenomenon that the traditional elite strategy only retains the individuals with the best fitness value, which will cause the spotted hyena population to fall into premature convergence, in an improved spotted hyena optimization algorithm provided by the present invention, G=4, it is necessary to select the first 4 spotted hyena individuals with the highest fitness and put them into the elite pool, and select the 4 individuals with the highest fitness from the elite pool and put them into the initial population of the next round of iteration;

[0080] In at least one embodiment of the present invention, the optimization through the individual positions of spotted hyenas includes:

[0081] The adaptively updated swing factor and control factor are used to optimize the individual position of spotted hyenas. and control factors The formula for adaptive update is as follows:

[0082]

[0083]

[0084] Where f represents the fitness value of the current spotted hyena individual, f avg represents the average fitness value of each generation population, f max Indicates the maximum fitness value of each generation population, B max and B min The swing factors The maximum and minimum values ​​of E max and E min Represents the control factors The maximum and minimum values ​​of ;

[0085] In at least one embodiment of the present invention, the optimization of the individual positions of the spotted hyenas further comprises:

[0086] The G individuals with the best fitness values ​​in the spotted hyena population of the last iteration are retained in the elite pool, and the top G individuals are selected from the elite pool and put into the initial population of the current iteration, where G is a positive integer.

[0087] In at least one embodiment of the present invention, the size of the spotted hyena population in the improved spotted hyena optimization algorithm ranges from 50 to 100, and the maximum number of iterations is 100 to 500.

[0088] Preferably, the spotted hyena population size is 60 and the maximum number of iterations is 100.

[0089] In at least one embodiment of the present invention, the number of energy stations is greater than or equal to 8, and the number of energy types is greater than or equal to 4. In this embodiment, there are 8 energy management and control center sites to be built in the industrial park, and the comprehensive energy involved includes 4 types of water, heat, electricity, and gas. The construction cost matrix is:

[0090]

[0091] The energy demand urgency matrix is:

[0092]

[0093] The row numbers of the two matrices represent the four types of energy, namely water, heat, electricity and gas, and the column numbers represent the site numbers of the energy control center to be built. For example, the first column represents the control center site numbered 1.

[0094] Suppose there are three spotted hyena individuals, and the coding sequence of spotted hyena individual 1 is [2,3,1,1,2,3,4,1], the coding sequence of spotted hyena individual 2 is [4,2,1,1,2,3,3,4], and the coding sequence of spotted hyena individual 3 is [1,2,2,4,3,2,4,4].

[0095] The construction cost sequence W of spotted hyena individual 1 1 for:

[0096] [0.27,0.25,0.24,0.27,0.14,0.20,0.38,0.23];

[0097] Energy urgency sequence P of spotted hyena individual 1 1 for:

[0098] [0.37,0.35,0.34,0.17,0.14,0.10,0.38,0.12];

[0099] Construction cost sequence W of spotted hyena individual 2 2 for:

[0100] [0.18,0.32,0.24,0.27,0.14,0.20,0.21,0.18];

[0101] Energy urgency sequence P of spotted hyena individual 2 2 for:

[0102] [0.28,0.22,0.34,0.17,0.14,0.10,0.21,0.29];

[0103] Construction cost sequence W of spotted hyena individual 3 3 for:

[0104] [0.35,0.32,0.28,0.31,0.36,0.19,0.38,0.18];

[0105] Energy urgency sequence P of spotted hyena individual 3 3 for:

[0106] [0.25,0.22,0.30,0.31,0.26,0.29,0.38,0.29];

[0107] The fitness value of spotted hyena individual 1 is calculated according to the fitness function formula in step S231. 1 =0.5265; the fitness value of spotted hyena individual 2 is fitness 2 =0.3842; the fitness value of spotted hyena individual 3 is fitness 3 =0.6833.

[0108] It can be seen that the solution of spotted hyena individual 3 is the best among the three, so the site sequence of the integrated energy control center carried by spotted hyena individual 3 should be selected as the integrated energy control solution of the industrial park. From the code [1,2,2,4,3,2,4,4] of spotted hyena individual 3, it can be found that a water energy control site is built in the first established area, a heat energy control site is built in the second established area, and so on. The eighth established area is built. Natural gas energy control site.

[0109] After considering the construction cost of the integrated energy management and control center site and the user's demand for various energy sources, the fitness value of the management and control scheme corresponding to spotted hyena individual 3 is 0.6833.

[0110] The detailed parameter configuration and simulation results of the computer simulation experiment of the comprehensive energy intelligent management and control method of the industrial park in the embodiment of the present invention are given below.

[0111] Example 1

[0112] In the embodiment of the present invention, there are 8 integrated energy control center sites and 4 different types of energy. The integrated energy system center of the industrial park has evaluated the construction costs of different energy control center sites in a given area and the urgency of users' demand for different energy sources in advance. The construction costs of the control center sites and the urgency of energy demand are stored in the form of matrices with the same structure. The row numbers of the matrix represent different energy sources, and the column numbers represent different regions. The number of spotted hyena individuals in the improved spotted hyena optimization algorithm is N=60, the maximum number of iterations MaxIt=100, and the top 4 individuals with the highest fitness are selected in each generation of the elite strategy and put into the elite pool. The population size of the simulated annealing algorithm and the particle swarm algorithm for comparison is 60, the maximum number of iterations is 100, the initial temperature of the simulated annealing algorithm is 300 degrees, the annealing coefficient is 0.96, the individual learning factor and social learning factor of the particle swarm algorithm are both 2, and the inertia weight is 0.8.

[0113] Figure 3 1 is a comparison curve of the benefits of the integrated energy management solution for industrial parks after 100 iterations of the method of the embodiment of the present invention and the simulated annealing algorithm and particle swarm algorithm in the prior art. Figure 3 As shown, the top curve is the benefit value curve obtained based on the improved spotted hyena optimization algorithm proposed in Example 1 of the present invention, the middle one is the life curve obtained by the particle swarm algorithm, and the following is the rotation life curve obtained by the simulated annealing algorithm. It can be seen from the figure that after 100 iterative operations, the comprehensive energy management and control benefit value of the industrial park finally obtained by the method described in Example 1 of the present invention is 0.89, the benefit value obtained by the particle swarm algorithm is 0.84, and the benefit value obtained by the simulated annealing algorithm is 0.75, that is, the performance of the algorithm of the present invention is 6% and 18% higher than that of the particle swarm algorithm and the simulated annealing algorithm in solving the control sequence, indicating that the comprehensive energy management and control sequence of the industrial park obtained by the method provided by the present invention can effectively balance the construction cost of the energy center site and the urgency of the user's demand for different energy sources. At the same time, it can be seen from the figure that compared with the particle swarm algorithm and the simulated annealing algorithm, the convergence speed of the method of the present invention is significantly faster, and the particle swarm algorithm and the simulated annealing algorithm fall into premature convergence during the iteration process, and cannot jump out of the local optimum, and the quality of the obtained control sequence is low, which is not conducive to actual adoption.

[0114] Example 2

[0115] In the embodiment of the present invention, there are 12 integrated energy control center sites and 4 different types of energy. The integrated energy system center of the industrial park has evaluated the construction costs of different energy control center sites in a given area and the urgency of users' demand for different energy sources in advance. The construction costs of the control center sites and the urgency of energy demand are stored in the form of matrices with the same structure. The row numbers of the matrix represent different energy sources, and the column numbers represent different regions. The number of spotted hyena individuals in the improved spotted hyena optimization algorithm is N=60, the maximum number of iterations MaxIt=200, and the top 4 individuals with the highest fitness are selected in each generation of the elite strategy and put into the elite pool. The population size of the simulated annealing algorithm and the particle swarm algorithm for comparison is 60, the maximum number of iterations is 200, the initial temperature of the simulated annealing algorithm is 300 degrees, the annealing coefficient is 0.96, the individual learning factor and social learning factor of the particle swarm algorithm are both 2, and the inertia weight is 0.8.

[0116] Figure 4 : is a comparison curve of the benefits of the industrial park comprehensive energy management solution of the method of Example 2 of the present invention relative to the simulated annealing algorithm and particle swarm algorithm in the prior art. Figure 4 As shown, the top curve is the benefit value curve obtained based on the improved spotted hyena optimization algorithm proposed in Example 2 of the present invention, the middle one is the life curve obtained by the particle swarm algorithm, and the following is the rotation life curve obtained by the simulated annealing algorithm. It can be seen from the figure that after 200 iterative operations, the comprehensive energy management and control benefit value of the industrial park finally obtained by the method described in Example 2 of the present invention is 0.98, the benefit value obtained by the particle swarm algorithm is 0.90, and the benefit value obtained by the simulated annealing algorithm is 0.82, that is, the performance of the algorithm of the present invention is 8% and 19% higher than that of the particle swarm algorithm and the simulated annealing algorithm in solving the control sequence, indicating that the comprehensive energy management and control sequence of the industrial park obtained by the method provided by the present invention can effectively balance the construction cost of the energy center site and the urgency of the user's demand for different energy sources. At the same time, it can be seen from the figure that the convergence speed of the method of the present invention is significantly faster than that of the particle swarm algorithm and the simulated annealing algorithm, and the particle swarm algorithm and the simulated annealing algorithm fall into the local optimal solution during the entire 200 iterations, and cannot jump out of the local optimal value, and the quality of the obtained control sequence is low, which is not conducive to actual adoption.

[0117] The following is a description of the integrated energy intelligent control device for industrial parks provided by the present invention. The integrated energy intelligent control device for industrial parks described below and the integrated energy intelligent control method for industrial parks described above can be referred to each other. Figure 5 The comprehensive energy intelligent control device for the industrial park shown includes:

[0118] An acquisition module 501 is used to acquire various energy station management and control solutions;

[0119] A construction module 502 is used to construct an objective function with the goal of maximizing the profit of the energy station;

[0120] A determination module 503 is used to determine the constraint conditions of the objective function according to the construction cost matrix of the energy station and the energy shortage degree matrix;

[0121] The selection module 504 is used to use a swarm intelligence algorithm to calculate the value of the objective function under each energy station control scheme according to the constraint conditions, and take the energy station control scheme corresponding to the maximum value of the objective function as the optimal energy station control scheme.

[0122] The industrial park comprehensive energy intelligent management and control device of the embodiment of the present invention aims to solve the problem that existing industrial parks fail to strike a good balance between construction cost and energy demand during the construction of comprehensive energy management and control center sites. By comprehensively considering the construction cost of the energy management and control center and the scarcity of different energy sources, an optimal energy center site management plan is obtained, which reduces the construction cost of the energy management and control center and ensures the needs of park users for various energy sources.

[0123] In at least one embodiment of the present invention, the selection module 504 includes:

[0124] An improved spotted hyena optimization algorithm is adopted, and the fitness value of the spotted hyena individual is corresponded to the constraint condition, and the multiple energy station control schemes are corresponded to multiple spotted hyena individual positions. The best spotted hyena individual position is obtained as the prey position by optimizing the spotted hyena individual position, and the optimal energy station control scheme is obtained. The optimal energy station control scheme corresponds to the prey position.

[0125] In at least one embodiment of the present invention, the optimization through the individual positions of spotted hyenas in the selection module 504 includes:

[0126] The adaptively updated sway factor and control factor are used to optimize the individual position of spotted hyenas.

[0127] In at least one embodiment of the present invention, the selection module also includes an elite strategy submodule, which is used to retain the G individuals with the best fitness values ​​in the spotted hyena population of the last iteration into the elite pool, and select the best top G individuals from the elite pool and put them into the initial population of the current iteration, where G is a positive integer.

[0128] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute a method for intelligent management and control of integrated energy in an industrial park, including:

[0129] Obtain various energy station management and control solutions;

[0130] Taking the maximum profit of the energy station as the goal, construct the objective function;

[0131] According to the energy station construction cost matrix and energy shortage matrix, determine the constraint conditions of the objective function;

[0132] A swarm intelligence algorithm is used to calculate the value of the objective function under each energy station control scheme according to the constraints, and the energy station control scheme corresponding to the maximum value of the objective function is taken as the optimal energy station control scheme.

[0133] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0134] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the industrial park comprehensive energy intelligent management and control method provided by the above methods, the method includes:

[0135] Obtain various energy station management and control solutions;

[0136] Taking the maximum profit of the energy station as the goal, construct the objective function;

[0137] According to the energy station construction cost matrix and energy shortage matrix, determine the constraint conditions of the objective function;

[0138] A swarm intelligence algorithm is used to calculate the value of the objective function under each energy station control scheme according to the constraints, and the energy station control scheme corresponding to the maximum value of the objective function is taken as the optimal energy station control scheme.

[0139] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the industrial park comprehensive energy intelligent management and control method provided by the above methods, the method comprising:

[0140] Obtain various energy station management and control solutions;

[0141] Taking the maximum profit of the energy station as the goal, construct the objective function;

[0142] According to the energy station construction cost matrix and energy shortage matrix, determine the constraint conditions of the objective function;

[0143] A swarm intelligence algorithm is used to calculate the value of the objective function under each energy station control scheme according to the constraints, and the energy station control scheme corresponding to the maximum value of the objective function is taken as the optimal energy station control scheme.

[0144] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent management and control of comprehensive energy in industrial parks, characterized in that: include: Obtain various energy station management and control solutions; Taking the maximum profit of the energy station as the goal, construct the objective function; According to the energy station construction cost matrix and energy shortage matrix, determine the constraint conditions of the objective function; A swarm intelligence algorithm is used to calculate the value of the objective function under each energy station control scheme according to the constraint conditions, and the energy station control scheme corresponding to the maximum value of the objective function is taken as the optimal energy station control scheme; The method of using a swarm intelligence algorithm to calculate the value of the objective function under each energy station control scheme according to the constraint conditions, and taking the energy station control scheme corresponding to the maximum value of the objective function as the optimal energy station control scheme, includes: The improved spotted hyena optimization algorithm is adopted, the fitness value of the spotted hyena individual corresponds to the constraint condition, the multiple energy station control schemes correspond to the multiple spotted hyena individual positions, the best spotted hyena individual position is obtained as the prey position by optimizing the spotted hyena individual position, and the optimal energy station control scheme is obtained, and the optimal energy station control scheme corresponds to the prey position; The optimization of the individual positions of the spotted hyenas also includes: The G individuals with the best fitness value in the last iteration of the spotted hyena population are retained in the elite pool, and the top G individuals are selected from the elite pool and put into the initial population of the current iteration, where G is a positive integer; The optimization of the individual positions of spotted hyenas includes: The adaptively updated sway factor and control factor are used to optimize the individual position of spotted hyenas.

2. The method for intelligent management and control of comprehensive energy in industrial parks according to claim 1 is characterized in that: In the improved spotted hyena optimization algorithm, the spotted hyena population size is 60 and the maximum number of iterations is 100.

3. The method for intelligent management and control of comprehensive energy in industrial parks according to any one of claims 1 to 2, characterized in that: The number of the energy stations is greater than or equal to 8, and the number of the types of energy is greater than or equal to 4.

4. An integrated energy intelligent management and control device for an industrial park, characterized in that: include: Acquisition module, used to obtain various energy station management and control solutions; A building module is used to build an objective function with the goal of maximizing the benefits of the energy station; A determination module is used to determine the constraint conditions of the objective function according to the construction cost matrix of the energy station and the energy shortage matrix; A selection module, for calculating the value of the objective function under each energy station control scheme according to the constraint conditions by using a swarm intelligence algorithm, and taking the energy station control scheme corresponding to the maximum value of the objective function as the optimal energy station control scheme; The method of using a swarm intelligence algorithm to calculate the value of the objective function under each energy station control scheme according to the constraint conditions, and taking the energy station control scheme corresponding to the maximum value of the objective function as the optimal energy station control scheme, includes: The improved spotted hyena optimization algorithm is adopted, the fitness value of the spotted hyena individual corresponds to the constraint condition, the multiple energy station control schemes correspond to the multiple spotted hyena individual positions, the best spotted hyena individual position is obtained as the prey position by optimizing the spotted hyena individual position, and the optimal energy station control scheme is obtained, and the optimal energy station control scheme corresponds to the prey position; The optimization of the individual positions of the spotted hyenas also includes: The G individuals with the best fitness value in the last iteration of the spotted hyena population are retained in the elite pool, and the top G individuals are selected from the elite pool and put into the initial population of the current iteration, where G is a positive integer; The optimization of the individual positions of spotted hyenas includes: The adaptively updated sway factor and control factor are used to optimize the individual position of spotted hyenas.

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