Energy-saving optimization method for electrically-driven inter-vehicle air conditioning system based on IBK-IPS

Abstract

The invention discloses an energy-saving optimization method for an electrically-driven inter-vehicle air conditioning system based on IBK-IPS, and the method is characterized in that the method comprises the following steps: S1, building an energy consumption and constraint condition mathematical model of each device, constructing a system operation energy consumption optimization objective function, and determining target parameters; s2, the target parameters are optimized through an IBK algorithm and an IPS algorithm, so that two sets of initial values of the optimal operation parameters of the system are obtained; s3, exchanging individuals of the IBK population and the IPS population by using an immigrant operator, and then respectively performing iteration; and S4, after the number of iterations is reached, judging whether an output condition is met or not, if not, repeating the steps S3 and S4, and if yes, taking the target parameter at the moment as the optimal operation parameter. The method has the advantages that mutual constraint of all equipment of the air conditioning system is considered, and energy can be dynamically saved to improve the energy-saving rate and the working efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning system control, and particularly to an energy-saving optimization method for an electric drive workshop air conditioning system based on IBK-IPS. Background Art

[0002] In recent years, with the continuous use of large industrial air conditioning systems, building energy consumption has been increasing year by year, and energy conservation and consumption reduction in buildings have become one of the key tasks for cost control in various application units. In the research on energy-saving optimization of air conditioning systems, various methods have been used to optimize the operating energy consumption of the systems, which can effectively improve the energy-saving efficiency and control effect of the air conditioning systems, but there are still the following two deficiencies: 1) In current research, most are aimed at energy-saving optimization of single equipment in the air conditioning system, while there is less research on overall energy consumption optimization of the air conditioning system; 2) In current research, when modeling constraint conditions, mainly the physical constraints of each equipment in the air conditioning system are considered, while the energy consumption offset phenomenon caused by mutual constraints between equipment is ignored.

[0003] Therefore, starting from the overall air conditioning system, conducting research on dynamic energy-saving optimization methods considering the mutual constraints of each equipment in the air conditioning system is of great significance for energy conservation and consumption reduction of the air conditioning system, and even for reducing enterprise manufacturing costs and promoting green development. Summary of the Invention

[0004] Aiming at the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide an energy-saving optimization method for an electric drive workshop air conditioning system that considers the mutual constraints of each equipment in the air conditioning system, can dynamically save energy to improve the energy-saving rate and working efficiency of the system

[0005] To solve the above technical problem, the present invention adopts the following technical solution:

[0006] An energy-saving optimization method for an electric drive workshop air conditioning system based on IBK-IPS, characterized by comprising the following steps:

[0007] S1. Establish a mathematical model of energy consumption and constraint conditions for each equipment, construct an optimization objective function for system operating energy consumption, and determine target parameters;

[0008] S2. Use the IBK algorithm and the IPS algorithm to optimize the target parameters respectively, so as to obtain two groups of initial values of the optimal operating parameters of the system;

[0009] S3. Use the immigration operator to exchange the individuals of the IBK population and the IPS population, and then perform iteration respectively;

[0010] S4. After reaching the iteration times, judge whether the output condition is satisfied. If not, repeat steps S3 and S4. If satisfied, take the target parameters at this time as the optimal operating parameters.

[0011] Furthermore, in step S1, taking the minimum system operation energy consumption as the optimization objective, the established mathematical model of system operation energy consumption is as follows:

[0012]

[0013] Where: W is the system operation energy consumption; P is the system operation power; P 1,i is the operation power of the i-th chiller; P 2,i is the operation power of the i-th cooling water pump; P 3,i is the operation power of the i-th chilled water pump; P 4,i is the operation power of the i-th cooling tower; P 5,i is the operation power of the i-th air-conditioning fan; A, B, C, D, E are the numbers of corresponding devices respectively; t is the system operation time;

[0014] The established mathematical model of constraint conditions is as follows:

[0015]

[0016] Where, T q,i is the cooling water inlet temperature; T d,o is the chilled water outlet temperature, G q is the cooling water pump flow rate, G d is the chilled water pump flow rate, F a is the air volume of the cooling tower fan, V a is the air volume of the air-conditioning fan, G z is the evaporator flow rate; Q e is the chiller load; T d,i is the chilled water inlet temperature; C is the specific heat capacity of water; and are model coefficients; G l is the condenser flow rate; P1 is the chiller operation power; T q,o is the cooling water outlet temperature; and are model coefficients; Q t is the heat exchange capacity of the cooling tower; F a is the air volume of the cooling tower; T q,i is the inlet temperature of the cooling tower; T sq is the wet bulb temperature; β 1,...,5 is a model coefficient; Q e is the chiller load; Q e0 is the rated chiller load; T sq0 is the rated value of the wet bulb temperature; T d,i0 is the rated value of the chilled water inlet temperature; G k is the water flow rate of the air-conditioning unit; Gk0 is the rated value of the water flow rate of the air-conditioning unit; V a is the air volume of the air-conditioning unit; V a0 is the rated value of the air volume of the air-conditioning unit; χ1 and χ2 are model coefficients.

[0017] Furthermore, the energy consumption mathematical model includes a chiller energy consumption mathematical model, a variable-frequency water pump energy consumption mathematical model, a cooling tower energy consumption mathematical model, and an air-conditioning fan energy consumption mathematical model;

[0018] The chiller energy consumption mathematical model is:

[0019]

[0020] In the formula: W1 is the operating energy consumption of the chiller; P1 is the operating power of the chiller; t is the system operating time; T q,i is the cooling water inlet temperature; T d,o is the chilled water outlet temperature; Q e is the chiller load; a 1,2,...,6 is the coefficient of the model;

[0021] The variable-frequency water pump energy consumption mathematical model is:

[0022] W2 = P2·t = [b1 + b2·G q + b3·G q 2 ·t

[0023] W3 = P3·t = [c1 + c2·G d + c3·G d 2 ·t

[0024] In the formula: W2 is the operating energy consumption of the cooling water pump; P2 is the operating power of the cooling water pump; G q is the cooling water pump flow rate; b 1,2,...,3 is the model coefficient; W3 is the operating energy consumption of the chilled water pump; P3 is the operating power of the chilled water pump; G d is the chilled water pump flow rate; c 1,2,...,3 is the model coefficient; t is the system operating time;

[0025] The cooling tower energy consumption mathematical model is:

[0026]

[0027] In the formula: W4 is the operating energy consumption of the cooling tower; P4 is the operating power of the cooling tower; P 40 is the rated power of the cooling tower; F a is the actual air volume of the cooling tower fan; F a0 is the rated air volume of the cooling tower fan; e1,2,3 is the model coefficient; t is the system operation time;

[0028] The mathematical energy consumption model of the air-conditioning fan is:

[0029] W5 = P5·t = (d1 - d2V a 3 - d3V a 2 + d4V a )·t

[0030] In the formula: W5 is the operation energy consumption of the air-conditioning fan; P5 is the operation power of the air-conditioning fan; V a is the air volume of the air-conditioning fan, d 1,2,...,4 is the model coefficient; t is the system operation time.

[0031] Furthermore, the IBK algorithm is obtained by improving the attack behavior and migration behavior of the BK algorithm using an oscillation algorithm on the basis of the BK algorithm. The improved mathematical model of the attack behavior of the black-winged kite is:

[0032]

[0033] In the formula: y t ij and y t+1 ij are the positions of the i-th black-winged kite in the j-th dimension at the t-th and t + 1-th iterations; n is a parameter of the attack behavior model; r is a random number in [0, 1]; p is a constant with a value of 0.9;

[0034] The calculation formula of n is as follows:

[0035]

[0036] In the formula: N is the total number of iterations, t is the current number of iterations;

[0037] The improved mathematical model of the migration behavior of the black-winged kite is:

[0038]

[0039] In the formula: L t j and L t+1 j are the leaders of the black-winged kite scores in the j-th dimension at the t-th and t + 1-th iterations; y t ij and y t+1 ij are the positions of the i-th black-winged kite in the j-th dimension at the t-th and t + 1-th iterations; F iis the current position of any black-winged kite in the j-th dimension at the t-th iteration; F ri is the random position of any black-winged kite in the j-th dimension at the t-th iteration; m is the parameter of the migration behavior model; C(0, 1) is the definition of Cauchy mutation;

[0040] The calculation formula of m is as follows:

[0041]

[0042] Furthermore, based on the particle swarm algorithm, the IPS algorithm first improves the PS algorithm using a second-order evolution equation, and then uses a dynamic algorithm to adjust the inertia weight ω of the PS algorithm.

[0043] Furthermore, the update formula of the second-order particle swarm algorithm obtained by improving the PS algorithm using a second-order evolution equation is:

[0044]

[0045] In the formula, the values of λ1 and λ1 are:

[0046]

[0047] In the formula: X i (t - 2) is the position of the i-th particle at time t - 2; n is a random parameter; λ1 and λ2 are random parameters; t is the current iteration number; G max is the maximum iteration number.

[0048] Furthermore, the parameters of the dynamic algorithm include an evolution factor s and an aggregation factor a; the calculation formula of the evolution factor s is:

[0049]

[0050] s ∈ (0, 1]

[0051] In the formula: p best (t) is the individual extreme value at the current iteration step, P best (t - 1) is the individual extreme value at the previous iteration step;

[0052] The calculation formula of the aggregation factor a is:

[0053]

[0054] a ∈ (0, 1]

[0055] In the formula: Z is the number of particles, g {i,best} is the swarm extreme value of the i-th particle at the current iteration step;

[0056] The inertia weight ω, evolution factor s, and aggregation factor a satisfy the following functional relationship:

[0057] ω = f(s, a).

[0058] In summary, the present invention has the advantages of considering the mutual constraints of various devices in the air conditioning system, being able to dynamically save energy to improve the energy saving rate and working efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a working flow chart of the air conditioning system for the electric drive workshop.

[0060] Figure 2 It is an optimization flow chart of this embodiment.

[0061] Figure 3 It is a schematic diagram of the mutual constraint relationship.

[0062] Figure 4 It is an optimization flow chart of the IBK-IPS algorithm.

[0063] Figure 5 It is a comparison chart of the system operation energy consumption before and after optimization.

[0064] Figure 6 It is a comparison chart of the system coefficient of performance before and after optimization.

[0065] Figure 7 It is a performance comparison chart of four algorithms.

[0066] Figure 8 It is a schematic diagram of the cooling water system simulation model.

[0067] Figure 9 It is a simulation curve chart of the cooling water system.

[0068] Figure 10 It is a simulation model diagram of the chilled water system.

[0069] Figure 11 It is a simulation curve chart of the chilled water system.

[0070] Figure 12 It is a test flow chart for practical application.

[0071] Figure 13 It is a statistical chart of the test results for practical application. DETAILED DESCRIPTION OF THE INVENTION

[0072] The present invention will be further described in detail below in conjunction with the embodiments.

[0073] In this embodiment, an electric drive workshop of an enterprise is taken as an example. This electric drive workshop mainly produces the electric drive assembly system for new energy electric vehicles, covering specifications from 100KW to 400KW. On the one hand, workstations such as the housing gluing, EOL inspection, and water channel airtightness in the production process of the electric drive system have strict requirements for temperature and humidity, and workstations such as battery chip installation have strict requirements for the cleanliness of the workshop; on the other hand, workstations such as safety regulations inspection and electrical performance inspection are more sensitive to changes in environmental parameters such as temperature, humidity, and cleanliness in the workshop. In order to ensure the production quality of products and the stability of the detection process, the enterprise has built the electric drive workshop into a constant temperature and humidity workshop of Class 300,000, with a monitoring level of CG3. The cleanliness control standard of this workshop is that the number of dust particles larger than 0.5 microns per cubic meter (per liter) of air ≤ 105 million pc / m 3 , and the number of dust particles greater than or equal to a certain micron per cubic meter (per liter) of air ≤ 60,000 pc / m 3 ; the temperature control standard is 18 - 28°C; the relative humidity control standard is 45 - 65RH%. Therefore, a high-performance process air conditioning system is required in the electric drive workshop to meet the requirements of the production environment in the workshop.

[0074] According to preliminary statistics, the energy consumption cost of the air conditioning system in the electric drive workshop is as high as more than 700,000 yuan per year, exceeding 80% of the total energy consumption cost of the workshop. Thus, it is extremely urgent to save energy and reduce consumption for the air conditioning system in the electric drive workshop. The air conditioning system in the electric drive workshop adopts a full-water system, which consists of a chiller, a cooling water pump, a chilled water pump, and a cooling tower inside, and is mainly composed of 76 air conditioning fans outside. The working process of the air conditioning system in the electric drive workshop is as Figure 1 shown, and the parameter models of each device are shown in Table 1.

[0075] Table 1 Equipment Parameters of the Air Conditioning System in the Electric Drive Workshop

[0076]

[0077]

[0078] Through investigation and analysis, it is found that the main reasons for the high operating energy consumption of the air conditioning system in the electric drive workshop are mainly the following two aspects:

[0079] (1) Excessive load. The operating parameters of each device in the air conditioning system of the electric drive workshop are set according to the maximum demand, but the system is basically in a partial load working state during the actual operation process, which will lead to excessive load and thus increase energy consumption.

[0080] (2) Energy consumption offset. There are mutual constraints and coupling effects among the devices in the air conditioning system of the electric drive workshop. When a certain device reaches the optimal working efficiency, it will cause the working efficiency of another device to decline, which will lead to energy consumption offset and thus increase energy consumption.

[0081] In order to reduce the energy consumption of the air-conditioning system in the electric drive workshop, based on the IBK-IPS algorithm, this embodiment proposes a dynamic energy-saving optimization method considering the mutual constraints of various equipment in the air-conditioning system to achieve the goal of overall energy-saving optimization of the system.

[0082] Optimization idea: The high operating energy consumption of the air-conditioning system in the electric drive workshop is mainly caused by two problems: overloading and energy consumption offset. To solve these two problems, based on the IBK-IPS algorithm, this embodiment proposes a dynamic energy-saving optimization method considering the mutual constraints of various equipment in the air-conditioning system. The optimization idea is as Figure 2 shown, and the specific logic of the optimization is as follows:

[0083] (1) Identify 6 groups of parameters that need to be optimized in the air-conditioning system, namely the cooling water inlet temperature T q,i , the chilled water outlet temperature T d,o , the cooling water pump flow rate G q , the chilled water pump flow rate G d , the cooling tower air volume F a , and the air volume V of the air-conditioning unit a ;

[0084] (2) Use the IBK algorithm and the IPS algorithm to optimize the target parameters respectively, so as to obtain 2 groups of initial values of the optimal operating parameters of the system;

[0085] (3) Use the immigration operator to exchange the individuals of the IBK population and the IPS population, and iterate N times again;

[0086] (4) Judge whether the output condition is satisfied. If not, exchange the population individuals again; if satisfied, output the final value of the optimal operating parameters of the air-conditioning system.

[0087] Establish a mathematical model, which includes an energy consumption mathematical model and constraint conditions. Among them, the energy consumption mathematical model includes a chiller energy consumption mathematical model, a variable-frequency water pump energy consumption mathematical model, a cooling tower energy consumption mathematical model, and an air-conditioning fan energy consumption mathematical model.

[0088] Establishment of the chiller energy consumption mathematical model: The energy consumption of the chiller accounts for about 50% of the total system energy consumption and is the equipment with the most energy consumption in the air-conditioning system. The energy consumption of the chiller is mainly related to the cooling water inlet temperature, the chilled water outlet temperature, and the unit load. The established chiller energy consumption mathematical model is as follows:

[0089]

[0090] In the formula: W1 is the operating energy consumption of the chiller; P1 is the operating power of the chiller; t is the system operating time; T q,i is the cooling water inlet temperature; T d,o is the chilled water outlet temperature; Qe is the load of the chiller; a 1,2,...,6 is the coefficient of the model.

[0091] Establishment of the mathematical model of the energy consumption of the variable-frequency water pump: The variable-frequency water pump is the second largest energy-consuming equipment in the air-conditioning system. The energy consumption of the variable-frequency water pump is mainly related to parameters such as the input power of the water pump, the output power of the water pump, the motor efficiency, the water pump efficiency, and the frequency converter efficiency. The output power of the variable-frequency water pump is calculated as follows:

[0092]

[0093] In the formula: p 2,o is the output power of the variable-frequency water pump; ρ is the density of water; g is the acceleration due to gravity; H is the head of the water pump; G is the flow rate of the variable-frequency water pump.

[0094] The input power of the variable-frequency water pump is related to the output power, the water pump efficiency, the motor efficiency, and the frequency converter efficiency. The calculation of the input power is as follows:

[0095]

[0096] In the formula: p 2,i is the input power of the variable-frequency water pump; p 2,o is the output power of the variable-frequency water pump; η1 is the efficiency of the variable-frequency water pump; η2 is the motor efficiency; η3 is the efficiency of the frequency converter.

[0097] The motor efficiency η2 and the frequency converter efficiency η3 can be calculated using empirical formulas. The calculation results are as follows:

[0098] η2 = 0.67483×(1.08 - e -5.648k ) (3)

[0099] η3 = 0.4258 + 1.674k - 1.2086k 2 + 0.3147k 3 (4)

[0100] In the formula: k is the speed ratio before and after frequency conversion.

[0101] Fitting the 5 parameters of the variable-frequency water pump, the mathematical model of the power consumption of the variable-frequency water pump is obtained as follows:

[0102] W2 = P2·t = [b1 + b2·G q + b3·G q 2 ·t (5)

[0103] W3 = P3·t = [c1 + c2·G d + c3·G d 2 ·t (6)

[0104] Where: W2 is the operating energy consumption of the cooling water pump; P2 is the operating power of the cooling water pump; G q is the flow rate of the cooling water pump; b 1,2,...,3 is the model coefficient; W3 is the operating energy consumption of the chilled water pump; P3 is the operating power of the chilled water pump; G d is the flow rate of the chilled water pump; c 1,2,...,3 is the model coefficient; t is the system operation time.

[0105] Establishment of the mathematical model for the energy consumption of the cooling tower: The energy consumption of the cooling tower is mainly related to the air volume of the cooling tower fan and is the energy-consuming equipment in the air-conditioning system second only to the chiller and the variable-frequency water pump. The established mathematical model for the power consumption of the cooling tower is as follows:

[0106]

[0107] Where: W4 is the operating energy consumption of the cooling tower; P4 is the operating power of the cooling tower; P 40 is the rated power of the cooling tower; F a is the actual air volume of the cooling tower fan; F a0 is the rated air volume of the cooling tower fan; e 1,2,3 is the model coefficient; t is the system operation time.

[0108] Establishment of the mathematical model for the energy consumption of the air-conditioning fan: The energy consumption of the air-conditioning fan consists of two parts: fresh air energy consumption and return air energy consumption. Assume the number of employees in the electric drive workshop is M, and each person requires a fresh air volume of D. The total fresh air volume V x is:

[0109] V x = M·D (8)

[0110] The established mathematical model for the energy consumption of the fresh air fan is as follows:

[0111] P x = k x ·M·D (9)

[0112] Where: P x is the operating power of the fresh air fan; k x is the energy consumption coefficient of the fresh air fan.

[0113] When the total return air volume is V h , the ratio of the fresh air return volume is as follows:

[0114]

[0115] Where: k is the ratio of the fresh air return volume.

[0116] The established mathematical model for the energy consumption of the return air fan is as follows:

[0117]

[0118] Where: P h is the operating power of the fresh (return) air fan, kW h is the energy consumption coefficient of the fresh (return) air fan.

[0119] The energy consumption of the fresh air fan and the return air fan is fitted into an overall energy consumption model, and the established air-conditioning fan power consumption model is as follows:

[0120] W5 = P5·t = (P x + P h )·t (12)

[0121] W5 = P5·t = (d1 - d2V a 3 - d3V a 2 + d4V a )·t (13)

[0122] Where: W5 is the operating energy consumption of the air-conditioning fan; P5 is the operating power of the air-conditioning fan; V a is the air volume of the air-conditioning fan, d 1,2,...,4 is the model coefficient; t is the system operation time.

[0123] The constraint conditions include physical constraints and mutual constraints. The physical constraints include the following constraints:

[0124] Water temperature constraint: The constraints on the cooling water inlet temperature T q,i and the chilled water outlet temperature T d,o are as follows:

[0125]

[0126] Flow rate constraint: The constraints on the cooling water pump flow rate G q and the chilled water pump flow rate G d are as follows:

[0127]

[0128] Air volume constraint: The constraints on the air volume F a of the cooling tower fan and the air volume V a of the air-conditioning fan are as follows:

[0129]

[0130] Load constraint: The constraint on the chiller load Q e is as follows:

[0131] 0 ≤ Q e ≤ Q e,max (17)

[0132] Mutual constraints: In addition to physical constraints, the air conditioning system in the electric drive workshop also has mutual constraints between the various devices, which are mainly reflected in the energy consumption offset. The mutual constraints between the various devices are as follows: Figure 3 shown.

[0133] Depend on Figure 3 It can be seen that the chiller and the chilled water circulation system are both affected by the chiller load Q e , Chilled water pump flow G d , Chilled water outlet temperature T d,o The chiller and cooling water circulation system are both affected by the chiller load Q e , unit power P1, cooling water pump flow G q and cooling water inlet temperature T q,i The cooling water system and cooling tower are affected by the cooling water pump flow G q , Cooling water inlet temperature T q,i The chilled water circulation system and air conditioning units are affected by the chilled water pump flow G d , Chilled water outlet temperature T d,o impact.

[0134] The heat transfer constraints for the air conditioning system include the chiller, cooling tower, and air conditioning fan. The cooling water pump and chilled water pump serve as a connection and do not have their own heat transfer constraints.

[0135] Chiller heat exchange constraints: The chiller heat exchange constraints include the evaporator and condenser.

[0136] The evaporator heat transfer is mainly affected by the unit load and the chilled water inlet and outlet temperatures. The established evaporator heat transfer constraints are as follows:

[0137]

[0138] Where: G z is the evaporator flow rate; Q e is the chiller load; T d,o is the chilled water outlet temperature; T d,i is the chilled water inlet temperature; C is the specific heat capacity of water; and are the model coefficients.

[0139] The heat transfer of the condenser is mainly affected by the load, the operating power of the chiller, and the inlet and outlet water temperatures of the cooling water pump. The heat transfer constraints established for the condenser are as follows:

[0140]

[0141] Where: G l is the condenser flow rate; Q eis the load of the chiller; P1 is the operating power of the chiller; T q,o is the outlet temperature of the cooling water; T q,i is the inlet temperature of the cooling water; C is the specific heat capacity of water; and are model coefficients.

[0142] Cooling tower heat transfer constraint: The cooling tower is mainly affected by the heat transfer amount of the cooling tower, the flow rate of the cooling water pump, the air volume of the cooling tower, the inlet temperature of the cooling water, and the wet bulb temperature. The established cooling tower heat transfer constraint is as follows:

[0143]

[0144] In the formula: Q t is the heat transfer amount of the cooling tower; G q is the flow rate of the cooling water pump; F a is the air volume of the cooling tower; T q,i is the inlet temperature of the cooling tower; T sq is the wet bulb temperature; β 1,...,5 are model coefficients.

[0145] Air conditioner fan heat transfer constraint: The air conditioner fan is mainly affected by the unit load, the inlet temperature of the chilled water, the air wet bulb temperature, the water flow rate of the air conditioner fan, and the air volume of the air conditioner fan. The established air conditioner fan heat transfer constraint is as follows:

[0146]

[0147] In the formula: Q e is the load of the chiller; Q e0 is the rated load of the chiller; T sq is the wet bulb temperature; T sq0 is the rated value of the wet bulb temperature; T d,i is the inlet temperature of the chilled water; T d,i0 is the rated value of the inlet temperature of the chilled water; G k is the water flow rate of the air conditioner unit; G k0 is the rated value of the water flow rate of the air conditioner unit; V a is the air volume of the air conditioner unit; V a0 is the rated value of the air volume of the air conditioner unit; χ1 and χ2 are model coefficients.

[0148] Determination of independent variables: There are a total of 8 variables in the mathematical model of the energy consumption of the electric drive workshop air conditioning system, including two types: manipulated variables and disturbance variables. Among them, the manipulated variables are variables that can be independently controlled, with a total of 6; the disturbance variables are variables that cannot be independently controlled, with a total of 2.

[0149] Manipulated variables: The inlet temperature of the cooling water T q,i 、the outlet temperature of the chilled water T d,o, Cooling water pump flow rate G q , Chilled water pump flow rate G d , Cooling tower air volume F a , Air volume of air conditioning unit V a .

[0150] Disturbance variable: Chiller load Q e , Air wet bulb temperature T sq .

[0151] Establishment of objective function: The operating energy consumption of the air conditioning system in the electric drive workshop consists of five parts: chiller, cooling water pump, chilled water pump, cooling tower and air conditioning fan. Taking the minimum operating energy consumption of the system as the optimization objective, the mathematical model of the system operating energy consumption is established as follows:

[0152]

[0153] In the formula: W is the system operating power; P is the system operating power; P 1,i is the operating power of the i-th chiller; P 2,i is the operating power of the i-th cooling water pump; P 3,i is the operating power of the i-th chilled water pump; P 4,i is the operating power of the i-th cooling tower; P 5,i is the operating power of the i-th air conditioning fan; A, B, C, D, E are the numbers of corresponding equipment respectively; t is the system operating time.

[0154] The established mathematical model of constraint conditions is as follows:

[0155]

[0156] IBK-IPS optimization algorithm:

[0157] IBK algorithm: The BK algorithm is a swarm optimization algorithm based on the attack behavior and migration behavior of black-winged kites. It can achieve a good balance between local search and global search and has high adaptability. However, the BK algorithm also has problems of weak early search ability and slow convergence speed. To solve these two problems, in this embodiment, the oscillation algorithm is used to improve the attack behavior and migration behavior of the BK algorithm.

[0158] (1) Improvement of attack behavior: The oscillation algorithm is used to improve the attack behavior model of black-winged kites to enhance the early search ability and accuracy of the model. The improved mathematical model of the attack behavior of black-winged kites is as follows:

[0159]

[0160] In the formula: y t ij and yt+1 ij is the position of the i-th black-winged kite in the j-th dimension during the t-th and (t + 1)-th iterations; n is a parameter of the attack behavior model; r is a random number in [0, 1]; p is a constant with a value of 0.9.

[0161] The calculation formula of n is as follows:

[0162]

[0163] In the formula: N is the total number of iterations, and t is the current iteration number.

[0164] (2) Improvement of migration behavior: The migration behavior model of the black-winged kite is improved by using the oscillation algorithm to enhance the migration convergence speed and search efficiency of the model. The improved mathematical model of the black-winged kite migration behavior is as follows:

[0165]

[0166] In the formula: L t j and L t+1 j are the score leaders of the black-winged kite in the j-th dimension during the t-th and (t + 1)-th iterations; y t ij and y t+1 ij are the positions of the i-th black-winged kite in the j-th dimension during the t-th and (t + 1)-th iterations; F i is the current position of any black-winged kite in the j-th dimension during the t-th iteration; F ri is the random position of any black-winged kite in the j-th dimension during the t-th iteration; m is a parameter of the migration behavior model; C(0, 1) is the definition of Cauchy mutation.

[0167] The calculation formula of m is as follows:

[0168]

[0169] Use the IBK algorithm to optimize the cooling water inlet temperature T q,i 、the chilled water outlet temperature T d,o 、the cooling water pump flow rate G q 、the chilled water pump flow rate G d 、the cooling tower air volume F a and the air volume V of the air conditioning unit a to achieve the energy-saving optimization goal of the system.

[0170] IPS algorithm: The particle swarm (PS) algorithm, as a traditional swarm optimization algorithm, is often used to solve multi-objective optimization problems. The update formulas for the particle coordinate position and flight speed are as follows:

[0171]

[0172] Where: V i (t) and V i (t - 1) are the velocities of the i-th particle at time t and t - 1; X i (t) and X i (t - 1) are the positions of the i-th particle at time t and t - 1; ω is the inertia weight; c1, c2 are learning factors; p best is the individual extreme value; g best is the global extreme value; the random number r 1，2 ∈ [0, 1], and t is the current iteration number.

[0173] In order to make the control effect as accurate as possible, the ITAE criterion is adopted for the calculation of the fitness function J, and the calculation formula is as follows:

[0174]

[0175] Where: N is the maximum number of iteration steps.

[0176] In order to better balance the search capabilities between local and global, in this embodiment, the second-order evolution equation is first used to improve the PS algorithm, and then the dynamic algorithm is used to optimize the PS algorithm. The update formula of the second-order particle swarm algorithm is as follows:

[0177]

[0178] Where the values of λ1 and λ1 are:

[0179]

[0180] Where: X i (t - 2) is the position of the i-th particle at time t - 2; n is a random parameter; λ1 and λ2 are random parameters; t is the current iteration number; G max is the maximum number of iterations. When is true, the global search ability of the particle swarm algorithm needs to be strengthened, and when is true, the local search ability of the particle swarm algorithm needs to be strengthened.

[0181] After improving the PS algorithm into the second-order, a dynamic algorithm is introduced to adjust the inertia weight ω of the PS algorithm. The parameters of the dynamic algorithm mainly include the evolution factor s and the aggregation factor a, and the definitions of the two variable factors are as follows:

[0182] The calculation formula of the evolution factor s is as follows:

[0183]

[0184] Where: pbest (t) is the individual extreme value at the current iteration step, P best (t - 1) is the individual extreme value at the previous iteration step.

[0185] The calculation formula of the aggregation factor a is as follows:

[0186]

[0187] In the formula: Z is the number of particles, g {i,best} is the global extreme value of the i-th particle at the current iteration step.

[0188] The values of the two variable factors are s ∈ (0, 1] and a ∈ (0, 1]. During the optimization process, the evolution factor s is used to reflect the evolution state of the particle population. To study the specific influence between variables, a functional relationship between the inertia weight ω, the evolution factor s, and the aggregation factor a is established, and the relationship formula is as follows:

[0189] ω = f(s, a) (34)

[0190] Through the analysis of the functional relationship in formula (34), it can be seen that the inertia weight ω is inversely proportional to the evolution factor s and directly proportional to the aggregation factor a. The specific function expression is as follows:

[0191] ω = ω0 - 0.8s + 0.2a (35)

[0192] It can be seen from formula (35) that the value range of the inertia weight ω is reduced to ω0 - 0.8 < ω ≤ ω0 + 0.2. Therefore, during the iteration process, the evolution factor s and the aggregation factor a can be used to realize the dynamic adjustment of the inertia weight ω, effectively improving the performance of the PS algorithm.

[0193] Use the IPS algorithm to optimize the cooling water inlet temperature T of the electric drive workshop air conditioning system q,i 、the chilled water outlet temperature T d,o 、the cooling water pump flow rate G q 、the chilled water pump flow rate G d 、the cooling tower air volume F a and the air volume V of the air conditioning unit a to achieve the energy-saving optimization goal of the system.

[0194] The optimization process of the IBK-IPS algorithm is as Figure 4 shown, and the specific process is as follows:

[0195] Step 1: Identify the target.

[0196] There are a total of 6 groups of targets to be identified, which are the cooling water inlet temperature T q,i 、the chilled water outlet temperature T d,o 、the cooling water pump flow rate G q, the flow rate G of the chilled water pump d , the air volume F of the cooling tower a , the air volume V of the air handling unit a .

[0197] Step 2: Population initialization.

[0198] Initialize the IBK population randomly so that the 6 groups of operating parameters are randomly distributed within the constraints; initialize the IPS population in a fixed-step manner so that the 6 groups of operating parameters are evenly distributed within the constraints.

[0199] Step 3: The IBK population and the IPS population operate independently N times.

[0200] The operation processes of the IBK population and the IPS population are the same. First, calculate the system power corresponding to the individuals in the population, then perform parameter optimization, followed by affinity calculation, optimization evaluation, and determination of the initial optimal parameters. Finally, judge whether the output conditions are met.

[0201] Step 4: Exchange of individuals in the population.

[0202] Use the immigration operator to exchange the individuals of the IBK population and the IPS population to achieve communication between the two populations.

[0203] Step 5: The IBK population and the IPS population operate independently M times.

[0204] Step 6: Judge whether the output conditions for the minimum total system energy consumption are met. If not, use the immigration operator to exchange the individuals of the two populations again; if so, output the 6 groups of final values of the optimal operating parameters.

[0205] Step 7: Output 6 groups of optimal operating parameters.

[0206] Result analysis:

[0207] Analysis of optimization results: Apply the dynamic energy-saving optimization method considering mutual constraints proposed in this embodiment based on the IBK-IPS algorithm to the example of energy-saving optimization of the electric drive workshop air conditioning system to obtain the optimization results of operating parameters under different loads. In order to simplify the calculation process during the optimization, this embodiment selects the combination of 1 chiller, 1 cooling water pump, 1 chilled water pump, 3 cooling towers, and 76 air conditioning fans for the research on the optimization of operating parameters. Therefore, the system load Q refers to the load of 1 chiller, and the flow rate G of the chilled water pump d refers to the flow rate of 1 chilled water pump, and the flow rate G of the cooling water pump q refers to the flow rate of 1 cooling water pump, and the air volume F of the cooling tower a refers to the total air volume of 3 cooling tower fans, and the air volume V of the air conditioning fan aRefers to the total air volume of the 76 air conditioning fans. In actual operation, the nine chillers operate in the same manner and principle, thus evenly distributing the system load. The flow rates of the three cooling water pumps and three chilled water pumps are also evenly distributed. The air volume distribution of the three cooling water pumps and 76 air conditioning units is also evenly distributed, as shown in Table 2. The parameter settings for the IBK-IPS algorithm used in the experiment are shown in Table 3.

[0208] Table 2 Optimization results of system operating parameters under different loads

[0209]

[0210]

[0211] Table 3 IBK-IPS algorithm parameter settings

[0212]

[0213] Through the analysis of Table 2, it can be seen that the system operating parameters have been effectively optimized. The comparison before and after optimization is shown in Table 4:

[0214] Table 4 Comparison of operating parameters before and after optimization

[0215]

[0216] After optimization, the system's operating energy consumption is effectively reduced. The energy consumption comparison before and after optimization is shown in Table 5 and Figure 5 As shown:

[0217] Table 5 Comparison of system energy consumption before and after optimization

[0218]

[0219] pass Figure 5 It can be seen that as the load of the chiller gradually increases, the energy saving rate gradually decreases, because the greater the load, the less waste caused by overload.

[0220] After optimization using the IBK-IPS algorithm, the system's operating energy efficiency has been effectively improved. The comparison of the energy efficiency coefficient of performance (EER) of the air-conditioning system before and after optimization is shown in Table 6 and Table 7. Figure 6 shown.

[0221] Table 6 Comparison of refrigeration performance coefficient before and after optimization

[0222]

[0223] pass Figure 6It can be seen that the operating energy efficiency of the optimized system is better than that before optimization under different loads. The energy efficiency after optimization increases with the increase of load, and reaches the maximum operating energy efficiency of 3.58 when the load is 215kW.

[0224] Algorithm effect analysis: In order to verify the effectiveness and advancement of the algorithm proposed in this embodiment, this embodiment analyzes the effect of the IBK-IPS algorithm from two aspects: energy saving effect and algorithm performance.

[0225] (1) Comparison of energy-saving effects: To verify the energy-saving effect of the IBK-IPS algorithm, a comparative experiment was conducted with the PS algorithm, the BK algorithm, and the BK-PS algorithm. The energy-saving effect comparison is shown in Table 7. As shown in Table 7, under the demand loads of 10%, 30%, 50%, 70%, and 90%, the IBK-IPS algorithm has better energy-saving effects than other algorithms.

[0226] Table 7 Comparison of optimization results of PS, BK, BK-PS, and IBK-IPS

[0227]

[0228] (2) Algorithm performance comparison: In order to verify the performance of the IBK-IPS algorithm, a comparative experiment was conducted with the PS, BK, and BK-PS algorithms in terms of convergence speed, search capability, and stability. The performance comparison of the four algorithms is as follows: Figure 7 and shown in Table 8.

[0229] Table 8 Performance comparison of four algorithms

[0230]

[0231] Depend on Figure 7 As shown in Table 8, the IBK-IPS algorithm reached its optimum at the 8th iteration, while the other three algorithms reached their optimum at the 11th, 21st, and 26th iterations, respectively. This improvement in convergence speed and search capability increased by 27.27%, 61.90%, and 69.23%, respectively. This demonstrates that the IBK-IPS algorithm has the best convergence speed and search capability. Furthermore, the IBK-IPS algorithm has a smaller search range than the other three algorithms, enabling it to find the optimum more quickly. Finally, the IBK-IPS algorithm's convergence iteration curve is flatter, demonstrating its superior stability and convergence.

[0232] Energy consumption simulation and practical application analysis:

[0233] Energy consumption simulation analysis - cooling water system energy consumption simulation and results analysis

[0234] (1) Cooling water system simulation model: The simulation model of the cooling water system is as follows: Figure 8As shown. The input variables of the cooling water system simulation model are the inlet temperature of the cooling water T q,i , the outlet temperature of the chilled water T d,o , the wet bulb temperature T sq , the air volume of the cooling tower F a and the load of the chiller Q e , and the output variable is the operating energy consumption W of the cooling water system q .

[0235] (2) Analysis of the simulation results of the cooling water system energy consumption: A simulation experiment is carried out under the condition that the load of a single chiller is 208 kW and the external wet bulb temperature is 28.48 °C. The simulation experiment parameters are set as follows: the simulation duration T = 50 s, the inlet temperature of the cooling water T q,i = 26 + 0.2·T °C, the system load Q = 208 kW, and the external wet bulb temperature T sq = 28.48 °C. The simulation results of the cooling water energy consumption are as Figure 9 shown.

[0236] In Figure 9 , the 5 curves from top to bottom are the simulation curves of the cooling water system energy consumption under the conditions that the outlet temperature of the chilled water is 6.4 °C, 6.7 °C, 7.0 °C, 7.3 °C, and 7.6 °C. In the curve where the outlet temperature of the chilled water T d,o = 6.7 °C, using the interpolation method, it can be known that when the inlet temperature of the cooling water T q,i = 30 °C, the energy consumption of the cooling water system is the smallest, which is highly consistent with the optimization result under the condition that the system load Q = 208 kW and the external wet bulb temperature T sq = 28.48 °C in the "Analysis of Optimization Results", indicating that the optimization result is reliable.

[0237] Energy consumption simulation analysis - Simulation and result analysis of the chilled water system energy consumption:

[0238] (1) Chilled water system energy consumption model: The simulation model of the chilled water system is as Figure 10 shown. The input variables of the chilled water system simulation model are the inlet temperature of the cooling water T q,i , the outlet temperature of the chilled water T d,o , the load of the chiller Q e and the air volume of the air conditioner fan V a , and the output variable is the operating energy consumption W of the chilled water system d .

[0239] (2) Analysis of the simulation results of the chilled water system energy consumption: A simulation experiment is carried out under the condition that the load of a single chiller is 208 kW and the external wet bulb temperature is 28.48 °C. The simulation experiment parameters are set as follows: the simulation duration T = 50 s, the outlet temperature of the chilled water T d,o = 5 + 0.1·T, the inlet temperature of the cooling water T q,i= 30°C, the system load Q = 208 kW, and the air volume ratio λ = 0.89. The simulation results of the chilled water system energy consumption are as Figure 11 shown.

[0240] In Figure 11 , the three curves from top to bottom are the simulation curves of the chilled water system energy consumption under the air volume ratios λ of 1, 0.89, and 0.79 respectively. In the curve of λ = 0.89, using the interpolation method, it can be known that when the chilled water outlet temperature T d,o = 6.7°C, the energy consumption of the chilled water system is the smallest, which is highly consistent with the optimization result under the condition that the system load Q = 208 kW and the water inlet temperature T q,i = 30.01°C in the "Analysis of Optimization Results", indicating that the optimization result is reliable.

[0241] Actual application analysis: To further verify the actual effect of the dynamic energy-saving optimization method of the air-conditioning system proposed in this embodiment, this method was actually applied and tested in the electric drive workshop of Chongqing X Enterprise. The components of the tested air-conditioning system are as follows: 1 chiller, 1 cooling water pump, 1 chilled water pump, 3 cooling towers, and 76 air-conditioning fans. The tests are divided into two groups, (a) and (b), where the test in group (a) represents the operation of the system before optimization, and the test in group (b) represents the operation of the system after optimization. In the specific test, the system load Q and the wet bulb temperature T sq are used as variables, and the cooling water inlet temperature T q,i , the chilled water outlet temperature T d,o , the cooling water pump flow rate G q , the chilled water pump flow rate G d , the cooling tower air volume F a , and the air-conditioning fan air volume V a are used as parameters. The test process is as Figure 12 shown.

[0242] In the test of group (a), the settings of the 6 groups of operating parameters are all set according to the rated values to simulate the actual working scenario of the air-conditioning system in the electric drive workshop before optimization. In the test of group (b), the dynamic energy-saving optimization method considering the mutual constraints of each device in the air-conditioning system proposed in this embodiment is introduced to ensure that the 6 groups of operating parameters can be dynamically adjusted according to the change of the system load. The actual application test results are shown in Table 9 and Figure 13 shown.

[0243] According to Figure 13 it can be known that under the system load Q and the wet bulb temperature T sqWhen they are the same, the optimized operating energy consumption of the air conditioning system in the electric drive workshop is higher than that before optimization. Under five different system loads, the energy-saving efficiency of the system after optimization is 22.62%, 17.24%, 16.94%, 14.97%, and 12.64% respectively. This proves that the dynamic energy-saving optimization method considering the mutual constraints of various equipment in the air conditioning system proposed in this embodiment based on the IBK-IPS algorithm has good energy-saving effects in practical applications.

[0244] Aiming at the problems of high energy consumption and low working efficiency in the operation of the air conditioning system in the electric drive workshop, a dynamic energy-saving optimization method considering the mutual constraints of various equipment in the air conditioning system is proposed based on the IBK-IPS algorithm to improve the energy-saving rate and working efficiency of the system. Through the analysis of the optimization results and simulation results, the following conclusions are obtained: 1) The operating energy consumption of the optimized system is reduced by 11.23% - 34.68%; 2) The operating energy efficiency of the optimized system is increased by 11.53% - 40.78(40.75)%; 3) The IBK-IPS algorithm has better energy-saving effects compared with the PS, BK, and BK-PS algorithms, and the performance indicators of the algorithm are improved by 27.27%, 61.90%, and 69.23% respectively compared with the other three algorithms; 4) The energy-saving optimization method proposed in this embodiment has good energy-saving effects and practicability, and the actual energy-saving rates of the optimized system under five different loads are 22.62%, 17.24%, 16.94%, 14.97%, and 12.64% respectively.

[0245] The above are only the preferred embodiments of the present invention and do not limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An energy-saving optimization method for the air conditioning system in the electric drive workshop based on IBK-IPS, characterized in that, The steps include: S1. Establish a mathematical model of energy consumption and constraints for each device, construct an objective function for optimizing system energy consumption, and determine target parameters; S2. Optimize the target parameters using the IBK algorithm and the IPS algorithm respectively, thereby obtaining two sets of initial values of the optimal operating parameters of the system; S3, use the immigration operator to exchange individuals of the IBK population and the IPS population, and then iterate them separately; S4. After reaching the number of iterations, determine whether the output conditions are met. If not, repeat steps S3 and S4. If so, use the target parameters at this time as the optimal operating parameters.

2. The energy-saving optimization method for the electric drive workshop air conditioning system based on IBK-IPS according to claim 1, characterized in that: In step S1, the minimum system operation energy consumption is taken as the optimization goal, and the established system operation energy consumption mathematical model is: Where: W is the energy consumption of the system operation; P is the system operation power; P 1,i is the operation power of the i-th chiller; P 2,i is the operation power of the i-th cooling water pump; P 3,i is the operation power of the i-th chilled water pump; P 4,i is the operation power of the i-th cooling tower; P 5,i is the operation power of the i-th air-conditioning fan; A, B, C, D, E are the numbers of corresponding devices respectively; t is the system operation time; The established mathematical model of constraints is: Wherein, T q,i is the inlet temperature of the cooling water; T d,o is the outlet temperature of the chilled water, G q is the flow rate of the cooling water pump, G d is the flow rate of the chilled water pump, F a is the air volume of the cooling tower fan, V a is the air volume of the air conditioner fan, G z is the flow rate of the evaporator; T d,i is the inlet temperature of the chilled water; C is the specific heat capacity of water; and are the coefficients of the evaporator heat transfer constraint model; G l is the flow rate of the condenser; P1 is the operating power of the chiller; T q,o is the outlet temperature of the cooling water; and are the coefficients of the condenser heat transfer constraint model; Q t is the heat transfer amount of the cooling tower; F a is the air volume of the cooling tower; T q,i is the inlet temperature of the cooling tower; T sq is the wet bulb temperature; β 1,...,5 is the coefficient of the cooling tower heat transfer constraint model; Q e is the load of the chiller; Q e0 is the rated load of the chiller; T sq0 is the rated value of the wet bulb temperature; T d,i0 is the rated value of the inlet temperature of the chilled water; G k is the water flow rate of the air conditioner unit; G k0 is the rated value of the water flow rate of the air conditioner unit; V a is the air volume of the air conditioner unit; V a0 is the rated value of the air volume of the air conditioner unit; χ1 and χ2 are the coefficients of the air conditioner fan heat transfer constraint model.

3. The energy-saving optimization method of the electric drive workshop air conditioning system based on IBK-IPS according to claim 2, characterized in that, The energy consumption mathematical model includes a chiller energy consumption mathematical model, a variable frequency water pump energy consumption mathematical model, a cooling tower energy consumption mathematical model and an air conditioning fan energy consumption mathematical model; The energy consumption mathematical model of the chiller is: Where: W1 is the operating energy consumption of the chiller; P1 is the operating power of the chiller; t is the system operation time; T q,i is the inlet cooling water temperature; T d,o is the chilled water outlet temperature; Q e is the chiller load; a 1,2,...,6 is the coefficient of the model; The energy consumption mathematical model of the variable frequency water pump is: W2 = P2·t = [b1 + b2·G q + b3·G q 2 ·t W3 = P3·t = [c1 + c2·G d + c3·G d 2 ·t Where: W2 is the operating energy consumption of the cooling water pump; P2 is the operating power of the cooling water pump; G q is the flow rate of the cooling water pump; b 1,2,...,3 is the model coefficient; W3 is the operating energy consumption of the chilled water pump; P3 is the operating power of the chilled water pump; G d is the flow rate of the chilled water pump; c 1,2,...,3 is the model coefficient; t is the system operation time; The cooling tower energy consumption mathematical model is: Where: W4 is the operating energy consumption of the cooling tower; P4 is the operating power of the cooling tower; P 40 is the rated power of the cooling tower; F a is the actual air volume of the cooling tower fan; F a0 is the rated air volume of the cooling tower fan; e 1,2,3 is the model coefficient; t is the system operation time; The mathematical energy consumption model of the air conditioning fan is: W5 = P5·t = (d1 - d2V a 3 - d3V a 2 + d4V a )·t Where: W5 is the operating energy consumption of the air-conditioning fan; P5 is the operating power of the air-conditioning fan; V a is the air volume of the air-conditioning fan, and d 1,2,...,4 is the model coefficient; t is the system operation time.

4. The energy-saving optimization method for the electric drive workshop air-conditioning system based on IBK-IPS according to claim 1, characterized in that The IBK algorithm is based on the BK algorithm and uses an oscillation algorithm to improve the attack behavior and migration behavior of the BK algorithm. The improved mathematical model of the black kite's attack behavior is: where: y t ij and y t+1 ij are the positions of the i-th black-winged kite in the j-th dimension during the t-th and (t + 1)-th iterations; n is a parameter of the attack behavior model; r is a random number in [0, 1]; p is a constant with a value of 0.9; The calculation formula for n is as follows: Where: N is the total number of iterations, t is the current number of iterations; The improved mathematical model of black kite migration behavior is: Where: L t j and L t+1 j are the leaders in the j-th dimension of the black-winged kite scores in the t-th and t+1-th iterations; and y t+1 ij are the positions of the i-th black-winged kite in the j-th dimension in the t-th and t+1-th iteration steps; F i is the current position of any black-winged kite in the j-th dimension in the t-th iteration; F ri is the random position of any black-winged kite in the j-th dimension in the t-th iteration; m is a parameter of the migration behavior model; C(0,1) is the definition of Cauchy mutation; The calculation formula for m is as follows:

5. The energy-saving optimization method for the electric drive workshop air-conditioning system based on IBK-IPS according to claim 1, characterized in that, The IPS algorithm is based on the particle swarm algorithm. It first improves the PS algorithm by using the second-order evolution equation, and then uses a dynamic algorithm to adjust the inertia weight ω of the PS algorithm.

6. The energy-saving optimization method for the electric drive workshop air conditioning system based on IBK-IPS according to claim 5, characterized in that The update formula of the second-order particle swarm algorithm obtained by improving the PS algorithm using the second-order evolution equation is: The values of λ1 and λ2 are: Where: X i (t - 2) is the position of the i-th particle at time t - 2; n is a random parameter; λ1 and λ2 are random parameters; t is the current iteration number; G max is the maximum number of iterations.

7. The energy-saving optimization method for the electric drive workshop air conditioning system based on IBK-IPS according to claim 6, characterized in that: The parameters of the dynamic algorithm include the evolution factor s and the aggregation factor a; the calculation formula of the evolution factor s is: s∈(0,1] where: p best (t) is the individual extreme value at the current iteration step, and P best (t - 1) is the individual extreme value at the previous iteration step; The calculation formula of aggregation factor a is: a∈(0,1] Where: Z is the number of particles, g {i,best} is the group extreme value of the i-th particle under the current iteration number; The inertia weight ω, evolution factor s, and aggregation factor a satisfy the following functional relationship: ω=f(s,a).

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