CN-121977312-A - Method, system, equipment and medium for optimizing energy efficiency of water chilling unit system

CN121977312ACN 121977312 ACN121977312 ACN 121977312ACN-121977312-A

Abstract

The invention belongs to the technical field of heating ventilation air conditioning systems, and discloses a method, a system, equipment and a medium for optimizing energy efficiency of a water chilling unit system, wherein the method comprises the steps of obtaining operation data of key equipment in a target water chilling unit system; the method comprises the steps of establishing a power consumption model of key equipment in a target water chilling unit system based on operation data of the key equipment in the target water chilling unit system, establishing an energy efficiency optimization objective function of the target water chilling unit system, solving the energy efficiency optimization objective function of the target water chilling unit system by utilizing an improved integral group intelligent optimization algorithm to obtain an energy efficiency optimization result of the target water chilling unit system, introducing a hybrid opposition-chaos initialization strategy, a self-adaptive operator scheduling mechanism based on performance feedback, an elite filing update strategy and a diversity restarting mechanism into the improved integral group intelligent optimization algorithm, and realizing efficient and stable optimization of energy efficiency of the water chilling unit system, thereby remarkably improving energy utilization efficiency of large public buildings and reducing operation cost.

Inventors

LUO XI
HUANG WENYUAN
LIU GUANGYU

Assignees

西安建筑科技大学

Dates

Publication Date: 20260505
Application Date: 20260204

Claims (10)

1. The energy efficiency optimization method for the water chilling unit system is characterized by comprising the following steps of: Acquiring operation data of key equipment in a target water chiller system; Constructing a power consumption model of key equipment in the target water chiller system based on operation data of the key equipment in the target water chiller system; establishing an energy efficiency optimization objective function of the target water chiller system based on a power consumption model of key equipment in the target water chiller system; Solving an energy efficiency optimization objective function of the target water chiller by utilizing an improved integral group intelligent optimization algorithm to obtain an energy efficiency optimization result of a target water chiller system; The improved whole intelligent optimization algorithm adopts a whole intelligent optimization algorithm which is introduced with a hybrid opposition-chaos initialization strategy, a self-adaptive operator scheduling mechanism based on performance feedback, an elite filing updating strategy and a diversity restarting mechanism.
2. The chiller system energy efficiency optimization method of claim 1, wherein the operational data of key devices in the target chiller system comprises operational data of a chiller, operational data of a cooling tower fan, operational data of a cooling water pump, and operational data of a chilled water pump; the operation data of the water chilling unit comprises the temperature, flow, power and rotating speed of the water chilling unit, the operation data of the cooling tower fan comprises the temperature, flow, power and rotating speed of the cooling tower fan, the operation data of the cooling water pump comprises the temperature, flow, power and rotating speed of the cooling water pump, and the operation data of the chilled water pump comprises the temperature, flow, power and rotating speed of the chilled water pump.
3. The chiller system energy efficiency optimization method of claim 1, wherein the target chiller system energy efficiency optimization objective function is as follows: Wherein, the The total power consumption of the target water chiller system; Is the first Power consumption of the water chilling unit; for running in the target water chilling unit system the total number of the water chilling units; Is the first Power consumption of the table cooling tower fan; the total number of cooling tower fans running in the target water chilling unit system is set; Is the first Power consumption of the table cooling water pump; The total number of the cooling water pumps running in the target water chiller system is set; Is the first Power consumption of the table chilled water pump; the total number of the chilled water pumps running in the target chiller system is set.
4. The chiller system energy efficiency optimization method of claim 1, wherein the hybrid opposition-chaos initialization strategy is as follows: randomly generating a plurality of initial individuals within the search boundary of a predetermined optimization variable; generating a opponent solution for each initial individual based on the opponent learning concept; The method comprises the steps of generating a chaotic sequence by using Logistic chaotic mapping, and applying scale disturbance to opposite solutions corresponding to each initial individual by using the chaotic sequence to obtain individuals after chaotic disturbance; And calculating the fitness of each initial individual and the individuals after the chaotic disturbance, and reserving the individuals with better fitness to obtain an initialized population.
5. The chiller system energy efficiency optimization method of claim 1, wherein the adaptive operator scheduling mechanism based on performance feedback is as follows: the performance score of each preset core search operator is updated in real time by adopting an exponential smoothing method, so that the performance score of each preset core search operator is obtained, wherein the preset core search operators are a global exploration operator, a neighborhood development operator and an elite disturbance operator; calculating the calling probability of each predetermined core search operator based on the performance score of each predetermined core search operator; And if the adaptability of the new candidate solution is better, updating the individual position and simultaneously updating the performance scores of the predetermined core search operators.
6. The chiller system energy efficiency optimization method of claim 1, wherein elite archive update strategy is as follows: Judging whether new individuals generated in each generation meet the condition that the fitness is superior to the fitness of worst individuals in elite archiving and also meet the minimum Euclidean distance with all individuals in elite archiving; if yes, inserting the generated new individual into elite archive; and when the number of individuals in the elite archive exceeds the elite archive capacity, deleting the individuals with the worst fitness in the elite archive, and reserving the individuals with the optimal fitness, wherein the number of the reserved individuals with the optimal fitness is equal to the elite archive capacity.
7. The chiller system energy efficiency optimization method of claim 1, wherein the diversity restart mechanism is as follows: After each generation of iteration, calculating average Euclidean distance among population individuals to obtain a population diversity index; judging whether the population diversity index is lower than a preset diversity index threshold value or not, if so, triggering diversity restarting operation; wherein the diversity restart operation includes: Generating new individuals based on the chaos sequence and the opposite disturbance by combining the pre-calculated elite center of the individuals in the elite archive to obtain restarting individuals, and forming the restarting individuals and the individuals in the elite archive into a new population together.
8. A chiller system energy efficiency optimization system, comprising: the operation data acquisition module is used for acquiring operation data of key equipment in the target water chiller system; The power consumption model construction module is used for constructing a power consumption model of the key equipment in the target water chiller system based on the operation data of the key equipment in the target water chiller system; the objective function construction module is used for establishing an energy efficiency optimization objective function of the objective water chiller system based on a power consumption model of key equipment in the objective water chiller system; The objective function solving module is used for solving an energy efficiency optimizing objective function of the objective water chiller by utilizing an improved integral group intelligent optimizing algorithm to obtain an energy efficiency optimizing result of the objective water chiller system; The improved whole intelligent optimization algorithm adopts a whole intelligent optimization algorithm which is introduced with a hybrid opposition-chaos initialization strategy, a self-adaptive operator scheduling mechanism based on performance feedback, an elite filing updating strategy and a diversity restarting mechanism.
9. An electronic device, comprising: a processor adapted to execute a computer program; A computer readable storage medium having a computer program stored therein, which when executed by the processor, performs the chiller system energy efficiency optimization method of any one of claims 1-7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the chiller system energy efficiency optimization method of any one of claims 1-7.

Description

Method, system, equipment and medium for optimizing energy efficiency of water chilling unit system Technical Field The invention belongs to the technical field of heating ventilation air conditioning systems, and particularly relates to a method, a system, equipment and a medium for optimizing energy efficiency of a water chilling unit system. Background In the energy management of large public buildings, a heating ventilation air conditioning (Heating Ventilation and Air Conditioning, HVAC) system is an energy-consuming user, the energy consumption of the HVAC system accounts for 40% -60% of the total energy consumption of the building, and the running efficiency of a water chilling unit system directly determines the energy utilization level and the running cost of the whole building as a core cold source of the HVAC system, so that the energy efficiency optimization of the water chilling unit system has important significance for reducing the energy consumption of the public buildings and improving the energy utilization efficiency. In the aspect of energy efficiency optimization of a water chilling unit system, a control strategy based on an empirical rule or an optimization method based on mechanism modeling is generally adopted by the traditional means, wherein the control strategy based on the empirical rule depends on preset logic and cannot cope with nonlinear influence of load fluctuation and equipment coupling under complex working conditions, so that the optimization effect is poor, and the optimization method based on the mechanism modeling is highly sensitive to parameter precision although being feasible theoretically, is insufficient in robustness in practical application and is difficult to adapt to various complex and variable working conditions. In order to cope with the defects of the traditional means, the prior art provides an optimization method based on an integral group intelligent optimization algorithm (Holistic Swarm Optimization, HSO), and the optimization method has strong global exploration capacity and low parameter dependence, but random initialization of the optimization method leads to uneven initial population distribution, incapability of adapting load change of a fixed search operator and lack of a diversity maintenance mechanism, is extremely easy to cause problems of premature convergence, exploration-development unbalance and insufficient stability, is directly applied to the optimization of a water chilling unit system, and is difficult to meet the actual requirements of high efficiency, stability and energy conservation of the water chilling unit system of a large public building. The existing whole group intelligent Optimization algorithm, such as Particle Swarm Optimization (PSO) and genetic algorithm (Genetic Algorithm, GA), has the core problems that firstly, the solution space is explored insufficiently in the initial period of searching, the Optimization result (namely premature convergence) is easy to fall into local Optimization, the global Optimization of the system cannot be achieved, secondly, a dynamic self-adaptive adjustment mechanism is lacking, a fixed searching operator is difficult to balance global exploration (expanding solution space coverage) and local development (fine Optimization) in a high-dimensional coupling system, the Optimization efficiency is low, thirdly, the population diversity is fast in attenuation, the convergence stability is poor under complex load change, the fluctuation of the Optimization result is large, and the requirement of stable operation of the system in actual engineering is difficult to meet. Disclosure of Invention Aiming at the technical problems existing in the prior art, the invention provides a method, a system, equipment and a medium for optimizing the energy efficiency of a water chilling unit system, which are used for solving the technical problems of premature convergence, unbalanced exploration and development and insufficient stability of the existing optimization method based on the whole group intelligent optimization algorithm, and are difficult to meet the actual requirements of high efficiency, stability and energy conservation of the water chilling unit system of a large public building. In order to achieve the above purpose, the invention adopts the following technical scheme: The invention provides a method for optimizing energy efficiency of a water chilling unit system, which comprises the following steps: Acquiring operation data of key equipment in a target water chiller system; Constructing a power consumption model of key equipment in the target water chiller system based on operation data of the key equipment in the target water chiller system; establishing an energy efficiency optimization objective function of the target water chiller system based on a power consumption model of key equipment in the target water chiller system; Solving an energy efficiency optimization objective funct