CN114580864B - Multi-element energy storage distribution method, system and equipment for comprehensive energy system - Google Patents
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Abstract
The application provides a multi-element energy storage distribution method, system and equipment for a comprehensive energy system. The multi-element energy storage distribution method for the comprehensive energy system comprises the following steps: determining initial multi-element energy storage cost minimum values of comprehensive energy sources in different functional areas based on the regional multi-element energy storage evaluation function; inputting the initial multi-element energy storage cost minimum value into a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost within the preset iteration number after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iteration calculation of the preset iteration number; and distributing comprehensive energy sources in different functional areas based on the optimized multi-element energy storage cost minimum value and the optimized multi-element energy storage proportion.
Description
Technical Field
The application relates to the field of comprehensive energy, in particular to a multi-element energy storage distribution method, a multi-element energy storage distribution system, electronic equipment and a storage medium for a comprehensive energy system.
Background
In recent years, due to the large use of fossil energy sources such as coal, petroleum, natural gas, etc., the global energy consumption demand continues to increase, and a series of problems such as energy shortage, serious environmental pollution, etc., become the difficulties and challenges that the global needs to cope with together. In this context, there is a worldwide urgent need for sustainable, cleaner, more efficient energy systems. In addition, the energy system with a large amount of applications in the market at present is to carry out overall arrangement and planning on single energy, and the traditional operation mode causes the problems of complex management, low energy utilization rate and the like of the energy supply system. In view of this, the prior art researches the coordinated utilization and collaborative operation of various energy sources, combines the energy sources with advanced technologies such as the internet, big data, information communication and the like, and develops a new energy system of the energy source internet. The comprehensive energy system is a physical carrier of the new energy system, and the problems of environmental pollution caused by fossil energy, low energy utilization rate of the traditional energy system and the like are efficiently solved through complementation and cascade utilization of cold, heat, electricity and gas energy. The integrated energy system is composed of a plurality of different types of energy, energy production equipment, energy conversion and energy storage equipment. The coordination, optimization and scheduling of various energy sources are realized through links of production, transportation, distribution, consumption and the like of cold, heat, electricity, gas and the like.
In order to more efficiently utilize energy, energy storage technology can be used in an integrated energy system to store temporarily unused or redundant energy through a certain medium, and the energy can be released and utilized when needed so as to make up for the quantity, form and time difference of the energy in the development, conversion, transportation and utilization processes. The energy storage has important significance for complementation, integration and optimization of various energy forms such as cold, hot and electric energy in the comprehensive energy system and coupling of energy supply networks such as cold, hot and electric energy.
The reasonable configuration of energy storage is a key for improving the use efficiency of the comprehensive energy system, however, for the regional comprehensive energy system, different functional areas are also included in the same region, the requirements of the different functional areas on energy are different, and the energy distribution and the load are also greatly different.
However, while the prior art regards the electric-thermal-cold equipment and demand side flexible loads as system generalized energy storage resources, modeling the regional integrated energy system optimization operation with the flexible coupling of cold and heat and the multi-energy complementation feature, the complementary operation of the integrated energy system in complex functional intervals and the complementary coordination between multiple energy sources such as cold-electric-thermal-gas cannot be achieved. In other words, the prior art has the problems of uneven resource allocation, low energy utilization rate and the like in different functional areas.
Disclosure of Invention
The application provides a multi-element energy storage distribution method, a multi-element energy storage distribution system, electronic equipment and a multi-element energy storage medium aiming at a comprehensive energy system, and aims to solve the problems of uneven resource distribution, low energy utilization rate and the like in different functional areas in the prior art and realize complementary operation of the comprehensive energy system in a complex functional area and complementary coordination among multiple energy sources such as cold, electricity, heat and gas.
Specifically, the embodiment of the application provides the following technical scheme:
in a first aspect, an embodiment of the present application provides a multi-element energy storage allocation method for an integrated energy system, including:
determining initial multi-element energy storage cost minimum values of comprehensive energy sources in different functional areas based on the regional multi-element energy storage evaluation function;
inputting the initial multi-element energy storage cost minimum value into a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost within a preset iteration number after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iterative computation of the preset iteration number;
and distributing the comprehensive energy sources in the different functional areas based on the lowest value of the optimized multi-element energy storage cost and the optimized multi-element energy storage proportion.
Further, the multi-element energy storage distribution method for the comprehensive energy system further comprises the following steps:
the method for determining the initial multi-element energy storage proportion of the comprehensive energy sources in different functional areas based on the regional multi-element energy storage evaluation function comprises the following steps:
the regional multi-element energy storage evaluation function is determined based on multi-element energy storage requirements, multi-element energy storage proportions and multi-element energy storage costs of the comprehensive energy system.
Further, the multi-element energy storage distribution method for the comprehensive energy system further comprises the following steps:
inputting the initial multi-element energy storage proportion into a multi-element energy storage distribution model based on an improved firefly algorithm to obtain an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost, which is output by the multi-element energy storage distribution model based on the improved firefly algorithm, wherein the method comprises the following steps:
step one, initializing firefly population individuals;
step two, randomly initializing the position of a firefly, and determining an objective function value of the firefly based on the regional multi-element energy storage evaluation function;
determining the relative fluorescence brightness and attraction degree of the fireflies in the population based on the objective function value of the fireflies;
determining the moving direction of the firefly based on the relative fluorescence brightness;
step five, updating the space position of the firefly based on a position updating formula and a step length formula;
step six, determining updated relative fluorescence brightness of the firefly based on the updated spatial position of the firefly;
step seven, determining whether the iterative computation reaches the preset iterative times.
Further, the multi-element energy storage distribution method for the comprehensive energy system further comprises the following steps:
the updating the spatial position of fireflies based on the position updating formula comprises:
and optimizing a control step factor based on the adaptive step-changing mechanism.
Further, the multi-element energy storage distribution method for the comprehensive energy system further comprises the following steps:
and under the condition that the iterative computation reaches the preset iterative times, outputting the optimal multi-element energy storage cost minimum value and the optimal multi-element energy storage proportion with the lowest multi-element energy storage cost within the preset iterative times.
Further, the multi-element energy storage distribution method for the comprehensive energy system further comprises the following steps:
and if the iterative computation does not reach the preset iterative times in the step six, repeating the steps two to seven in sequence until the iterative computation reaches the preset iterative times.
Further, the multi-element energy storage distribution method for the comprehensive energy system further comprises the following steps:
the different functional areas include residential areas, office areas, business areas, industrial areas, logistics warehouse areas, transportation facility areas, public facility areas, green areas and agriculture and forestry areas, and the comprehensive energy sources include cold, electricity, heat and gas.
In a second aspect, embodiments of the present application also provide a multi-element energy storage distribution system for an integrated energy system, comprising:
the initial multi-element energy storage cost evaluation unit is used for determining the initial multi-element energy storage cost minimum value of the comprehensive energy sources in the different functional areas based on the regional multi-element energy storage evaluation function;
the multi-element energy storage cost optimizing unit is used for inputting the initial multi-element energy storage cost minimum value into a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the multi-element energy storage cost minimum within the preset iteration number after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iteration calculation of the preset iteration number;
and the comprehensive energy distribution unit is used for distributing the comprehensive energy in the different functional areas based on the optimized multi-element energy storage cost minimum value and the optimized multi-element energy storage proportion.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the steps of the above multi-element energy storage allocation method for an integrated energy system are implemented when the processor executes the program.
In a fourth aspect, embodiments of the present application also provide a non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the above-mentioned multi-element energy storage allocation method for an integrated energy system.
According to the technical scheme, the multi-element energy storage distribution method, the multi-element energy storage distribution system, the electronic equipment and the storage medium for the comprehensive energy system aim to solve the problems of uneven resource distribution, low energy utilization rate and the like in different functional areas in the prior art, and on the basis of a system multi-energy complementary configuration principle, the regional energy storage evaluation function and the comprehensive energy system energy storage planning model are utilized to achieve overall planning of various energy sources in the comprehensive energy system with the aim of economic cost minimization, so that an energy storage distribution scheme with the minimum economic cost is found, and complementary operation of the comprehensive energy system in a complex functional interval and complementary coordination among various energy sources such as cold, electricity, heat and gas are achieved.
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In order to more clearly illustrate the application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a multi-element energy storage distribution method for an integrated energy system according to an embodiment of the present application;
FIG. 2 is a graph showing the comparison of energy storage costs of firefly algorithm and the integrated energy sources for improving firefly algorithm according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a multi-element energy storage distribution system for an integrated energy system according to an embodiment of the present application; and
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The various terms or phrases used herein have the ordinary meaning known to those of ordinary skill in the art, but rather the application is intended to be more fully described and explained herein. If the terms and phrases referred to herein have a meaning inconsistent with the known meaning, the meaning expressed by the present application; and if not defined in the present application, have meanings commonly understood by those of ordinary skill in the art.
In the field of comprehensive energy, in the prior art, electric-thermal-cold equipment and demand side flexible load are regarded as system generalized energy storage resources, and the optimized operation of the regional comprehensive energy system is modeled by utilizing the flexible coupling of cold and heat and multi-energy complementary characteristics, but the complementary operation of the comprehensive energy system in a complex functional interval and the complementary coordination among various energy sources such as cold, electric, thermal and gas cannot be realized. In other words, the prior art has the problems of uneven resource allocation, low energy utilization rate and the like in different functional areas.
In view of this, in a first aspect, an embodiment of the present application proposes a multi-element energy storage distribution method for an integrated energy system.
The multi-element energy storage distribution method for the integrated energy system according to the present application is described below with reference to fig. 1.
Fig. 1 is a flowchart of a multi-element energy storage distribution method for an integrated energy system according to an embodiment of the present application.
In this embodiment, it should be noted that the multi-element energy storage distribution method for the integrated energy system may include the following steps:
103: determining initial multi-element energy storage cost minimum values of comprehensive energy sources in different functional areas based on the regional multi-element energy storage evaluation function;
102: inputting the initial multi-element energy storage cost minimum value into a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost within the preset iteration number after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iteration calculation of the preset iteration number;
101: and distributing comprehensive energy sources in different functional areas based on the optimized multi-element energy storage cost minimum value and the optimized multi-element energy storage proportion.
Specifically, for 101, in this embodiment, it should be noted that the multi-element energy storage distribution method for the integrated energy system may further include: based on the regional multi-element energy storage evaluation function, determining an initial multi-element energy storage proportion of the comprehensive energy sources in different functional regions comprises: the regional multi-element energy storage evaluation function is determined based on multi-element energy storage requirements, multi-element energy storage proportions and multi-element energy storage costs of the comprehensive energy system.
For 101, because the range of the radiation of the integrated energy system is large, even under the same area, the use of energy by different functional areas is still quite different. Based on the above, the energy storage cost of each energy source (such as cold, heat, electricity, gas, etc.) in the different functional areas and the shortage degree of each energy source are determined according to the use condition of the energy source in the different functional areas, and the regional multi-element energy storage evaluation function is established according to the energy storage cost of each energy source in the different functional areas and the shortage degree of each energy source.
In this embodiment, it should be noted that the method for distributing multi-element energy storage for the integrated energy system may further include: different functional areas include residential areas, office areas, business areas, industrial areas, logistics warehouse areas, transportation facility areas, public facility areas, green areas and agriculture and forestry areas, and comprehensive energy sources include cold, electricity, heat and gas. However, it should be emphasized that embodiments of the present application are not limited thereto, and that one of ordinary skill in the art may set more different functional areas and/or energy categories according to actual needs.
Specifically, the area multi-element energy storage evaluation function is established by considering the use, distribution and conversion conditions of cold, heat, electricity and gas energy sources in different functional areas, including: and establishing a regional multi-element energy storage evaluation function according to the energy storage cost of each energy source in different regions, the demand degree of each energy source and the energy storage distribution proportion of each energy source in different regions so as to determine the distribution scheme with the lowest comprehensive energy storage cost.
More specifically, the regional multi-element energy storage evaluation function provided in an embodiment of the present application may be expressed as follows: mesc=sum (cost x.s)). In the comprehensive energy system, the costs of cold, electricity, heat and gas energy storage are different, so that the cost matrix cost is used for representing the costs of different energy storages in the comprehensive energy; setting a demand matrix S of energy sources in different areas by analyzing the energy source use characteristics of the different functional areas; the initial energy proportion matrix X represents the proportion of different energy allocated to different functional areas.
For example, the cost matrix cost may be expressed as: cost= [ c 1 c 2 … c k ]。
For another example, the demand matrix S may be expressed as:
wherein s is ij (i=1, 2, …, k, j=1, 2, …, m) represents a regionThe degree of demand for energy i in j, and k and m are natural numbers. s is(s) ij A larger value indicates a larger energy consumption in the region, and a higher demand level.
Specifically, the demand matrix S may include, but is not limited to, the following:
however, it should be emphasized that embodiments of the present application are not limited thereto, and that one of ordinary skill in the art may set more different demand matrices S according to actual needs.
For another example, the initial energy ratio matrix X may be expressed as:
wherein x is ij (i=1, 2, …, k, j=1, 2, …, m) represents the energy proportion of the ith energy source in the region j, and k and m are natural numbers.
Specifically, the initial energy proportion matrix X that is initially randomly generated may include, but is not limited to, the following:
however, it should be emphasized that embodiments of the present application are not limited thereto, and one of ordinary skill in the art may set a plurality of different initial energy ratio matrices X according to actual needs.
In other words, in the regional multi-element energy storage evaluation function, x·s represents the multiplication of the initial energy proportion matrix X and the energy demand matrix S corresponding values, and MESC represents the lowest value of the multi-element energy storage cost of the integrated energy system. Specifically, in the above embodiment, the cost (x..s) is a matrix of 1 row and 9 column, which can be understood as 9 values corresponding to 9 functional areas, respectively, i.e. the cost of storing these four energy sources in each area, and the MESC represents that the element values of this matrix are added to obtain the total cost. The minimum value of the multi-energy storage cost of the integrated energy system at the above 9 regions, that is, the minimum fluorescence intensity to be described below, is calculated based on the multi-energy storage evaluation function MESC.
For 102, in this embodiment, it should be noted that the multi-element energy storage distribution method for the integrated energy system may further include: inputting the initial multi-element energy storage proportion into a multi-element energy storage distribution model based on an improved firefly algorithm to obtain an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost output by the multi-element energy storage distribution model based on the improved firefly algorithm, wherein the method comprises the following steps: step one, initializing firefly population individuals; step two, randomly initializing the position of the firefly, and determining the objective function value of the firefly based on the regional multi-element energy storage evaluation function; determining the relative fluorescence brightness and attraction degree of fireflies in the population based on the objective function value of the fireflies; determining the moving direction of fireflies based on the relative fluorescence brightness; step five, updating the space position of the firefly based on a position updating formula and a step length formula; step six, determining updated relative fluorescence brightness of the firefly based on the updated spatial position of the firefly; step seven, determining whether the iterative computation reaches a preset iterative number.
Specifically, the multi-energy storage planning model of the comprehensive energy system based on the regional variability is built by taking the lowest energy storage cost as an optimization target, the multi-energy storage proportion and/or the lowest initial multi-energy storage cost value under different regions are determined by utilizing the regional multi-energy storage evaluation function, and the multi-energy storage proportion and/or the lowest initial multi-energy storage cost value are input into the multi-energy storage planning model of the comprehensive energy system, so that an energy distribution scheme with the lowest multi-energy storage cost in the comprehensive energy system is found by utilizing an improved variable step-length self-adaptive firefly algorithm.
More specifically, in step one, initializing firefly population individuals may include: setting the number of firefly population individuals as N, the individual dimension as M, the maximum iteration number as T and the position of the firefly individual as x i The light intensity absorption coefficient is omega, and the maximum attraction degree is beta 0 The step size coefficient is alpha. For example, the number of firefly individuals may be n=40, the maximum number of iterations t=100, and firefly individual x i The value range of (2) is [0,1]]It should be noted that examples of values or ranges of values of other of the above parameters will be shown in the following description. However, it should be emphasized that the embodiments of the present application are not limited thereto, and those skilled in the art may set more different parameters, parameter values and/or parameter value ranges according to actual needs, for example, the maximum iteration number T may be 200 or 300, so long as the iteration result tends to converge to find the optimal solution.
More specifically, in step two, randomly initializing the position of the firefly and determining the objective function value of the firefly based on the regional multi-element energy storage evaluation function may include: the firefly positions are randomly initialized, and the objective function values of the fireflies are calculated as the respective minimum fluorescence intensities. In addition, the minimum fluorescence brightness is a multi-element energy storage cost value in the comprehensive energy system.
It should be noted here that, since the firefly algorithm is used for solving the maximum value, and the method proposed by the present application is a problem of solving the minimum value of the multi-element energy storage cost, one of the improvements of the firefly algorithm in the present application is to change the firefly to move toward the darker brightness, and on this basis, the value solved based on the improved firefly algorithm is referred to as the minimum fluorescence brightness for convenience of description.
Further, randomly initializing the firefly position, calculating the objective function value of the firefly as the respective minimum fluorescence intensities includes determining the minimum luminescence intensity of the firefly using the following formula:
l o ＝fun(x i ) (1)
wherein fun (x) i ) For calculating the objective function of firefly, l o The minimum fluorescence brightness of the position of each firefly in the population is represented, and the lower the objective function value is, the darker the self brightness of the fireflies is, and the lower the multi-element energy storage cost is.
Here, fun () means sum () in the foregoing, in other words, l o The MESC is obtained.
More specifically, in step three, determining the relative fluorescence intensity and attractiveness of fireflies in the population based on the objective function values of fireflies may include: the relative fluorescence intensity and attractiveness of fireflies in the population were determined using the following formula.
For example, the relative fluorescence intensity calculation formula of firefly can be expressed as follows:
wherein l o Represents the minimum fluorescence intensity of firefly, ω represents the light intensity absorption coefficient, r i,j Representing the spatial distance between firefly i and firefly j.
For another example, the formula for calculating the attractiveness of fireflies can be expressed as follows:
wherein beta is 0 The maximum coefficient of attraction is represented, and may be set to be a constant 1 in this embodiment.
More specifically, in step four, determining the moving direction of the firefly based on the relative fluorescence brightness may include: fluorescence gradually decreases with increasing distance and absorption by the propagation medium.
More specifically, in step five, updating the spatial position of the firefly based on the position update formula and the step formula may include: and updating the spatial position of the firefly according to a position updating formula, and randomly moving the firefly at the optimal position.
For example, the firefly location update formula may be expressed as follows:
where α represents a step size coefficient, which is set to be constant in this embodiment, the range of values is between [0,1], which may be, for example, 0.975, and rand represents a random factor, which is uniformly distributed over [0,1], and furthermore t represents the number of iterations.
Further, in this embodiment, it should be noted that the method for distributing multi-element energy storage for an integrated energy system may further include: updating the spatial location of the firefly based on the location update formula, comprising: and optimizing a control step factor based on the adaptive step-changing mechanism.
Specifically, since the α step factor in the formula (4) has a great influence on the search and optimization of the firefly population, and α is randomly valued in [0,1], an excessively large or excessively small random value interferes with the searching capability of the population, and thus, an adaptive step factor is introduced to improve the optimizing capability.
More specifically, the optimization improvement formula of the step factor can be expressed as:
α t+1 ＝α t δ，0.5<δ<1 (5)
where δ represents the cooling coefficient. It should be noted that, when iteration is started, the value of the step factor α is larger, so that a better global optimizing capability is ensured in the initial stage of iteration, along with the increase of the iteration times, the α factor is continuously reduced, the local searching capability is enhanced, and by changing the value of the α step factor as described above, the global searching capability and the local searching capability of the firefly algorithm can be better balanced.
More specifically, in step six, the updated relative fluorescence brightness of the firefly is determined based on the updated spatial position of the firefly.
More specifically, in step seven, determining whether the iterative calculation reaches a predetermined number of iterations may include: it is determined whether the number of iterations T exceeds a predetermined maximum number of iterations T.
Further, in this embodiment, it should be noted that the method for distributing multi-element energy storage for an integrated energy system may further include: under the condition that the iterative computation reaches the preset iterative times, outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost within the preset iterative times
Further, in this embodiment, it should be noted that the method for distributing multi-element energy storage for an integrated energy system may further include: and under the condition that the iterative computation does not reach the preset iterative times in the step six, repeating the steps two to seven in sequence until the iterative computation reaches the preset iterative times.
The effect of the multi-element energy storage distribution method for an integrated energy system of the present application is further described below with reference to fig. 2.
FIG. 2 is a graph showing the comparison of energy storage costs of firefly algorithm and the integrated energy source for improving firefly algorithm according to an embodiment of the present application.
As shown, the solid line portion is the firefly algorithm (Firefly Algorithm, FA), while the dashed line portion is the modified Variable-step adaptive firefly algorithm (Variable-step Adaptive Firefly Algorithm, VAFA) provided by embodiments of the present application, with the horizontal axis representing the number of iterations and the vertical axis representing the energy storage cost (units, years/ten thousand yuan).
As can be seen from fig. 2, the VAFA converges faster than the FA, in other words, the multiple energy storage allocation method based on the VAFA can find a solution with the minimum comprehensive energy storage cost faster, and the solution with the minimum comprehensive energy storage cost determined based on the VAFA is better than the solution with the minimum comprehensive energy storage cost determined based on the FA, that is, the comprehensive energy storage cost is smaller.
In summary, the multi-element energy storage distribution method for the comprehensive energy system provided by the embodiment of the application solves the problems of uneven resource distribution, low energy utilization rate and the like in different functional areas in the prior art, and on the basis of a system multi-energy complementary configuration principle, the overall planning of various energy sources in the comprehensive energy system is realized by using an area energy storage evaluation function and an energy storage planning model of the comprehensive energy system with the aim of economic cost minimization, an energy storage distribution scheme with the minimum economic cost is found, and the complementary operation of the comprehensive energy system in a complex functional interval and the complementary coordination among various energy sources such as cold, electricity, heat and gas are realized.
Based on the same inventive concept, in another aspect, an embodiment of the present application provides a multi-element energy storage distribution system for an integrated energy system.
The multi-element energy storage distribution system for the integrated energy system provided by the application is described below with reference to fig. 3, and the multi-element energy storage distribution system for the integrated energy system described below and the multi-element energy storage distribution method for the integrated energy system described above can be referred to correspondingly.
Fig. 3 is a schematic structural diagram of a multi-element energy storage distribution system for an integrated energy system according to an embodiment of the present application.
In this embodiment, the multi-element energy storage distribution system 1 for an integrated energy system includes: an initial multi-element energy storage cost evaluation unit 10, configured to determine an initial multi-element energy storage cost minimum value of the integrated energy source in the different functional areas based on the regional multi-element energy storage evaluation function; a multi-element energy storage cost optimizing unit 20 for inputting the initial multi-element energy storage cost minimum value to a multi-element energy storage distribution model based on the improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the multi-element energy storage cost minimum within a predetermined number of iterations after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iterative computation of the predetermined number of iterations; and the comprehensive energy distribution unit 30 is used for distributing comprehensive energy in different functional areas based on the optimized multi-element energy storage cost minimum value and the optimized multi-element energy storage proportion.
Since the system provided by the embodiment of the present application can be used to perform the method described in the above embodiment, the working principle and the beneficial effects thereof are similar, and therefore, the detailed description will not be given here, and the specific content can be referred to the description of the above embodiment.
In this embodiment, it should be noted that, each unit in the system of the embodiment of the present application may be integrated into a whole or may be separately deployed. The above units may be combined into one unit or may be further split into a plurality of sub units.
In yet another aspect, a further embodiment of the present application provides an electronic device based on the same inventive concept.
Fig. 4 is a schematic diagram of an electronic device according to an embodiment of the application.
In this embodiment, it should be noted that the electronic device may include: processor 410, communication interface (Communications Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a method comprising: determining initial multi-element energy storage cost minimum values of comprehensive energy sources in different functional areas based on the regional multi-element energy storage evaluation function; inputting the initial multi-element energy storage cost minimum value into a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost within the preset iteration number after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iteration calculation of the preset iteration number; and distributing comprehensive energy sources in different functional areas based on the optimized multi-element energy storage cost minimum value and the optimized multi-element energy storage proportion.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor is implemented to perform a method comprising: determining initial multi-element energy storage cost minimum values of comprehensive energy sources in different functional areas based on the regional multi-element energy storage evaluation function; inputting the initial multi-element energy storage cost minimum value into a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage proportion with the lowest multi-element energy storage cost within the preset iteration number after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iteration calculation of the preset iteration number; and distributing comprehensive energy sources in different functional areas based on the optimized multi-element energy storage cost minimum value and the optimized multi-element energy storage proportion.
The system embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and components shown as units may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Moreover, in the present application, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Furthermore, in the present application, the description of the terms "embodiment," "this embodiment," "yet another embodiment," and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (4)
1. A multi-element energy storage distribution method for an integrated energy system is characterized by comprising the following steps:
step S1, determining the initial multi-element energy storage cost minimum value of the comprehensive energy sources in different functional areas based on the multi-element energy storage evaluation function of the areas,
the regional multiple energy storage evaluation function is determined based on multiple energy storage demands, multiple energy storage proportions, multiple energy storage costs of the integrated energy system, the different functional regions include residential areas, office areas, business areas, industrial areas, logistical warehouse areas, transportation facility areas, public facility areas, green areas, and agriculture and forestry areas, and the integrated energy includes cold, electricity, heat, gas,
the regional multi-element energy storage evaluation function is represented by mesc=sum (cost x.s)), the cost matrix cost represents the cost of storing different energy sources in the comprehensive energy source, the energy source demand matrix S under different regions is set by analyzing the energy source use characteristics of different functional regions, the initial energy source proportion matrix X represents the proportion of different energy sources allocated to different functional regions,
the cost matrix cost is expressed as: cost= [ c 1 ,c 2 ,…,c k ],
The energy demand matrix S under different areas is expressed as:
s ij representing the extent of demand for energy i in region j, i=1, 2, …, k, j=1, 2, …, m, and k and m are natural numbers,
the initial energy proportion matrix X is expressed as:
x ij represents the energy ratio of the ith energy source in the region j, i=1, 2, …, k, j=1, 2, …, m, and k and m are natural numbers,
the X.S represents multiplication of the initial energy proportion matrix X and corresponding values of the energy demand matrix S in different areas;
step S2 of inputting the initial multi-element energy storage cost minimum value to a multi-element energy storage distribution model based on an improved firefly algorithm, and outputting an optimized multi-element energy storage cost minimum value and an optimized multi-element energy storage ratio within a predetermined number of iterations after the multi-element energy storage distribution model based on the improved firefly algorithm is subjected to iterative computation of the predetermined number of iterations, the step S2 comprising:
step one, initializing firefly population individuals, which comprises setting the number of firefly population individuals as N, the individual dimension as M, the maximum iteration number as T and the position of the firefly individuals as x i The light intensity absorption coefficient is omega, and the maximum attraction degree is beta 0 The step factor is alpha;
step two, randomly initializing the position of the firefly, changing the firefly into a movement in a direction with darker brightness, and calculating the objective function value of the firefly based on the regional multi-element energy storage evaluation function to serve as the minimum fluorescence brightness l of the position of each firefly individual in the population o Wherein l o ＝fun(x i )，fun(x i ) For calculating the objective function of firefly, l o The smaller the objective function value of the firefly is, the darker the self brightness of the firefly is, and the lower the multi-element energy storage cost is;
determining the relative fluorescence brightness L and the attraction degree beta of the fireflies in the population based on the objective function value of the fireflies, wherein,omega represents the light intensity absorption coefficient, r i,j Representing the spatial distance between firefly i and firefly j, +.>
Determining the moving direction of the firefly based on the relative fluorescence brightness;
step five, updating the position of the firefly individual based on a position updating formula and a step length formula, updating the position of the firefly individual according to the position updating formula, randomly moving the firefly in the optimal position, optimally controlling the step length factor based on a self-adaptive step length changing mechanism,
wherein, the location update formula is:
wherein, the value range of alpha is between 0 and 1, the rand represents a random factor, the random factor is uniformly distributed on 0 and 1, and t represents the iteration times,
the step formula is as follows:
α t+1 ＝α t δ，0.5<δ<1
wherein δ represents the cooling coefficient and α decreases continuously as the number of iterations increases;
step six, determining updated relative fluorescence brightness L of the fireflies based on the updated positions of the fireflies;
step seven, determining whether the iterative computation reaches the preset iterative times;
outputting a multi-element energy storage cost minimum value within the preset iteration times as the optimized multi-element energy storage cost minimum value and obtaining the corresponding optimized multi-element energy storage proportion under the condition that the iteration calculation reaches the preset iteration times in the seventh step, and repeatedly executing the second step to the seventh step in sequence until the iteration calculation reaches the preset iteration times under the condition that the iteration calculation does not reach the preset iteration times in the seventh step;
and step S3, distributing the comprehensive energy sources in the different functional areas based on the lowest value of the optimized multi-element energy storage cost and the optimized multi-element energy storage proportion.
2. A multi-element energy storage distribution system for an integrated energy system, wherein the multi-element energy storage distribution system for an integrated energy system performs the multi-element energy storage distribution method for an integrated energy system according to claim 1.
3. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method for multi-element energy storage allocation for an integrated energy system as claimed in claim 1.
4. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the multi-element energy storage allocation method for an integrated energy system as claimed in claim 1.
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