CN107906810A - A kind of energy saving group control method of salt water cooling system of more handpiece Water Chilling Units cooperations - Google Patents

Abstract

The present invention provides a kind of energy saving group control method of salt water cooling system of more handpiece Water Chilling Units cooperations.The present invention uses improvement short annealing algorithm that under each cooling condition demand, distribution, lifting system overall performance index are optimized to the refrigeration work consumption of handpiece Water Chilling Units to ship cooling system.The foundation of target equation has considered the energy-saving effect of chilled water pump temperature difference control variable-flow measure, and the delivery requirements of freezing water are met by chilled water pump frequency control, while reduces energy loss meaningless in transmitting procedure.By the dynamic property index of adaptive polo placement separate unit handpiece Water Chilling Units, the accuracy of target equation is improved.

Description

A method for energy-saving group control of seawater cooling system with joint operation of multiple chillers

technical field

The invention discloses an energy-saving group control method for a seawater cooling system operated jointly by multiple chillers, and belongs to the technical field of seawater cooling methods for intelligent ships.

Background technique

As the most common form of centralized cooling for ships, the intercooled Freon chiller performs heat exchange with seawater and fresh water respectively to reduce the temperature inside the cabin, as shown in Figure 1. However, due to the large fluctuations in the cooling load in the cabin, the configuration of a single chiller (the unit is selected according to the maximum cooling load), when the system load deviates too much from the design value (low cooling condition), the cooling system will run at low efficiency. Therefore, it is the current development direction of the marine seawater cooling system to configure dual chillers for high-load and low-load conditions, so that the system has high operating efficiency under most working conditions, as shown in Figure 2. In the past, the multi-unit control strategy mostly used the cooling load to control the start and stop of the chiller. Generally, when the load demand is close to the full cooling capacity output of a unit, a new unit is considered to be loaded. However, the best performance index of a single chiller generally does not appear at full load, so there is room for energy saving in which multiple chillers share the load demand compared with a single chiller that undertakes independently.

In 1984, R.J.Hankner, et al. proposed in their research "HVAC system dynamics and energy use buildings-Part I" to control the output of chillers by using an equal average method, that is, the output of a single chiller is equal to the total cooling load demand Multiplied by the load rate, the load rate refers to the quotient of the capacity of each chiller and the sum of the capacities of each chiller. This control strategy is simple and easy to implement, but it cannot achieve optimal energy efficiency.

In 2004, Y. Yao, et al., in their research on "Optimal operation of a large cooling system based on an empirical model", comprehensively considered the frequency conversion and energy-saving operation of chilled water pumps, and proposed to maximize the value of the system performance index (SCOP) as Control objectives, so as to obtain the optimal control strategy of the chiller. However, the decision-making process of this strategy is complex, and the quadratic programming method adopted is not easy to converge under low-load conditions.

In 2008, Yang Tongqing et al. conducted multi-unit loading tests in their research on “Energy-saving Control Strategy Based on Chiller Optimal Control”, and explored the optimal loading point of multiple chillers based on single-unit COP under different demand conditions, but did not It is not clear whether the cooling system achieves the optimal overall energy consumption.

The existing invention (200810182566.5) marine central air-conditioning refrigeration device proposes a marine central air-conditioning refrigeration device, the purpose of which is to provide a marine central air-conditioning refrigeration device with stable refrigeration performance and corrosion resistance, and does not involve how to optimize the energy efficiency of the system.

The existing invention (201210125509.X) a group control method for central air-conditioning refrigeration units proposes to use heat control, an algorithm for group control of chillers, to effectively make chillers run in a more economical and energy-saving state, prolonging equipment life and reducing purpose of energy consumption. Its algorithm does not specify the dividing conditions for the number of chillers to be turned on, that is, how to determine the total design load of 0%, 20%, 38%, 55%, and 70%. Therefore, there is no basis for saying that such division can make chillers operate in a more economical and energy-saving state.

Existing invention (201510819296.4) A computer room group control device based on global correlation optimization and its control method A computer room group control device based on global correlation optimization is proposed, including a central group control device, a water pump control device, a cooling tower control device, The chiller communication device and the air handling unit control device; the water pump control device, the cooling tower control device, the chiller communication device and the air handling unit control device are all connected to the central group control device; the central group control device has a built-in industrial computer, Industrial switches and central processing units; the water pump control device has a built-in first controller and a first smart meter; the cooling tower control device has a built-in second controller and a second smart meter; the air handling unit control device has a built-in third The controller and the third smart meter; the chiller communication device has a built-in building energy protocol gateway; the chiller communication device is connected to the chiller control device, and the chiller control device includes a fourth controller. Its core is to propose the structure and composition of the group control system, but does not describe the specific measures of how to carry out correlation optimization.

The existing invention (201610013560.X) proposes a group control method and system for water chillers The present invention provides a group control method and system for water chillers, wherein the method includes: collecting the chiller corresponding to the currently running chiller load; comparing the collected cooling load with the preset cooling load; when the collected cooling load and the preset cooling load meet the preset conditions, the current The core of the loading or unloading operation of the running chiller is to propose the structure and composition of the group control system, and does not describe the generation method of the preset conditions.

The existing invention (20161010953.4) control method and device of an air-conditioning group control system discloses an air-conditioning system and its control method and device. The air-conditioning system includes a plurality of air-conditioning hosts, and the control method of the air-conditioning system includes: calculating the energy consumption power required by the air-conditioning system; determining the number of air-conditioning hosts that need to be started according to the energy consumption power required by the air-conditioning system; Two air conditioners are turned on at the same time. This invention does not describe the relationship between the determination method of the load threshold and the energy-saving performance when it performs group control of multi-air-conditioning hosts for adding or subtracting machines.

Based on the above literature review, the difficulties of the current multi-unit group control optimization energy-saving technology are as follows: 1. The performance index of the chiller changes with the operating years, and the performance index under partial load needs to be continuously updated to accurately allocate power optimally. 2. The energy consumption of the cooling system includes not only the cooling power of the chiller, but also the transmission energy loss of transporting chilled water. In the optimal control decision-making, the comprehensive influence of the pump frequency conversion operation on the system power consumption should be considered. 3. When optimizing decision-making, since the objective equation covers multiple different energy-using systems and has different conditional constraints, it is easy to fall into the situation of local optimum or slow or non-convergent iteration process when solving optimal power allocation.

Contents of the invention

The purpose of the invention is to reduce the overall energy consumption of the cooling system and improve the operating efficiency of the system.

In order to achieve the above object, the technical solution of the present invention provides a seawater cooling system energy-saving group control method for joint operation of multiple chillers, which is characterized in that it includes the following steps:

Step 1. Establish the system performance index SCOP objective equation

In the formula, n ₁ and n ₂ are the total number of chillers and chilled water pumps respectively, P _{chiller, i} is the power consumption of the i-th chiller, COP _i is the dynamic performance index of the i-th chiller, and C is the chiller Water specific heat capacity, Δt is the temperature difference between supply and return water, G _{0, i} is the rated flow of the i-th chilled water pump, P _{0, i} is the rated power of the i-th chilled water pump;

Step 2. Adaptively calculate the dynamic performance index of a single chiller, where the dynamic performance index COP _i of the i-th chiller is expressed as:

COP _i ＝a _i +b _i ·R _i +c _i ·R _i ²

In the formula, R _i is the partial load rate of the i-th chiller, and the linear coefficients a _i , b _i , and c _i are adaptively updated using the following formula:

Step 3. Determine the electric power constraints, refrigeration power constraints and energy conservation of the system performance index SCOP objective equation, where:

The electric power constraints are:

min(P _{chiller, i} ) ≤ P _{chiller, i} ≤ max(P _{chiller, i} )

min(P _{pump, i} ) ≤ P _{pump, i} ≤ max(P _{pump, i} )

In the formula, P _{pump, i} is the power consumption of the i-th chilled water pump;

The cooling power constraints are:

min(Q _{chiller, i} ) ≤ Q _{chiller, i} ≤ max(Q _{chiller, i} )

In the formula, Q _{chiller, i} is the cooling power of the i-th chiller;

Conservation of energy:

Q _load is the load power;

Step 4, using the fast simulated annealing algorithm to maximize the solution of the system performance index SCOP objective equation, and determine the optimal chiller power allocation;

Step 5: Calculating the chilled water flow of each chiller, wherein the chilled water flow Q _i of the i-th chiller = C×ΔT×P _{chiller, i} ×COP _i .

Preferably, said step four includes process one and process two, wherein:

The first process is to use a higher initial temperature and perturb the model for global fast global optimization, including the following steps:

Step 1.1, generate a new random solution according to the random global disturbance equation;

Step 1.2. Substitute the newly generated random solution into the energy equation. If the energy value decreases, the new solution will be accepted as the optimal solution in the current state. If the energy value increases, it will be judged whether to accept the new solution based on the acceptance probability of the Boltzmann-Gibbs distribution and the Metropolis criterion. As the optimal solution in the current state;

Step 1.3, if the new solution is rejected, return to step 1.1;

Step 1.4, if the new solution is accepted, update the current temperature according to the annealing plan formula 1, the annealing plan formula 1 is:

In the formula, T and T ₀ are the current temperature and the initial temperature, respectively; α is the temperature attenuation coefficient; j is the number of iterations;

The second process is to use a lower initial temperature and perturb the model for local retardation optimization, including the following steps:

Step 2.1, generate a new random solution according to the random local disturbance equation;

Step 2.2. Substitute the newly generated random solution into the energy equation. If the energy value decreases, the new solution is accepted as the optimal solution in the current state. If the energy value increases, the probability and the Metropolis criterion are accepted according to the Boltzmann-Gibbs distribution, hereinafter referred to as the M criterion , to determine whether to accept the new solution as the optimal solution in the current state;

Step 2.3, if the new solution is rejected, return to step 2.1;

Step 2.4, if the new solution is accepted, update the current temperature according to the annealing plan formula 2, the annealing plan formula 2 is:

In the formula, k ₀ is the iteration number of process one; β is the temperature rise factor.

Preferably, it also includes:

Step 6. Calculate the speed of each chiller, where the speed of the i-th chiller is In the formula, Q _i,0 is the rated chilled water flow of the i-th chiller, and n _i,0 is the rated speed of the i-th chiller.

The invention adopts the improved rapid annealing algorithm to optimize the distribution of the cooling power of the chiller in the cooling system of the ship under the requirements of various cooling conditions, and improve the systemic overall performance index (SCOP). The establishment of the objective equation comprehensively considers the energy-saving effect of the temperature difference control variable flow measure of the chilled water pump, and meets the transportation demand of chilled water through the frequency conversion speed regulation of the chilled water pump, and at the same time reduces the unnecessary energy loss in the transmission process. The accuracy of the objective equation is improved by adaptively calculating the dynamic performance index of a single chiller. In the optimization process, the improved fast simulated annealing algorithm is adopted, and the ideal optimization process of optimizing the initial global optimization and optimizing the later local optimization is realized through the design of the staged optimization link, getting rid of the conventional algorithm that is easy to fall into when optimizing the low-load rate. The local optimal problem improves the optimization efficiency of the simulated annealing algorithm. These designs effectively solve the difficulties in realizing multi-unit group control optimization and energy saving at present.

Description of drawings

Figure 1 is a structure diagram of a single-unit seawater cooling system;

Figure 2 is a structural diagram of the chilled water system of the multi-unit seawater cooling system;

Fig. 3 is the annealing temperature curve comparison of rapid annealing and improved rapid annealing algorithm;

Fig. 4 is the optimization process of improving fast simulated annealing algorithm;

Figure 5 shows the COP curves of 50RT and 20RT chillers under different load rates;

Fig. 6 is the SCOP optimization process of the cooling system under the demand condition 45RT;

Figure 7 shows the comparison of performance index before and after system optimization.

Detailed ways

In order to make the present invention more comprehensible, preferred embodiments are described in detail below with accompanying drawings.

The invention provides an energy-saving group control method for a seawater cooling system operated jointly by multiple chillers, comprising the following steps:

Step 1. Establish the system performance index SCOP objective equation

In the formula, n ₁ and n ₂ are the total number of chillers and chilled water pumps respectively, P _{chiller, i} is the power consumption of the i-th chiller, COP _i is the dynamic performance index of the i-th chiller, and C is the chiller Water specific heat capacity, Δt is the temperature difference between supply and return water, G _0,i is the rated flow of the i-th chilled water pump, P _0,i is the rated power of the i-th chilled water pump.

The derivation process of the above formula is:

The system performance index is an index to characterize the energy efficiency of the seawater cooling system, which is defined as the quotient of the system cooling power and the total power consumption of the system:

In the formula, SCOP is the overall performance index of the cooling system, Q _{chiller, i} is the cooling power of the i-th chiller, P _{chiller, i} is the power consumption of the i-th chiller, P _{pump, i} is the i-th chilled water pump power consumption. Generally, chillers and chilled water pumps are configured in one-to-one correspondence, so n ₁ =n ₂ . If the cooling power of the chiller is replaced by a single performance index COP _i , the formula (1) can be transformed into:

Under a certain cooling condition, the cooling load Q is fixed, and the temperature difference between the supply and return water of the variable flow system is constant, then the chilled water flow rate is determined by the following formula:

In the formula, G is the chilled water flow, Q is the cooling load, C is the specific heat capacity of chilled water, and ΔT is the temperature difference between supply and return water. According to the similarity law of the water pump, the electric power of the water pump can be obtained by the following formula:

Where G ₀ is the rated flow of the pump, and P ₀ is its rated power. After substituting equation (4) into equation (2), the objective equation (2) can be transformed into:

Step 2, adaptively calculating the dynamic performance index of a single chiller. It can be deduced from formula (5) that the performance index SCOP of the system is related to the load distribution of each chiller and the single performance index COP under the load distribution. Since the chiller performance index (COP) changes with the operating years, it is necessary to perform adaptive calculation on the performance index of a single chiller before optimizing the overall performance of the system.

The dynamic performance index COP _i of the i-th chiller is expressed as:

COP _i ＝a _i +b _i ·R _i +c _i ·R _i ²

In the formula, R _i is the partial load rate of the i-th chiller, and the linear coefficients a _i , b _i , and c _i are adaptively updated using the following formula:

The above method can obtain the COP value of the unit in system operation in real time through the partial load rate R, so that the single-machine COP-R relationship in the objective equation can be better updated. If the R matrix is irreversible, the least squares recursive method can be used to obtain the COP-R R relational formula.

Step 3. Determine the electric power constraints, refrigeration power constraints and energy conservation of the system performance index SCOP objective equation, where:

The electric power constraints are:

min(P _{chiller, i} ) ≤ P _{chiller, i} ≤ max(P _{chiller, i} )

min(P _{pump, i} ) ≤ P _{pump, i} ≤ max(P _{pump, i} )

In the formula, P _{pump, i} is the power consumption of the i-th chilled water pump;

The cooling power constraints are:

min(Q _{chiller, i} ) ≤ Q _{chiller, i} ≤ max(Q _{chiller, i} )

In the formula, Q _{chiller, i} is the cooling power of the i-th chiller;

Conservation of energy:

Q _load is the load power;

Step 4: Use the fast simulated annealing algorithm to maximize the solution to the SCOP objective equation of the system performance index, and determine the optimal chiller power allocation.

According to the definition of Step 3, by maximizing formula (5), the system can achieve the maximum energy-saving effect under the premise of meeting the cooling capacity demand. In order to avoid the situation where the optimization process is not easy to converge under low load rate, this paper uses the improved fast simulated annealing algorithm to maximize the solution of formula (5).

The improved fast simulated annealing algorithm can be divided into two processes: fast global annealing optimization and slow local annealing optimization, as shown in Figure 3.

Step 4 includes process 1 and process 2, in which:

The first process is to use a higher initial temperature and perturb the model for global fast global optimization, including the following steps:

Step 1.1. Generate a new random solution according to the random global disturbance equation:

x=min(X)+r·(max(X)-min(X))

In the formula, X is the range interval of the solution set; r is a random number between 0 and 1, subject to uniform distribution; x is the newly generated random solution.

Step 1.2. Substitute the newly generated random solution into the energy equation. If the energy value decreases, the new solution will be accepted as the optimal solution in the current state. If the energy value increases, it will be judged whether to accept the new solution based on the acceptance probability of the Boltzmann-Gibbs distribution and the Metropolis criterion. As the optimal solution in the current state;

Step 1.3, if the new solution is rejected, return to step 1.1;

Step 1.4, if the new solution is accepted, update the current temperature according to the annealing plan formula 1, the annealing plan formula 1 is:

In the formula, T and T ₀ are the current temperature and the initial temperature, respectively; α is the temperature attenuation coefficient; j is the number of iterations;

The second process is to use a lower initial temperature and perturb the model for local retardation optimization, including the following steps:

Step 2.1. Generate a new random solution according to the random local disturbance equation:

x _j ＝x _j-1 +(r-0.5)(max(X)-min(X))/L(j)

In the formula, X is the range interval of the solution set; r is a random number between 0 and 1, subject to uniform distribution; x _j is the newly generated random solution; x _j-1 is the random solution generated in the last iteration; L( j) is the search restriction factor, which is positively related to the number of iterations j.

Step 2.2. Substitute the newly generated random solution into the energy equation. If the energy value decreases, the new solution is accepted as the optimal solution in the current state. If the energy value increases, the probability and the Metropolis criterion are accepted according to the Boltzmann-Gibbs distribution, hereinafter referred to as the M criterion , to determine whether to accept the new solution as the optimal solution in the current state;

Step 2.3, if the new solution is rejected, return to step 2.1;

Step 2.4, if the new solution is accepted, update the current temperature according to the annealing plan formula 2, the annealing plan formula 2 is:

In the formula, k ₀ is the iteration number of process one; β is the temperature rise factor.

The improved fast annealing algorithm changes the single perturbation mode of the fast annealing algorithm. Different annealing plans cooperate with the perturbation mode to form an ideal optimization process that optimizes the initial global optimization and optimizes the later local optimization, and solves the problem of solving the optimal It is easy to fall into the difficulty of local optimum or slow convergence or non-convergence in the iterative process during power allocation. Figure 4 describes the implementation process of the improved annealing algorithm.

Step 5. Calculating the chilled water flow of each chiller, wherein, the chilled water flow Q _i of the i-th chiller = C×ΔT×P _{chiller, i} ×COP _i ;

Step 6. Calculate the speed of each chiller, where the speed of the i-th chiller is In the formula, Q _i,0 is the rated chilled water flow of the i-th chiller, and n _i,0 is the rated speed of the i-th chiller.

The invention adopts the improved rapid annealing algorithm to optimize the distribution of the cooling power of the chiller in the cooling system of the ship under the requirements of various cooling conditions, and improve the systemic overall performance index (SCOP). The establishment of the objective equation comprehensively considers the energy-saving effect of the temperature difference control variable flow measure of the chilled water pump, and meets the transportation demand of chilled water through the frequency conversion speed regulation of the chilled water pump, and at the same time reduces the unnecessary energy loss in the transmission process. The accuracy of the objective equation is improved by adaptively calculating the dynamic performance index of a single chiller. In the optimization process, the improved fast simulated annealing algorithm is adopted, and the ideal optimization process of optimizing the initial global optimization and optimizing the later local optimization is realized through the design of the staged optimization link, getting rid of the conventional algorithm that is easy to fall into when optimizing the low-load rate. The local optimal problem improves the optimization efficiency of the simulated annealing algorithm. These designs effectively solve the difficulties in realizing multi-unit group control optimization and energy saving at present.

Further illustrate the present invention below in conjunction with specific data:

An example is equipped with a chiller with a rated cooling capacity of 50RT and 20RT to meet the needs of the ship under high and low cooling conditions. The performance coefficient under partial load is shown in Figure 5. Each unit can realize cooling power regulation with a power interval of 10%. The 50RT unit is equipped with a chilled water pump with a rated power of 5kw and its rated flow rate is 120T/h, and the 20RT unit is equipped with a chilled water pump with a rated power of 2kw and its rated flow rate is 60T/h.

Based on the above assumptions, the parameters are brought into formula (5), then formula (5) can be simplified as:

In the formula, Q ₁ , Q ₂ , P ₁ , P ₂ , Q _{1, N} , Q _{2, N} correspond to the operating cooling power, operating electrical power, and rated cooling power of the 50RT unit and the 20RT unit, respectively. P _{3, N} , P _{4, N} are the rated power of the 10kw and 5kw water pumps respectively.

Under a certain system working condition Q _d , the objective equation can be abbreviated as:

where Q ₁ +Q ₂ ≈Q. The optimization of the objective equation can be considered as the optimal allocation of the cooling power of the two chillers under the condition of meeting the requirements of the working conditions.

Figure 6 describes the power optimization process of the cooling system in the case of demand condition 45RT. During the early iterations, the global perturbation equation generates random solutions. In the case of higher temperature, the non-optimal solution also has a higher probability to meet the Metropolis criterion and be accepted, so the system performance index fluctuates greatly in the initial stage of optimization. In the middle and late stages of the iterative process, stochastic solutions are generated by local perturbation equations. Due to the rapid drop in temperature, the probability that the non-optimal solution satisfies the Metropolis criterion gradually decreases to 0, so in the later stage of optimization, SCOP converges, and the power allocation of the system reaches the optimum.

Figure 7 describes the comparison of the system performance index under different cooling conditions before and after optimization. Before the optimization, the system adopted the conventional full-load expansion strategy. When the 20RT unit was fully loaded, the 50RT unit was turned on, and the chilled water pump operated at a constant flow rate. After optimization, the system allocates the output power of each chiller according to the optimization results. Since the general unit performance (COP) of large-capacity units is higher than that of small-capacity units, the large-capacity units are also preferentially turned on under small operating conditions. Since the chilled water pump operates with variable flow rate controlled by a constant temperature difference, the power consumption of the pump is determined by the cooling capacity. Under small working conditions, the energy consumption saved by the water pump accounts for a large proportion, so the system performance index before and after optimization is quite different. In the case of large working conditions, due to the gradual full load of the dual units, the difference in power consumption of the chilled water pump under variable flow and constant flow gradually decreases, so the system performance index curves before and after optimization are also gradually closed. When the cooling condition is 5RT, the optimized SCOP increases by 2.36, which is the highest increase under all partial load conditions. Under each load condition after optimization, SCOP increased by 0.88 on average.

Table 1 lists the different strategies of increasing and decreasing machines before and after optimization. Compared with the conventional system of increasing and decreasing units, when the cooling load changes after optimization, the power adjustment of the unit is more frequent. In order to avoid the short-term fluctuation of the cooling load, the system frequently loads and unloads the unit, and the real-time measured load data is smoothed to reduce the impact of load disturbance on system stability. Many documents have described it in detail, so I won't repeat it here.

Table 1 Comparison of system increase and decrease strategies before and after optimization

Claims (3)

1. a kind of energy saving group control method of salt water cooling system of more handpiece Water Chilling Units cooperations, it is characterised in that including following step Suddenly：

Step 1: establish system performance index SCOP target equations

<mrow> <mi>S</mi> <mi>C</mi> <mi>O</mi> <mi>P</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> </munderover> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>i</mi> <mi>l</mi> <mi>l</mi> <mi>e</mi> <mi>r</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>COP</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> </munderover> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>i</mi> <mi>l</mi> <mi>l</mi> <mi>e</mi> <mi>r</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </munderover> <mrow> <mo>{</mo> <mrow> <msup> <mrow> <mo>&amp;lsqb;</mo> <mfrac> <mrow> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>i</mi> <mi>l</mi> <mi>l</mi> <mi>e</mi> <mi>r</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>COP</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>C</mi> <mo>&amp;times;</mo> <mi>&amp;Delta;</mi> <mi>T</mi> <mo>&amp;times;</mo> <msub> <mi>G</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </mfrac> <mo>&amp;rsqb;</mo> </mrow> <mn>3</mn> </msup> <mo>&amp;times;</mo> <msub> <mi>P</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> <mo>}</mo> </mrow> </mrow> </mfrac> </mrow>

In formula, n₁、n₂The respectively total number of handpiece Water Chilling Units and chilled water pump, P_{Chiller, i}Electric work is used for i-th handpiece Water Chilling Units Rate, COP_iFor the dynamic property index of i-th handpiece Water Chilling Units, C is chilled water specific heat capacity, and Δ t is supply backwater temperature difference, G_{0, i}For i-th The metered flow of platform chilled water pump, P_{0, i}For the rated power of i-th chilled water pump；

Step 2: the dynamic property index of adaptive polo placement separate unit handpiece Water Chilling Units, wherein, the dynamic property of i-th handpiece Water Chilling Units Index COP_iIt is expressed as：

COP_i=a_i+b_i·R_i+c_i·R_i ²

In formula, R_iFor the part load ratio of i-th handpiece Water Chilling Units, linear coefficient a_i, b_i, c_iUsing following formula adaptive updates：

<mrow> <mo>&amp;lsqb;</mo> <mtable> <mtr> <mtd> <msub> <mi>a</mi> <mi>i</mi> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mi>i</mi> </msub> </mtd> <mtd> <msub> <mi>c</mi> <mi>i</mi> </msub> </mtd> </mtr> </mtable> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>i</mi> <mi>n</mi> <mi>v</mi> <mrow> <mo>(</mo> <mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <msub> <mi>R</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <msub> <mi>R</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>&amp;CenterDot;</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <msub> <mi>R</mi> <mi>i</mi> </msub> </mtd> <mtd> <mrow> <msup> <msub> <mi>R</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <msub> <mi>R</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <msub> <mi>R</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>&amp;CenterDot;</mo> <msub> <mi>COP</mi> <mi>i</mi> </msub> </mrow>

Step 3: determine electrical power constraints, refrigeration work consumption constraints and the energy of system performance index SCOP target equations Conservation is measured, wherein：

Electrical power constraints is：

min(P_{Chiller, i})≤P_{Chiller, i}≤max(P_{Chiller, i})

min(P_{Pump, i})≤P_{Pump, i}≤max(P_{Pump, i})

In formula, P_{Mmp, i}For the electric power of i-th chilled water pump；

Refrigeration work consumption constraints is：

min(Q_{Chiller, i})≤Q_{Chiller, i}≤max(Q_{Chiller, i})

In formula, Q_{Chiller, i}For the refrigeration work consumption of i-th handpiece Water Chilling Units；

The conservation of energy：

Q_loadFor bearing power；

Step 4: carrying out maximization solution to system performance index SCOP targets equation using fast simulated annealing algorithm, determine Optimal handpiece Water Chilling Units power distribution；

Step 5: the chilled-water flow of each handpiece Water Chilling Units is calculated, wherein, the chilled-water flow Q of i-th handpiece Water Chilling Units_i=C × ΔT×P_{Chiller, i}×COP_i。

2. a kind of energy saving group control method of salt water cooling system of more handpiece Water Chilling Units cooperations as claimed in claim 1, it is special Sign is that the step 4 includes process one and process two, wherein：

Process one is to use higher initial temperature, and Disturbance Model makees global quick global optimizing, includes the following steps：

The random global perturbation equation of step 1.1, foundation, generates new RANDOM SOLUTION；

Step 1.2, by newly-generated RANDOM SOLUTION substitute into energy equation, if energy value declines new explanation be accepted as current state Under optimal solution, if energy rise if according to Boltzmann-Gibbs distribution acceptance probability and Metropolis criterions judge is It is no to receive new explanation as the optimal solution under current state；

If step 1.3, new explanation are rejected, return to step 1.1；

If step 1.4, new explanation are received, Current Temperatures are updated according to annealing scheme formula one, annealing scheme formula one is：

In formula, T and T₀It is Current Temperatures and initial temperature respectively；α is temperature decline coefficient；J is iteration time Number；

Process two is to use relatively low initial temperature, and Disturbance Model makees local slow optimizing, includes the following steps：

Step 2.1, foundation Random Local perturbation equation, generate new RANDOM SOLUTION；

Step 2.2, by newly-generated RANDOM SOLUTION substitute into energy equation, if energy value declines new explanation be accepted as current state Under optimal solution, according to Boltzmann-Gibbs distribution acceptance probabilities and Metropolis criterions if energy rises, below letter Claim M criterions, determine whether to receive new explanation as the optimal solution under current state；

If step 2.3, new explanation are rejected, return to step 2.1；

If step 2.4, new explanation are received, Current Temperatures are updated according to annealing scheme formula two, annealing scheme formula two is：

In formula, k₀For the iterations of process one；β is the temperature rise factor.

3. a kind of energy saving group control method of salt water cooling system of more handpiece Water Chilling Units cooperations as claimed in claim 1, it is special Sign is, further includes：

Step 6: the rotating speed of each handpiece Water Chilling Units is calculated, wherein, the rotating speed of i-th handpiece Water Chilling UnitsIn formula, Q_{I, 0} For the specified chilled-water flow of i-th handpiece Water Chilling Units, n_{I, 0}For the rated speed of i-th handpiece Water Chilling Units.