A genetic algorithm-based ice storage strategy generation method, device and medium

Abstract

The application belongs to the technical field of refrigeration, and particularly relates to an ice storage strategy generation method, device and medium based on a genetic algorithm, comprising the following steps: establishing a target function and a constraint violation degree matrix according to pre-acquired basic information; calculating an optimal solution of the target function by using a genetic algorithm according to the constraint violation degree matrix; and determining an ice storage strategy generation scheme according to the optimal solution of the target function. The purpose is to solve the problem that the ice storage cost is lower when the indoor cooling demand is met by relying on the experience of managers.

Description

Technical Field

[0001] This invention belongs to the field of refrigeration technology, and in particular relates to a method, device and medium for generating ice storage strategies based on genetic algorithms. Background Technology

[0002] In engineering applications, we need to provide ice storage and melting strategies for ice storage projects. Based on fluctuating electricity prices, we need an ice storage strategy that maximizes the cooling capacity released from melting ice to meet indoor cooling needs while minimizing electricity costs during the ice storage process. Current methods rely heavily on expert experience to provide strategies, making them essential for management expertise. Therefore, we need a widely replicable, universal, and efficient strategy. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] This invention addresses the above-mentioned problems by proposing a method, device, and medium for generating ice storage strategies based on genetic algorithms. Its purpose is to reduce ice storage costs in the current situation where indoor cooling needs are met by relying on the experience of managers.

[0005] (II) Technical Solution

[0006] To achieve the above objectives, this invention provides a method for generating ice storage strategies based on genetic algorithms, comprising the following steps:

[0007] Based on the pre-obtained basic information, an objective function and a constraint violation degree matrix are established. The objective function is:

[0008] f1 = P(L1,L3)*X1(L1,L3)*Q / COP

[0009] f2 = X1(L1,L3) * Q / COP

[0010] Where Q is the rated cooling capacity of the chiller; COP is the refrigeration efficiency; P(L1,L3) represents the electricity price during the ice storage period and the period with cooling load; X1(L1,L3) represents the load rate of the chiller during the ice storage period and the period with cooling load.

[0011] Based on the constraint violation degree matrix, the optimal solution of the objective function is calculated using a genetic algorithm;

[0012] Based on the optimal solution of the objective function, the generation scheme of the ice storage cooling strategy is determined.

[0013] Furthermore, the constraint violation degree matrix is ​​as follows:

[0014] J1=[X1(L1,L3)*Q-Q_predict(sum)] 2-e

[0015] J2=[Q_predict*(1-X2(L3))-Q*X1(L3)] 2 -e

[0016] Where Q_predict(sum) is the sum of the hourly cooling capacity required for the day; Q_predict*(1-X2(L3)) is the cooling capacity provided by ice melting per hour during the time when there is a cooling load; X2(L3) is the proportion of the hourly cooling capacity released by the chiller to the cooling capacity requirement during the time when there is a cooling load; Q*X1(L3) is the cooling capacity provided by the chiller per hour during the time when there is a cooling load; e represents the allowable error, which is a known constant value.

[0017] Furthermore, the steps for determining the optimal solution of the objective function using a genetic algorithm include:

[0018] Select the initial population;

[0019] The fitness values ​​of individuals in the population are evaluated according to the predetermined objective function.

[0020] Based on the fitness value, superior individuals are selected to generate the next generation of individuals;

[0021] The selected individuals are then replicated, mutated, and crossovered to form a new population.

[0022] Repeat the above steps until the objective function converges, and determine the ice storage cooling strategy route corresponding to the converged value as the optimal route.

[0023] Furthermore, the ice storage strategy achieves lower electricity costs during the ice storage process, provided that the cold released from melting ice meets the indoor cooling demand.

[0024] Furthermore, the pre-acquired basic information includes at least: the rated cooling capacity of the chiller, real-time electricity price information, real-time weather forecast, cooling efficiency, and real-time information on the cooling capacity required indoors.

[0025] Furthermore, the objective functions include: electricity cost objective function f1 and chiller power consumption objective function f2.

[0026] To achieve the above objectives, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described above.

[0027] To achieve the above objectives, the present invention provides a computer-readable storage medium having a processor program stored thereon, the processor program being used to execute the method described thereon.

[0028] (III) Beneficial Effects

[0029] The above-mentioned technical solution of the present invention has the following advantages: by establishing an objective function and a constraint violation degree matrix, a strategy can be intelligently generated based on a genetic algorithm, so as to achieve the goal of both meeting the indoor cooling demand as much as possible with the cold released by ice melting and reducing the electricity price consumed in the ice storage process. Attached Figure Description

[0030] Figure 1 This is a flowchart of a process for obtaining the optimal solution based on a genetic algorithm, as disclosed in this invention.

[0031] Figure 2 This is a diagram illustrating the results of obtaining the optimal solution using a genetic algorithm, as disclosed in this invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0034] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0035] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0036] Genetic Algorithm: A genetic algorithm is a search algorithm based on natural selection and population genetic mechanisms. It simulates the reproduction, crossover, and mutation phenomena in natural selection and natural inheritance. When using a genetic algorithm to solve a problem, each possible solution is encoded as a "chromosome," i.e., an individual. Several individuals constitute a population (all possible solutions). At the beginning of the genetic algorithm, some individuals (i.e., initial solutions) are always randomly generated. Each individual is evaluated according to a predetermined objective function, and a fitness value is assigned. Based on this fitness value, some individuals are selected to generate the next generation. The selection operation reflects the principle of "survival of the fittest." "Good" individuals are used to generate the next generation, while "bad" individuals are eliminated. The selected individuals are then recombine through crossover and mutation operators to generate a new generation. Individuals in this new generation inherit some superior traits from the previous generation, thus performing better than the previous generation. In this way, the evolution gradually moves towards the optimal solution. Therefore, a genetic algorithm can be seen as a preliminary evolutionary process of a population composed of feasible solutions, used to solve various combinatorial optimization problems.

[0037] Please see Figure 1 The present invention provides a method for predicting and allocating cooling load demand for ice storage air conditioning, comprising the following steps:

[0038] Step S100: Based on the pre-acquired basic information, establish the objective function and the constraint violation degree matrix. The objective function is:

[0039] f1 = P(L1,L3)*X1(L1,L3)*Q / COP

[0040] f2 = X1(L1,L3) * Q / COP

[0041] Where Q is the rated cooling capacity of the chiller; COP is the refrigeration efficiency; P(L1,L3) represents the electricity price during the ice storage period and the period with cooling load; X1(L1,L3) represents the load rate of the chiller during the ice storage period and the period with cooling load.

[0042] Specifically, the ice storage cooling strategy aims to address issues such as energy efficiency, grid balancing, and cost savings. It utilizes off-peak electricity generated at night to produce ice, which is then stored in ice storage devices. During the day, melting the ice releases the stored cooling capacity, reducing the electricity load on air conditioning systems and the installed capacity of air conditioning systems during peak grid hours, thereby effectively reducing operating costs. However, current strategies rely heavily on expert experience, making them highly dependent on the manager's expertise. Therefore, to improve energy efficiency and minimize energy loss and operating costs, this invention uses machine algorithms to intelligently generate strategies, including daily ice storage and melting periods.

[0043] The first step is to obtain the necessary basic information. This includes, for example, the chiller's rated cooling capacity Q, the hourly electricity price P for the day, the hourly weather forecast for the day, the cooling efficiency COP, and the hourly cooling capacity required indoors for the day, Q_predict. The time periods with low electricity prices are designated as the ice storage period (L1), and other time periods are designated as the non-ice storage period (L2). The time periods requiring cooling capacity are designated as the periods with cooling load (L3), and other time periods are designated as the periods without cooling load (L4).

[0044] The second step is to identify the issues to be optimized. This is based on the peak and trough of electricity prices, and requires an ice storage cooling strategy that can both maximize the cooling capacity released by melting ice to meet indoor cooling needs and minimize the electricity cost of the ice storage process.

[0045] The third step is to determine the objective functions. In this embodiment, the objective function for electricity costs is f1, and the objective function for chiller power consumption is f2.

[0046] f1 = P(L1,L3)*X1(L1,L3)*Q / COP

[0047] f2 = X1(L1,L3) * Q / COP

[0048] Where P(L1,L3) represents the electricity price during the ice storage period and the period with cooling load; X1(L1,L3) represents the load rate of the chiller during the ice storage period and the period with cooling load.

[0049] Step 4: Set the constraint violation degree matrix:

[0050] J1=[X1(L1,L3)*Q-Q_predict(sum)] 2 -e constraint 1

[0051] J2=[Q_predict*(1-X2(L3))-Q*X1(L3)] 2 -e constraint2

[0052] Where Q_predict(sum) is the sum of the hourly cooling capacity required for the day; Q_predict*(1-X2(L3)) is the cooling capacity provided by ice melting per hour during the time when there is a cooling load; X2(L3) is the proportion of the hourly cooling capacity released by the chiller to the cooling capacity requirement during the time when there is a cooling load; Q*X1(L3) is the cooling capacity provided by the chiller per hour during the time when there is a cooling load; e represents the allowable error, which is a known constant value.

[0053] It should be noted that J1 and J2 being less than or equal to 0 indicates that the corresponding individual meets the corresponding constraint conditions, while being greater than 0 indicates that they do not meet the constraints, and the larger the value, the higher the degree of violation of the constraint conditions.

[0054] Step S200: Based on the constraint violation degree matrix, calculate the optimal solution of the objective function using a genetic algorithm;

[0055] Step S300: Determine the generation scheme of the ice storage cooling strategy based on the optimal solution of the objective function.

[0056] In this step, a multi-population genetic algorithm is used to select an initial population; the fitness values ​​of individuals in the population are evaluated according to the predetermined objective function; based on the fitness values, superior individuals are selected to generate the next generation of individuals; the selected individuals are replicated, mutated, and crossovered to form a new population; the above steps are repeated until the value of the objective function converges, and the ice storage cooling strategy route corresponding to the converged value is determined as the optimal route, which is the desired result.

[0057] Figure 2 The graph shows the desired results. In the graph, PLR represents the load rate of the X1 chiller, and ita represents the proportion of cooling capacity released by the X2 chiller to the total cooling capacity requirement.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] It should be noted that:

[0060] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0061] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0062] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0063] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for generating ice storage cooling strategies based on genetic algorithms, characterized in that, Includes the following steps: Based on the pre-acquired basic information, an objective function and a constraint violation degree matrix are established. The objective function includes the electricity cost objective function f1 and the chiller power consumption objective function f2. Where Q is the rated cooling capacity of the chiller; COP is the refrigeration efficiency; L1 represents the ice storage period, L3 represents the period with cooling load, P(L1,L3) represents the electricity price during the ice storage period and the period with cooling load; X1(L1,L3) represents the load rate of the chiller during the ice storage period and the period with cooling load. Based on the constraint violation degree matrix, the optimal solution of the objective function is calculated using a genetic algorithm; Based on the optimal solution of the objective function, determine the generation scheme of the ice storage cooling strategy; The steps of using a genetic algorithm to calculate the optimal solution of the objective function include: Select the initial population; The fitness values ​​of individuals in the population are evaluated according to the predetermined objective function. Based on the fitness value, superior individuals are selected to generate the next generation of individuals; The selected individuals are then replicated, mutated, and crossovered to form a new population. Repeat the above steps until the value of the objective function converges, and determine the ice storage cooling strategy route corresponding to the converged value as the optimal route; The constraint violation degree matrix is ​​as follows: Where Q_predict(sum) is the sum of the hourly cooling capacity required for the day; Q_predict*(1-X2(L3)) is the cooling capacity provided by ice melting per hour during the time with cooling load; X2(L3) is the proportion of the hourly cooling capacity released by the chiller to the cooling capacity requirement during the time with cooling load; Q*X1(L3) is the cooling capacity provided by the chiller per hour during the time with cooling load; e represents the allowable error, which is a known constant; J1 and J2 are less than or equal to 0, indicating that the corresponding individuals meet the corresponding constraints, and greater than 0, indicating that they do not meet the constraints, and the larger the value, the higher the degree of violation of the constraints.

2. The method for generating ice storage cooling strategies based on genetic algorithms as described in claim 1, characterized in that, The ice storage strategy allows for lower electricity costs while ensuring that the cooling capacity released from melting ice meets indoor cooling needs.

3. The method for generating ice storage cooling strategies based on genetic algorithms as described in claim 1, characterized in that, The pre-acquired basic information includes at least: the rated cooling capacity of the chiller, real-time electricity price information, real-time weather forecast, cooling efficiency, and real-time information on the cooling capacity required indoors.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method of any one of claims 1 to 3.

5. A computer-readable storage medium having a processor program stored thereon, characterized in that, The processor program is used to execute the method according to any one of claims 1 to 3.

Images