Method and system for determining energy consumption coefficient evaluation index of refrigeration station system

Abstract

The invention provides a refrigeration station system energy consumption coefficient evaluation index determination method and system, and relates to the technical field of system energy efficiency evaluation. The method comprises the following steps: firstly, acquiring inner and outer side temperatures, chilled water flow and power consumption data of a refrigeration station at equal time intervals, calculating refrigerating capacity and load rates based on a thermodynamic principle, and dividing the load rates into a low load rate interval, a medium-low load rate interval, a medium-high load rate interval and a high load rate interval; according to the method, steady-state sampling points are screened through the power, the refrigerating capacity and the temperature difference change rate of the inner side and the outer side, based on the performance coefficients and the load rates of the steady-state sampling points, a performance coefficient characteristic curve of the performance coefficient COP of each interval is fitted through a quadratic polynomial, unsteady-state data are removed by comparing the deviation degree of the real-time COP and the fitted curve, and the performance coefficient characteristic curve of the real-time COP is obtained. And calculating interval average energy efficiency by combining an exponential weighted average method of attenuation factors, and constructing an energy consumption coefficient evaluation index model by taking a time proportion as a weight to realize dynamic evaluation of the long-term operation energy efficiency of the refrigeration station.

Description

Technical Field

[0001] This invention relates to the field of system energy efficiency assessment technology, specifically to a method and system for determining the evaluation index of energy consumption coefficient of a refrigeration station system. Background Technology

[0002] Food freezing stations need to frequently handle dynamic operating conditions such as raw material entry and exit, equipment start-up and shutdown. Freezing stations usually integrate a variety of high-energy-consuming equipment, such as chillers, water pumps, and fans. In actual operation, freezing stations often experience sudden equipment failures and temperature fluctuations caused by batches of food entering and leaving, resulting in drastic changes in the instantaneous load of the system. This manifests as frequent compressor start-up and shutdown and unstable operating conditions, which in turn causes drastic fluctuations in the coefficient of performance (COP).

[0003] Many current energy efficiency assessment methods directly utilize COP data from all time points for statistical analysis, failing to distinguish between steady-state and disturbance states. This approach is particularly unsuitable for the food industry, where frequent inbound and outbound operations during peak hours or temporary equipment maintenance cause refrigeration units to repeatedly enter partial load operating ranges within short periods, leading to a rapid decline in COP. For instance, in meat processing workshops, refrigeration units experience multiple loading and unloading operations daily, resulting in cold loss due to door openings and load rate fluctuations of up to 30% in a short time. In dairy storage warehouses, temporary malfunctions such as evaporator frosting or fan failures can cause sudden changes in input power exceeding 15%. In these scenarios, the coefficient of performance (COP) is significantly affected by non-steady-state factors. Traditional energy consumption assessment methods, if directly using full-condition data, will lead to errors due to the following shortcomings.

[0004] Therefore, there is an urgent need for an energy efficiency assessment method that is tailored to the operational characteristics of cold storage plants in the food industry. This method should be able to identify and eliminate data during short-term disturbances, and evaluate the system based solely on load rates and performance coefficients under stable operating conditions, thereby obtaining more accurate and representative energy consumption coefficient evaluation results.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for determining the energy consumption coefficient evaluation index of a refrigeration plant system, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for determining the energy consumption coefficient evaluation index of a refrigeration plant system, comprising the following steps:

[0009] Step 1: The chiller station collects the temperature inside and outside the chiller station, chilled water flow rate, power consumption and input power at equal time intervals. Based on the thermodynamic principle, the cooling capacity of the chiller station is calculated according to the temperature difference between the inside and outside and the chilled water flow rate. The load rate of each collection point is calculated according to the cooling capacity and the rated cooling capacity, and different load rate intervals are divided.

[0010] Step 2: Calculate the performance coefficient of each sampling point using cooling capacity and power consumption. Calculate the rate of change based on the input power, cooling capacity, and temperature difference between the inside and outside of the sampling point. Divide the sampling points into steady-state operation points according to the input power, cooling capacity, and rate of change of the temperature difference between the inside and outside of each sampling point. Extract the performance coefficient and load rate of the sampling points in steady-state operation. Use a quadratic polynomial to fit the performance coefficient characteristic curve of each load rate interval.

[0011] Step 3: Compare the performance coefficient of the current sampling point with the fitted performance coefficient characteristic curve. When the deviation exceeds the preset deviation threshold, it is determined to be an unsteady operation sampling point. After removing the unsteady sampling points, the number and time proportion of sampling points in each load rate interval are counted.

[0012] Step 4: Set the attenuation factor, calculate the average energy efficiency of each load rate interval using the exponential weighted average method based on the attenuation factor, and construct and analyze the energy consumption coefficient evaluation index model by real-time weighting based on the time proportion and average energy efficiency of each load rate interval.

[0013] Furthermore, based on thermodynamic principles, the method for calculating the cooling capacity of a chiller plant using the temperature difference between the inner and outer sides and the chilled water flow rate is as follows:

[0014] The temperature data collected each time inside the refrigeration station are recorded sequentially according to the collection time as: T in，1 T in,2 ... T in,n Where n represents the total number of data collections;

[0015] The temperature data collected each time outside the refrigeration station are recorded sequentially in chronological order as: T out，1 T out，2 ... T out,n

[0016] The traffic data collected each time is recorded sequentially in chronological order as: F1, F2, ..., F n ;

[0017] The power consumption for each data collection is recorded sequentially in chronological order as: E1, E2, ..., E n ;

[0018] Extract chilled water flow rate data, obtain chilled water density, and calculate chilled water mass flow rate:

[0019] mi′ =ρ·F i′

[0020] In the formula, m i′ Let F represent the mass flow rate of chilled water collected in the i′th sampling, ρ represent the density of chilled water, and F represent the mass flow rate of chilled water collected in the i′th sampling. i′ This represents the traffic data collected in the i′th time, where i′ represents the index of the collection number, i′=1,2,...,n;

[0021] Calculate the temperature difference between the inside and outside:

[0022] ΔT i′ =T out，i′ -T in，i′

[0023] In the formula, ΔT i′ T represents the temperature difference between the inner and outer sides during the i′-th sampling. in，i′ T represents the temperature inside the refrigeration station during the i′th data collection. out，i′ This represents the temperature outside the freezing station collected in the i′th sampling.

[0024] A formula for calculating cooling capacity is constructed using chilled water mass flow rate, chilled water specific heat capacity, and the temperature difference between the inside and outside:

[0025] Q i′ =c·m i′ ·ΔT i′

[0026] In the formula, Q i′ Let represent the cooling capacity collected in the i′th sampling, and c represent the specific heat capacity of the chilled water.

[0027] Furthermore, the method for calculating the load rate of each sampling point based on the cooling capacity and rated cooling capacity, and dividing different load rate ranges, is as follows:

[0028] By comparing the actual cooling capacity with the rated cooling capacity, the load factor formula is constructed:

[0029]

[0030] In the formula, L i′ Q represents the load factor of the i′th data collection. rated Indicates the rated cooling capacity of the refrigeration station system;

[0031] The load factor range of 0%-29% is divided into low load factor range A, 30%-59% load factor range B, 60%-89% load factor range C, and 90%-100% load factor range D.

[0032] Furthermore, the method for dividing the sampling points for steady-state operation based on the input power, cooling capacity, and rate of change of the temperature difference between the inner and outer sides at each sampling point is as follows:

[0033] Extract the input power P1, P2, ..., P from the refrigeration station at equal time intervals. n Calculate the power change rate at the i′-th sampling point:

[0034]

[0035] In the formula, ΔP i′ This represents the rate of change of input power at the i′-th sampling point;

[0036] Calculate the rate of change of cooling capacity at the i′th sampling point:

[0037]

[0038] In the formula, ΔQ i′ This represents the rate of change of power at the i′-th sampling point;

[0039] Calculate the rate of change of the temperature difference between the inside and outside of the i′-th sampling point:

[0040]

[0041] In the formula, ΔT mi′ This represents the rate of change of the temperature difference between the inner and outer sides of the i′-th sampling point;

[0042] Calculate the performance coefficient of the i′-th sampling point:

[0043]

[0044] In the formula, COP i′ This represents the performance coefficient of the i′-th sampling point;

[0045] For satisfying ΔP i′ ≤0.5, ΔQ i′ ≤0.5,ΔT mi′ Sampling points ≤0.5 are considered to be in a steady state. The performance coefficient and load factor of sampling points in a steady state are recorded as COP. l and L l , where l represents the index of historical steady-state operating data, l = 1, 2, ..., n0, n0 represents the number of sampling points selected in steady state, and n0 is a positive integer higher than 1000.

[0046] Furthermore, the performance coefficient and load factor of the sampling points during steady-state operation are extracted, and the performance coefficient characteristic curves for each load factor interval are fitted using a quadratic polynomial:

[0047] Extract and record the performance coefficient (COP) of sample points where the load rate is in the low load rate range (A). l1 and load factor L l1 Coefficient of performance (COP) in the low-to-medium load range B l2 and load factor L l2 The coefficient of performance (COP) for the medium-to-high load factor range C and the load factor range D l3 and load factor L l3 ;

[0048] Extracting the performance coefficient (COP) of region A under low load conditions l1 and load factor L l1 The performance coefficient characteristic curve is fitted using a quadratic polynomial:

[0049] COP A =a1·L 2 +b1·L+c

[0050] In the formula, COP A The performance coefficient characteristic curve represents the low load rate range A, where L represents the load rate, and a1 and b1 represent the coefficients.

[0051] Extracting the performance coefficient (COP) for the low-to-medium load rate range B. l2 and load factor L l2 The performance coefficient characteristic curve is fitted using a quadratic polynomial:

[0052] COP B =a2·L 2 +b2·L+c

[0053] In the formula, COP B The performance coefficient characteristic curve represents the low-to-medium load rate range B, where a2 and b2 represent coefficients.

[0054] Extract the coefficient of performance (COP) for the medium-to-high load factor range C and the load factor range D. l3 and load factor L l3 The performance coefficient characteristic curve is fitted using a quadratic polynomial:

[0055] COP C，D =a3·L 2 +b2·L+c

[0056] In the formula, COP C，D The performance coefficient characteristic curves represent the performance coefficients in the medium-to-high load rate range C and the load rate range D, where a3 and b3 represent coefficients, and the coefficients are solved using the least squares method.

[0057] Furthermore, the method for determining the number and time percentage of sampling points within each load rate interval after eliminating non-steady-state sampling points is as follows:

[0058] Set the deviation threshold \(k\), where \(0 < k\leq0.1\). Extract the load rate of the sampling point at the current moment. After determining the load rate interval it belongs to, calculate the theoretical coefficient of performance COP through the characteristic curve of the coefficient of performance corresponding to its interval. ll Extract the coefficient of performance COP of the current sampling point. n Calculate the deviation: In the formula, \(r\) represents the deviation of the coefficient of performance of the sampling point at the current moment. When \(r\geq k\), determine that this sampling point is an unsteady - state operation sampling point. After removing the unsteady - state sampling points, count the number of sampling points in each load rate interval and the total number of sampling points. For each load rate interval, calculate the proportion of the time of the sampling points in steady - state operation. The proportion of the time of the low - load - rate interval A is:

[0059]

[0060] In the formula, \(\omega\) A represents the proportion of the time of the low - load - rate interval A, \(N\) A represents the number of sampling points in the low - load - rate interval A, and \(N\) represents the total number of sampling points;

[0061] The proportion of the time of the medium - low - load - rate interval B is:

[0062]

[0063] In the formula, \(\omega\) B represents the proportion of the time of the medium - low - load - rate interval B, \(N\) B represents the number of sampling points in the medium - low - load - rate interval B;

[0064] The proportion of the time of the medium - high - load - rate interval C is:

[0065]

[0066] In the formula, \(\omega\) C represents the proportion of the time of the medium - high - load - rate interval C, \(N\) C represents the number of sampling points in the medium - high - load - rate interval C;

[0067] The proportion of the time of the high - load - rate interval D is:

[0068]

[0069] In the formula, \(\omega\) D represents the proportion of the time of the high - load - rate interval D, \(N\) D represents the number of sampling points in the high - load - rate interval D;

[0070] Calculate the coefficient of performance of each sampling point in each load rate interval under steady - state operation through the refrigerating capacity and power consumption:

[0071]

[0072] In the formula, η j Q represents the performance coefficient of the j-th sampling point in each load rate interval. j E represents the cooling capacity at the j-th sampling point in each load rate interval. j This represents the power consumption at the j-th sampling point in each load rate interval, where j represents the index of the sampling point within each load rate interval.

[0073] Furthermore, by setting a decay factor, the method for calculating the average energy efficiency for each load factor range using the exponential weighted average method based on the decay factor is as follows:

[0074] Calculate average energy efficiency:

[0075]

[0076] In the formula, Indicates average energy efficiency;

[0077] Constructing the decay factor:

[0078]

[0079] In the formula, α represents the attenuation factor;

[0080] Calculate the average energy efficiency for each load factor range:

[0081]

[0082] In the formula, η A η represents the average energy efficiency in the low load factor range A. B η represents the average energy efficiency in the low-to-medium load range B. C η represents the average energy efficiency of C in the medium-to-high load factor range. D η represents the average energy efficiency in the high load factor range D. j N represents the performance coefficient of the j-th sampling point in each load rate interval. A N represents the number of sampling points in the low load rate interval A. B N represents the number of sampling points in the low-to-medium load rate range B. C N represents the number of sampling points in the medium-to-high load rate range C. D This indicates the number of sampling points in the high load rate interval D.

[0083] Furthermore, the method for constructing a real-time weighted formula and calculating and evaluating the energy consumption coefficient evaluation index is as follows:

[0084] By using the time proportion of each load rate interval and average energy efficiency under steady-state operation, a weighted formula for calculating the real-time energy consumption coefficient evaluation index is constructed:

[0085] I=ω A ·ηA +ω B ·η B +ω C ·η C +ω D ·η D

[0086] In the formula, I represents the energy consumption coefficient evaluation index. Energy consumption thresholds I1 and I2 are set, and I1 > I2 > 0. When I ≥ I1, the energy consumption evaluation of the refrigeration station is excellent; when I2 ≤ I < I1, the energy consumption evaluation of the refrigeration station is good; and when I < I2, the energy consumption evaluation of the refrigeration station is poor.

[0087] The present invention also provides a system for determining the energy consumption coefficient evaluation index of a chiller plant system, the system being used to execute the above-described method for determining the energy consumption coefficient evaluation index of a chiller plant system, comprising:

[0088] The load rate interval division module is used to collect the temperature inside and outside the chiller, chilled water flow, power consumption and input power at equal time intervals. Based on thermodynamic principles, it calculates the cooling capacity of the chiller according to the temperature difference between the inside and outside and the chilled water flow. Based on the cooling capacity and the rated cooling capacity, it calculates the load rate of each collection point and divides different load rate intervals.

[0089] The performance coefficient characteristic curve fitting module is used to calculate the performance coefficient of each sampling point using cooling capacity and power consumption, calculate the rate of change based on the input power, cooling capacity and the temperature difference between the inside and outside of the sampling point, divide the sampling points for steady-state operation according to the input power, cooling capacity and the rate of change of the temperature difference between the inside and outside of the sampling point, extract the performance coefficient and load rate of the sampling points for steady-state operation, and use a quadratic polynomial to fit the performance coefficient characteristic curve of each load rate interval.

[0090] The non-steady-state operation determination module is used to compare the performance coefficient of the sampling point at the current time with the fitted performance coefficient characteristic curve. When the deviation exceeds the preset deviation threshold, it is determined to be a non-steady-state operation sampling point. After removing the non-steady-state sampling points, the number and time proportion of sampling points in each load rate interval are counted.

[0091] The energy consumption index construction module is used to set the attenuation factor, calculate the average energy efficiency of each load rate interval based on the exponential weighted average method of the attenuation factor, and construct and analyze the energy consumption coefficient evaluation index model by real-time weighting based on the time proportion and average energy efficiency of each load rate interval.

[0092] Compared with the prior art, the beneficial effects of the present invention are:

[0093] This invention identifies and eliminates non-steady-state operation sampling points by combining input power, cooling capacity, and the rate of change of temperature difference between the inner and outer sides. It performs quadratic polynomial fitting of the performance coefficient characteristic curves for each load rate interval based only on the performance coefficient and load rate of steady-state operation. This allows the extraction of the time proportion and average energy efficiency of each load rate interval under the actual operating level of the refrigeration plant, and the construction of an energy consumption coefficient evaluation index model. This model highlights the average energy efficiency assignment under high time proportions, avoids interference from performance coefficient fluctuations caused by disturbances on the energy consumption evaluation results, and makes the energy consumption coefficient evaluation results more accurate. Attached Figure Description

[0094] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0095] Figure 2 This is a graph showing the change in load rate as a function of cooling capacity according to the present invention.

[0096] Figure 3 This is a graph showing the change in load rate as a function of power consumption according to the present invention.

[0097] Figure 4 This is a graph showing the change in load rate of the present invention with the temperature difference between the inner and outer sides;

[0098] Figure 5 This is a COP fitting graph for the low load rate of this invention;

[0099] Figure 6 This is a graph showing the changes in the time percentage of low load rate intervals, average energy efficiency, and energy consumption coefficient evaluation indicators in this invention.

[0100] Figure 7 This is a graph showing the changes in the time percentage of high load rate intervals, average energy efficiency, and energy consumption coefficient evaluation indicators in this invention.

[0101] Figure 8 This is a schematic diagram of the overall system modules of the present invention. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0103] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0104] Example:

[0105] Please see Figures 1 to 7 The present invention provides a technical solution:

[0106] A method for determining the energy consumption coefficient evaluation index of a refrigeration plant system, comprising the following steps:

[0107] Step 1: The chiller station collects the temperature inside and outside the chiller station, chilled water flow rate, power consumption and input power at equal time intervals. Based on the thermodynamic principle, the cooling capacity of the chiller station is calculated according to the temperature difference between the inside and outside and the chilled water flow rate. The load rate of each collection point is calculated according to the cooling capacity and the rated cooling capacity, and different load rate intervals are divided.

[0108] Different time interval settings can make the collected data more representative and effective. The input power of the refrigeration plant refers to the total power of all electrical equipment in the refrigeration plant during operation. It fluctuates frequently due to factors such as ambient temperature and building cooling demand, and can directly reflect the stability of the refrigeration plant system. For stable refrigeration plant systems, a longer time interval can reduce the amount of data collected while still reflecting the overall operating trend of the system. For unstable systems, a shorter time interval can obtain more key dynamic information and avoid missing important details of changes due to excessively long time intervals. This helps to analyze the system performance and energy consumption more accurately in the future. For example, a fixed time interval of 1 hour can cover its main operating conditions, which ensures that there is enough data for detailed analysis without losing key information due to excessively sparse data.

[0109] The internal temperature reflects the real-time temperature inside the chiller station, such as the evaporator and chilled water pipes, and reflects the core cooling capacity of the refrigeration system. The external temperature is the temperature of the external environment or cooling water side of the chiller station, such as the outdoor air temperature or the cooling tower inlet water temperature, reflecting the external load on the system operation. The temperature difference between the internal and external temperatures directly determines the cooling efficiency. The chilled water flow rate is the volume of circulating water flowing through the chiller station per unit time, reflecting the circulation speed of the refrigerant water in the system. A sudden drop in flow rate may indicate pipe blockage or valve failure. The power consumption is the cumulative power consumption of the chiller station as a whole or of individual equipment, such as compressors, water pumps, and fans. Monitoring power consumption can directly calculate whether the equipment is consuming power abnormally.

[0110] The temperature data collected each time inside the refrigeration station are recorded sequentially according to the collection time as: T in,1 T in，2 ... T in,n Where n represents the total number of data collections;

[0111] The temperature data collected each time outside the refrigeration station are recorded sequentially in chronological order as: T out，1 T out，2 ... T out,n

[0112] The traffic data collected each time is recorded sequentially in chronological order as: F1, F2, ..., F n ;

[0113] The power consumption for each data collection is recorded sequentially in chronological order as: E1, E2, ..., E n ;

[0114] Extract chilled water flow rate data, obtain chilled water density, and calculate chilled water mass flow rate:

[0115] m i′ =ρ·F i′

[0116] In the formula, m i′ Let F represent the mass flow rate of chilled water collected in the i′th sampling, ρ represent the density of chilled water, and F represent the mass flow rate of chilled water collected in the i′th sampling. i′ This represents the traffic data collected in the i′th time, where i′ represents the index of the collection number, i′=1,2,…,n;

[0117] Calculate the temperature difference between the inside and outside:

[0118] ΔT i′ =T out,i′ -T in,i′

[0119] In the formula, ΔT i′ T represents the temperature difference between the inner and outer sides during the i′-th sampling. in，i′T represents the temperature inside the refrigeration station during the i′th data collection. out，i′ This represents the temperature outside the freezing station collected in the i′th sampling.

[0120] Cooling capacity is a key indicator for measuring the degree of achievement of this core function. It directly reflects how much heat the chiller can absorb and transfer from the object being cooled within a certain period of time, thus accurately calculating the system's coefficient of performance. This serves as one of the important data points for understanding the system's energy consumption level under different operating conditions. The cooling capacity calculation formula is constructed using chilled water mass flow rate, chilled water specific heat capacity, and the temperature difference between the inside and outside.

[0121] Q i′ =c·m i′ ·ΔT i′

[0122] In the formula, Q i′ Let represent the cooling capacity collected in the i′th sampling, and c represent the specific heat capacity of the chilled water.

[0123] Load factor is an indicator that measures the ratio between the actual operating load of a refrigeration plant and its rated load. Rated cooling capacity only represents the cooling capacity of the system under ideal, full-load conditions. However, in real-world scenarios, such as factory operations, due to the diversity of production processes, the variability of environmental conditions, and the differences in production demands at different times, refrigeration plants rarely operate at full load continuously and stably.

[0124] By calculating the load factor, the ratio of the actual load borne by the equipment to its rated cooling capacity at various times can be accurately determined, thus truly reflecting the operating status of the equipment under actual working conditions, rather than relying solely on the theoretical indicator of rated cooling capacity. The load factor formula is constructed by comparing the actual cooling capacity with the rated cooling capacity.

[0125]

[0126] In the formula, L i′ Q represents the load factor of the i′th data collection. rated This represents the rated cooling capacity of the refrigeration plant system, quantifying the relationship between the actual load and the maximum design load at a given moment as a percentage. Rated cooling capacity is the maximum cooling capacity that the refrigeration plant can stably output under standard operating conditions, expressed in L. i′ By comparing the actual cooling capacity with the rated cooling capacity, it can be determined whether the refrigeration plant system is operating in its high-efficiency range or under low load. Therefore, monitoring the load rate L at various time intervals is crucial. nIt can quantify the deviation between actual operating conditions and design capacity. Rated cooling capacity is an inherent parameter of the refrigeration plant, representing its maximum designed cooling capacity. It is the benchmark value for calculating the load rate. The collected actual cooling capacity reflects the cooling capacity output of the system during actual operation. The larger the value, the larger the load rate. The two are positively correlated.

[0127] The relationship between the coefficient of performance (COP) and load rate for energy-consuming equipment, such as air conditioning units, motors, and water pumps, is not globally linear but exhibits piecewise nonlinear characteristics. After partitioning, the performance curve within each interval can be considered a local approximation of quadratic nonlinearity, which better reflects actual operating patterns. For refrigeration plants, when the unit is operating at partial load, frequent compressor starts and stops result in lower efficiency, and the COP increases rapidly with the load rate. As the load rate increases, the heat exchange area of ​​the evaporator and condenser is gradually fully utilized, and the COP continues to rise in the low-to-medium load rate range, but the rate of increase slows significantly until it approaches zero, entering the next phase. When the COP reaches its peak and remains stable within a load rate range, the COP will gradually decrease due to the overload effect, and the degree of decrease will gradually increase. The parameters of each range are independently fitted, and can be calibrated based on the measured data of typical operating conditions to avoid mutual interference between parameters of high and low load ranges when fitting the overall load. Therefore, the load rate of 0%-29% is divided into low load rate range A, the load rate of 30%-59% is divided into medium-low load rate range B, the load rate of 60%-89% is divided into medium-high load rate range C, and the load rate of 90%-100% is divided into high load rate range D. The specific adjustments can also be made according to the actual situation of the unit.

[0128] Step 2: Calculate the performance coefficient of each sampling point using cooling capacity and power consumption. Calculate the rate of change based on the input power, cooling capacity, and temperature difference between the inside and outside of the sampling point. Divide the sampling points into steady-state operation points according to the input power, cooling capacity, and rate of change of the temperature difference between the inside and outside of each sampling point. Extract the performance coefficient and load rate of the sampling points in steady-state operation. Use a quadratic polynomial to fit the performance coefficient characteristic curve of each load rate interval.

[0129] Extract the input power P1, P2, ..., P from the refrigeration station at equal time intervals. n Calculate the power change rate at the i′-th sampling point:

[0130]

[0131] In the formula, ΔP i′ This represents the rate of change of input power at the i′-th sampling point;

[0132] Calculate the rate of change of cooling capacity at the i′th sampling point:

[0133]

[0134] In the formula, ΔQ i′This represents the rate of change of power at the i′-th sampling point;

[0135] Calculate the rate of change of the temperature difference between the inside and outside of the i′-th sampling point:

[0136]

[0137] In the formula, ΔT mi′ This represents the rate of change of the temperature difference between the inner and outer sides of the i′-th sampling point;

[0138] Calculate the performance coefficient of the i′-th sampling point:

[0139]

[0140] In the formula, COP i′ The COP of the i′ sampling point directly reflects the efficiency of converting unit electrical energy into cooling capacity. It can evaluate the energy efficiency of the system under different loads and operating conditions in real time and is a core indicator for measuring the economic efficiency of the refrigeration plant. If the COP of a certain sampling point is significantly lower than the theoretical value or historical data of the same load rate range, it may indicate equipment abnormality and thus serve as one of the indicators for evaluating the energy consumption coefficient of the refrigeration plant.

[0141] For satisfying ΔP i′ ≤0.5, ΔQ i′ ≤0.5,ΔT mi′ ≤0.5 sampling points are considered to be in a steady state. When the input power and cooling capacity change greatly, the equipment may be in the start-up and shutdown phase or a sudden load change. At this time, the energy efficiency data, such as COP, fluctuates greatly and cannot represent the normal operating performance. Abnormal temperature difference may indicate a decrease in heat exchanger efficiency or water circuit blockage. Selecting data obtained in this process can avoid the deviation of energy efficiency assessment caused by transient fluctuations.

[0142] The performance coefficient and load factor at the sampling points in steady state are recorded as COP. l and L l Furthermore, it is necessary to select a large amount of steady-state data from the initial stage of equipment commissioning. After long-term operation, chiller equipment such as chillers, water pumps, and heat exchangers will inevitably experience problems such as component wear, reduced heat exchange efficiency, and decreased motor efficiency. Selecting a large amount of steady-state data from the initial stage of equipment commissioning can obtain the ideal benchmark value of the equipment. In subsequent evaluations, it is possible to analyze whether the performance has degraded. Here, l represents the index of historical steady-state operating data, l = 1, 2, ..., n0, n0 represents the number of sampling points selected in steady state, and n0 is a positive integer higher than 1000.

[0143] Extract and record the performance coefficient (COP) of sample points where the load rate is in the low load rate range (A). l1 and load factor L l1Coefficient of performance (COP) in the low-to-medium load range B l2 and load factor L l2 The coefficient of performance (COP) for the medium-to-high load factor range C and the load factor range D l3 and load factor L l3 ;

[0144] Extracting the performance coefficient (COP) of region A under low load conditions l1 and load factor L l1 The performance coefficient characteristic curve is fitted using a quadratic polynomial:

[0145] COP A =a1·L 2 +b1·L+c

[0146] In the formula, COP A The performance coefficient characteristic curve represents the low load rate range A, where L represents the load rate, and a1 and b1 represent the coefficients.

[0147] Extracting the performance coefficient (COP) for the low-to-medium load rate range B. l2 and load factor L l2 The performance coefficient characteristic curve is fitted using a quadratic polynomial:

[0148] COP B =a2·L 2 +b2·L+c

[0149] In the formula, COP B The performance coefficient characteristic curve represents the low-to-medium load rate range B, where a2 and b2 represent coefficients.

[0150] Extract the coefficient of performance (COP) for the medium-to-high load factor range C and the load factor range D. l3 and load factor L l3 Quadratic polynomial fitting requires sufficient data points. Considering that most refrigeration stations do not operate at near full capacity, fitting only the C or D interval may result in insufficient sample size due to overly fine interval division, leading to fitting bias. Therefore, quadratic polynomial fitting is used to fit the performance coefficient characteristic curve.

[0151] COP C，D =a3·L 2 +b3·L+c

[0152] In the formula, COP C，D The performance coefficient characteristic curves represent the performance coefficients in the medium-to-high load rate range C and the load rate range D, where a3 and b3 represent coefficients, and the coefficients are solved using the least squares method.

[0153] When collecting data from the refrigeration station, sampling is carried out at fixed time intervals, which means that the time length represented by each sampling point is equal. For example, if a certain device is in the low load rate range at 20 out of 100 sampling points, it can be directly known that it is in the low load state for 20% of the time. Table 1 shows the temperature inside and outside the refrigeration station, the chilled water flow rate data collected in 44 groups, the actual cooling capacity and rated cooling capacity calculated through the data, and the corresponding load rate data for each group. After summarization, a statistical table is formed for subsequent identification of energy consumption differences in each load rate range and evaluation of the energy consumption coefficient;

[0154]

[0155] Step 3: Compare the performance coefficient of the sampling point at the current moment with the fitted performance coefficient characteristic curve. When the deviation degree exceeds the preset deviation threshold, it is determined as an unsteady operation sampling point. After removing the unsteady sampling points, count the number and time proportion of sampling points in each load rate range;

[0156] During operation, due to factors such as small changes in ambient temperature and load, the COP will have natural fluctuations. Most refrigeration station control systems set the allowable deviation threshold between the measured COP value and the theoretical value to 10% or even lower. Therefore, set the deviation threshold k, and 0 < k ≤ 0.1, which can be adjusted according to the actual situation of the accuracy requirements. Extract the load rate of the sampling point at the current moment. After determining the load rate range it belongs to, calculate the theoretical performance coefficient COP through the performance coefficient characteristic curve it belongs to ll , extract the performance coefficient COP of the current sampling point n , calculate the deviation degree: In the formula, r represents the performance coefficient deviation degree of the sampling point at the current moment. When r ≥ k, it is determined that the sampling point is an unsteady operation sampling point. The refrigeration station is closer to the theoretical performance coefficient characteristic curve of the design standard in the initial stage of operation, which is more suitable as a reference value for evaluating the current operating state. At this time, when the difference between the performance coefficient of the current sampling point and the theoretical value exceeds the normal range, there may be problems such as temporary equipment failures, control strategy failures or sudden changes in working conditions;

[0157] After removing the unsteady sampling points, count the number of sampling points and the total number of sampling points in each load rate range. Classify the load rates calculated for each time interval according to the preset load rate ranges. Finally, for each load rate range, count the corresponding number of sampling points. For example, for a sampling point with a load rate of 20%, classify this sampling point into the low load rate range A; if the load rate is 45%, classify it into the medium - low load rate range B, and so on, to complete the classification operation for all time intervals;

[0158] For each load rate range, calculate the time proportion of steady - state operation sampling points. The time proportion of the low load rate range A is:

[0159]

[0160] In the formula, ω A N represents the percentage of time in the low load period A. A N represents the number of sampling points in the low load rate interval A, and N represents the total number of sampling points.

[0161] The time percentage of the low-to-medium load range B is as follows:

[0162]

[0163] In the formula, ω B N represents the time percentage of the low to medium load range B. B This indicates the number of sampling points in the low-to-medium load rate range B.

[0164] The time percentage of the medium-to-high load factor range C is as follows:

[0165]

[0166] In the formula, ω C N represents the time percentage of the medium-to-high load range C. C This indicates the number of sampling points in the medium-to-high load rate range C;

[0167] The time percentage of the high load period D is:

[0168]

[0169] In the formula, ω D N represents the time percentage of the high load period D. D This indicates the number of sampling points in the high load rate interval D;

[0170] Calculate the performance coefficients at each sampling point within each load rate range under steady-state operation using cooling capacity and power consumption:

[0171]

[0172] In the formula, η j Q represents the performance coefficient of the j-th sampling point in each load rate interval. j E represents the cooling capacity at the j-th sampling point in each load rate interval. j This represents the power consumption at the j-th sampling point in each load rate interval, where j represents the index of the sampling point within each load rate interval. For example, for the low load rate interval A, j = 1, 2, ..., N. AThe coefficient of performance (COP) is used to number each sampling point within the low load rate range A. Calculated as the ratio of cooling capacity to power consumption, it is a quantitative indicator that directly reflects the efficiency of the refrigeration plant system in converting electrical energy into cooling capacity at each sampling point. A higher COP indicates more cooling capacity converted per unit of power consumption, and higher system operating efficiency at that point. A larger COP value also indicates a better evaluation of the refrigeration plant's energy efficiency. When power consumption remains constant, a larger cooling capacity results in a larger COP, indicating a higher cooling capacity output. Conversely, a smaller cooling capacity results in a smaller COP, and under constant power consumption, a smaller cooling capacity output. Therefore, cooling capacity is positively correlated with the COP and negatively correlated with power consumption.

[0173] Table 2 shows the quantitative relationship between the internal and external temperature difference, refrigeration capacity, and load rate during the operation of the chiller plant. It demonstrates that the greater the internal and external temperature difference, the higher the refrigeration capacity and the higher the load rate. For example, when the internal and external temperature difference increases from 12.7℃ to 24.1℃, the load rate increases from 13.3% to 77.53%. Load rate fluctuations reflect system load stability. When the load rate changes significantly, the standard deviation σ of the relative rate of change increases. r Increasing the sampling interval triggers a shorter sampling interval, and the one-to-one correspondence between cooling capacity and load rate provides a data source for subsequent coefficient of performance calculations.

[0174]

[0175] like Figures 2-5 As shown, the cooling capacity, power consumption, and temperature difference between the inner and outer sides are approximately linearly related to the load rate. The load rate increases with the increase of cooling capacity, power consumption, and temperature difference between the inner and outer sides, showing a positive correlation. This is consistent with the logic under normal operation of the refrigeration plant and provides intuitive data support for energy consumption assessment. At this time, the time proportion of low load rate interval A is 34.09%, the time proportion of medium-low load rate interval B is 50%, and the time proportion of medium-high load rate interval C is 15.91%. The quadratic polynomial fitting accuracy of load rate and COP is high, reflecting the nonlinear rising characteristic of COP in the low load area with the acceleration of the load rate. Therefore, it can be used as a benchmark curve to calculate the deviation of the sampling points.

[0176] Step 4: Set the attenuation factor, calculate the average energy efficiency of each load rate interval based on the exponential weighted average method of the attenuation factor, and construct and analyze the energy consumption coefficient evaluation index model by real-time weighting based on the time proportion and average energy efficiency of each load rate interval.

[0177] Calculate average energy efficiency:

[0178]

[0179] In the formula, Indicates average energy efficiency;

[0180] Constructing the decay factor:

[0181]

[0182] In the formula, α represents the attenuation factor, where, when η n Compared with average energy efficiency The closer they get, The smaller the value, the better. The value of η will increase, and conversely, when η... n Compared with average energy efficiency The further away from the average energy efficiency, the smaller the attenuation factor becomes. This construction method allows energy efficiency data under stable operating conditions that are closer to the average energy efficiency to have greater weight in the calculation, avoiding excessive interference from individual abnormal data that deviate from the average energy efficiency, such as extreme sampling points with sudden high energy consumption or high cooling capacity, on the overall evaluation. This makes the evaluation results more consistent with the actual operating characteristics of the system. In actual operation, the cooling efficiency under these commonly used load rates is directly related to the system's energy consumption and operating costs.

[0183] Calculate the average energy efficiency for each load factor range:

[0184]

[0185] In the formula, η A η represents the average energy efficiency in the low load factor range A. B η represents the average energy efficiency in the low-to-medium load range B. C η represents the average energy efficiency of C in the medium-to-high load factor range. D This represents the average energy efficiency of the high load rate range D. Because this method can give greater weight to stable data close to the average energy efficiency when calculating the average energy efficiency of each load rate range, stable and efficient cooling efficiency data can be more fully reflected for those frequently used load rate ranges.

[0186] A real-time weighted formula is constructed using the time percentage of each load factor interval and the average energy efficiency:

[0187] I=ω A ·η A +ω B ·η B +ω C ·η C +ω D ·η D

[0188] In the formula, I represents the energy consumption coefficient evaluation index, which is the weighted average of the average energy efficiency of each load rate range according to the time proportion. The larger the energy consumption coefficient evaluation index, the higher the cooling efficiency of the system in these commonly used load rate ranges during the overall operation. The energy consumption coefficient evaluation index calculated by this weighted formula can give it a higher score, indicating that more cooling capacity can be achieved under the same energy input, that is, more cooling capacity can be provided for each unit of electricity consumed. This shows that the refrigeration plant can significantly reduce energy consumption in the long-term operation. The time proportion can objectively reflect the operating time distribution of the refrigeration plant system in different load rate ranges, which is close to the actual operating state of the system. It further highlights the importance of the load rate range with long-term operation in energy consumption evaluation and is positively correlated with the energy consumption coefficient evaluation index. The performance coefficient reflects the energy utilization efficiency of the refrigeration plant. Under the same cooling capacity, the larger the performance coefficient, the less electricity is consumed. It is positively correlated with the energy consumption coefficient evaluation index. That is, the higher the energy efficiency and the larger the operating time proportion of the high energy efficiency range, the larger the value of the energy consumption coefficient evaluation index, and vice versa.

[0189] Energy consumption thresholds I1 and I2 are set, with I1 > I2 > 0. When I ≥ I1, the energy consumption evaluation of the chiller station is excellent; when I2 ≤ I < I1, the energy consumption evaluation of the chiller station is good; and when I < I2, the energy consumption evaluation of the chiller station is poor. For example, I1 = 5.0 and I2 = 4.3 are set. After collecting the inner temperature, outer temperature, chilled water flow rate, and power consumption of multiple chiller stations, the time proportion and average energy efficiency of each load rate interval are calculated and summarized to form a data report. Table 3 shows that among the energy consumption coefficient evaluation indicators of 40 samples, there are 11 samples with I ≥ 5.0, 13 samples with energy consumption coefficient evaluation indicator values ​​of 4.3 ≤ I < 5.0, and 6 samples with I < 4.3.

[0190]

[0191] like Figures 6-7 As shown, when I ≥ 5.0 in the sample, the refrigeration station mostly exhibits the characteristics of high-efficiency operation under medium- and high loads, with a relatively small proportion of low-load operation time. That is, the proportion of high-efficiency operation time under medium- and high loads and the average energy efficiency are relatively high, making a more prominent contribution to the energy consumption coefficient evaluation index. This indicates that when operating in this load rate range for a long time, the refrigeration station can output a high cooling capacity while maintaining low energy consumption. It is worth noting that there is a special sample, which, although the proportion of high-efficiency operation time under medium- and high loads and the average energy efficiency are relatively low, still has an energy consumption coefficient evaluation index exceeding 5.0 and is judged as excellent. This sample has a relatively high proportion of low-load rate operation time and excellent average energy efficiency performance, making the product of the proportion of time under medium- and low loads and the average energy efficiency significantly contribute to the total score. At the same time, the proportion of low-load operation time is small, avoiding the adverse effects of low-load inefficient operation on the overall energy consumption coefficient evaluation index.

[0192] Please see Figure 8 The present invention also provides a system for determining the energy consumption coefficient evaluation index of a refrigeration plant system. The system is used to execute the above-described method for determining the energy consumption coefficient evaluation index of a refrigeration plant system, comprising:

[0193] The load rate interval division module is used to collect the temperature inside and outside the chiller, chilled water flow, power consumption and input power at equal time intervals. Based on thermodynamic principles, it calculates the cooling capacity of the chiller according to the temperature difference between the inside and outside and the chilled water flow. Based on the cooling capacity and the rated cooling capacity, it calculates the load rate of each collection point and divides different load rate intervals.

[0194] The performance coefficient characteristic curve fitting module is used to calculate the performance coefficient of each sampling point using cooling capacity and power consumption, calculate the rate of change based on the input power, cooling capacity and the temperature difference between the inside and outside of the sampling point, divide the sampling points for steady-state operation according to the input power, cooling capacity and the rate of change of the temperature difference between the inside and outside of the sampling point, extract the performance coefficient and load rate of the sampling points for steady-state operation, and use a quadratic polynomial to fit the performance coefficient characteristic curve of each load rate interval.

[0195] The non-steady-state operation determination module is used to compare the performance coefficient of the sampling point at the current time with the fitted performance coefficient characteristic curve. When the deviation exceeds the preset deviation threshold, it is determined to be a non-steady-state operation sampling point. After removing the non-steady-state sampling points, the number and time proportion of sampling points in each load rate interval are counted.

[0196] The energy consumption index construction module is used to set the attenuation factor, calculate the average energy efficiency of each load rate interval based on the exponential weighted average method of the attenuation factor, and construct and analyze the energy consumption coefficient evaluation index model by real-time weighting based on the time proportion and average energy efficiency of each load rate interval.

[0197] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0198] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0199] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0200] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for determining the energy consumption coefficient evaluation index of a refrigeration station system, characterized in that, The specific steps include: Step 1: The chiller station collects the temperature inside and outside the chiller station, chilled water flow rate, power consumption and input power at equal time intervals. Based on the thermodynamic principle, the cooling capacity of the chiller station is calculated according to the temperature difference between the inside and outside and the chilled water flow rate. The load rate of each collection point is calculated according to the cooling capacity and the rated cooling capacity, and different load rate intervals are divided. Step 2: Calculate the performance coefficient of each sampling point using cooling capacity and power consumption. Calculate the rate of change based on the input power, cooling capacity, and temperature difference between the inside and outside of the sampling point. Divide the sampling points into steady-state operation points according to the input power, cooling capacity, and rate of change of the temperature difference between the inside and outside of each sampling point. Extract the performance coefficient and load rate of the sampling points in steady-state operation. Use a quadratic polynomial to fit the performance coefficient characteristic curve of each load rate interval. Step 3: Compare the performance coefficient of the current sampling point with the fitted performance coefficient characteristic curve. When the deviation exceeds the preset deviation threshold, it is determined to be an unsteady operation sampling point. After removing the unsteady sampling points, the number and time proportion of sampling points in each load rate interval are counted. Step 4: Set the attenuation factor, calculate the average energy efficiency of each load rate interval using the exponential weighted average method based on the attenuation factor, and construct and analyze the energy consumption coefficient evaluation index model by real-time weighting based on the time proportion and average energy efficiency of each load rate interval.

2. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 1, characterized in that: Based on thermodynamic principles, the method for calculating the cooling capacity of a chiller plant using the temperature difference between the inside and outside and the chilled water flow rate is as follows: The temperature data collected each time inside the refrigeration station are recorded sequentially according to the collection time as: T in，1 T in，2 ... T in，n , where n represents the total number of data collections; The temperature data collected each time outside the refrigeration station are recorded sequentially in chronological order as: T out，1 ·T out，2 ... T out，n The traffic data collected each time is recorded sequentially in chronological order as: F1, F2, ..., F n ; The power consumption for each data collection is recorded sequentially in chronological order as: E1, E2, ..., E n ; Extract chilled water flow rate data, obtain chilled water density, and calculate chilled water mass flow rate: m i′ =ρ·F i′ In the formula, m i′ Let F represent the mass flow rate of chilled water collected in the i′th sampling, ρ represent the density of chilled water, and F represent the mass flow rate of chilled water collected in the i′th sampling. i′ This represents the traffic data collected in the i′th time, where i′ represents the index of the collection number, i′=1,2,...,n; Calculate the temperature difference between the inside and outside: ΔT i′ =T out，i′ -T in，i′ In the formula, ΔT i′ T represents the temperature difference between the inner and outer sides during the i′-th sampling. in，i′ T represents the temperature inside the refrigeration station during the i′th data collection. out，i′ This represents the temperature outside the freezing station collected in the i′th sampling. A formula for calculating cooling capacity is constructed using chilled water mass flow rate, chilled water specific heat capacity, and the temperature difference between the inside and outside: Q i′ =c·m i′ ·ΔT i′ In the formula, Q i′ Let represent the cooling capacity collected in the i′th sampling, and c represent the specific heat capacity of the chilled water.

3. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 2, characterized in that: The method for calculating the load rate of each sampling point based on the cooling capacity and rated cooling capacity, and dividing different load rate ranges, is as follows: By comparing the actual cooling capacity with the rated cooling capacity, the load factor formula is constructed: In the formula, L i′ Q represents the load factor of the i′th data collection. rated Indicates the rated cooling capacity of the refrigeration station system; The load factor range of 0%-29% is divided into low load factor range A, 30%-59% load factor range B, 60%-89% load factor range C, and 90%-100% load factor range D.

4. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 2, characterized in that: The method for dividing the sampling points for steady-state operation based on the input power, cooling capacity, and rate of change of the temperature difference between the inner and outer sides at each sampling point is as follows: Extract the input power P1, P2, ..., P from the refrigeration station at equal time intervals. n Calculate the i-th ′ Power change rate at each sampling point: In the formula, ΔP i′ This represents the rate of change of input power at the i′-th sampling point; Calculate the rate of change of cooling capacity at the i′th sampling point: In the formula, ΔQ i′ This represents the rate of change of power at the i′-th sampling point; Calculate the rate of change of the temperature difference between the inside and outside of the i′-th sampling point: In the formula, ΔT mi′ This represents the rate of change of the temperature difference between the inner and outer sides of the i′-th sampling point; Calculate the performance coefficient of the i′-th sampling point: In the formula, COP i′ This represents the performance coefficient of the i′-th sampling point; For satisfying ΔP i′ ≤0.5, ΔQ i′ ≤0.5, ΔT mi′ Sampling points ≤0.5 are considered to be in a steady state. The performance coefficient and load factor of sampling points in a steady state are recorded as COP. l and L l Where l represents the index of the historical steady-state operating data, l = 1, 2, ..., n0, n0 represents the number of sampling points selected in the steady state, and n0 is a positive integer higher than 1000.

5. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 2, characterized in that: The method for extracting the performance coefficient and load factor of the sampling points under steady-state operation, and fitting the performance coefficient characteristic curve of each load factor interval using a quadratic polynomial is as follows: Extract and record the performance coefficient (COP) of sample points where the load rate is in the low load rate range (A). l1 and load factor L l1 Coefficient of performance (COP) in the low-to-medium load range B l2 and load factor L l2 The coefficient of performance (COP) for the medium-to-high load factor range C and the load factor range D l3 and load factor L l3 ; Extracting the performance coefficient (COP) of region A under low load conditions l1 and load factor L ll The performance coefficient characteristic curve is fitted using a quadratic polynomial: COP A =a1·L 2 +b1·L+c In the formula, COP A The performance coefficient characteristic curve represents the low load rate range A, where L represents the load rate, and a1 and b1 represent the coefficients. Extracting the performance coefficient (COP) for the low-to-medium load rate range B. l2 and load factor L l2 The performance coefficient characteristic curve is fitted using a quadratic polynomial: COP B =a2·L 2 +b2·L+c In the formula, COP B The performance coefficient characteristic curve represents the low-to-medium load rate range B, where a2 and b2 represent coefficients. Extract the coefficient of performance (COP) for the medium-to-high load factor range C and the load factor range D. l3 and load factor L l3 The performance coefficient characteristic curve is fitted using a quadratic polynomial: COP C，D <a3·L 2 +b3·L+c In the formula, COP C，D The performance coefficient characteristic curves represent the performance coefficients in the medium-to-high load rate range C and the load rate range D, where a3 and b3 represent coefficients, and the coefficients are solved using the least squares method.

6. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 3, characterized in that: The method for determining sampling points as non-steady-state operation points, and then counting the number and time percentage of sampling points within each load rate interval after removing non-steady-state sampling points, is as follows: Set a deviation threshold k, where 0 < k ≤ 0.1, extract the load rate of the sampling point at the current time, determine its load rate range, and then calculate the theoretical coefficient of performance (COP) through its corresponding performance coefficient characteristic curve. ll Extract the performance coefficient (COP) of the current sampling point. n Calculate the deviation: In the formula, r represents the deviation of the performance coefficient of the sampling point at the current time. When r ≥ k, the sampling point is determined to be a non-steady-state operation sampling point. After removing non-steady-state sampling points, the number of sampling points in each load rate interval and the total number of sampling points are counted. For each load rate interval, the time proportion of the sampling points in steady-state operation is calculated. The time proportion of the low load rate interval A is: In the formula, ω A N represents the percentage of time in the low load period A. A N represents the number of sampling points in the low load rate interval A, and N represents the total number of sampling points. The time percentage of the low-to-medium load range B is as follows: In the formula, ω B n represents the time percentage of the low-to-medium load range B. B This indicates the number of sampling points in the low-to-medium load rate range B. The time percentage of the medium-to-high load factor range C is as follows: In the formula, ω C N represents the time percentage of the medium-to-high load range C. C This indicates the number of sampling points in the medium-to-high load rate range C; The time percentage of the high load period D is: In the formula, ω D N represents the time percentage of the high load period D. D This indicates the number of sampling points in the high load rate interval D; Calculate the performance coefficients at each sampling point within each load rate range under steady-state operation using cooling capacity and power consumption: In the formula, η j Q represents the performance coefficient of the j-th sampling point in each load rate interval. j E represents the cooling capacity at the j-th sampling point in each load rate interval. j This represents the power consumption at the j-th sampling point in each load rate interval, where j represents the index of the sampling point within each load rate interval.

7. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 6, characterized in that: The method for calculating the average energy efficiency for each load factor range based on the exponentially weighted average method using a set attenuation factor is as follows: Calculate average energy efficiency: In the formula, Indicates average energy efficiency; Constructing the decay factor: In the formula, α represents the attenuation factor; Calculate the average energy efficiency for each load factor range: In the formula, η A η represents the average energy efficiency in the low load factor range A. B η represents the average energy efficiency in the low-to-medium load range B. c η represents the average energy efficiency of C in the medium-to-high load factor range. D η represents the average energy efficiency in the high load factor range D. j N represents the performance coefficient of the j-th sampling point in each load rate interval. A N represents the number of sampling points in the low load rate interval A. B N represents the number of sampling points in the low-to-medium load rate range B. C N represents the number of sampling points in the medium-to-high load rate range C. D This indicates the number of sampling points in the high load rate interval D.

8. The method for determining the energy consumption coefficient evaluation index of a refrigeration station system according to claim 7, characterized in that: The method for constructing a real-time weighted formula to calculate and evaluate the energy consumption coefficient evaluation index is as follows: By using the time proportion of each load rate interval and average energy efficiency under steady-state operation, a weighted formula for calculating the real-time energy consumption coefficient evaluation index is constructed: l=ω A ·or A +oh B ·or B +oh C ·or C +oh D ·or D In the formula, I represents the energy consumption coefficient evaluation index. Energy consumption thresholds I1 and I2 are set, and I1 > I2 > 0. When I ≥ I1, the energy consumption evaluation of the refrigeration station is excellent; when I2 ≤ I < I1, the energy consumption evaluation of the refrigeration station is good; and when I < I2, the energy consumption evaluation of the refrigeration station is poor.

9. A system for determining the energy consumption coefficient evaluation index of a refrigeration station system, characterized in that: The system is used to execute the method for determining the energy consumption coefficient evaluation index of a refrigeration station system as described in any one of claims 1-8: The load rate interval division module is used to collect the temperature inside and outside the chiller, chilled water flow, power consumption and input power at equal time intervals. Based on thermodynamic principles, it calculates the cooling capacity of the chiller according to the temperature difference between the inside and outside and the chilled water flow. Based on the cooling capacity and the rated cooling capacity, it calculates the load rate of each collection point and divides different load rate intervals. The performance coefficient characteristic curve fitting module is used to calculate the performance coefficient of each sampling point using cooling capacity and power consumption, calculate the rate of change based on the input power, cooling capacity and the temperature difference between the inside and outside of the sampling point, divide the sampling points for steady-state operation according to the input power, cooling capacity and the rate of change of the temperature difference between the inside and outside of the sampling point, extract the performance coefficient and load rate of the sampling points for steady-state operation, and use a quadratic polynomial to fit the performance coefficient characteristic curve of each load rate interval. The non-steady-state operation determination module is used to compare the performance coefficient of the sampling point at the current time with the fitted performance coefficient characteristic curve. When the deviation exceeds the preset deviation threshold, it is determined to be a non-steady-state operation sampling point. After removing the non-steady-state sampling points, the number and time proportion of sampling points in each load rate interval are counted. The energy consumption index construction module is used to set the attenuation factor, calculate the average energy efficiency of each load rate interval based on the exponential weighted average method of the attenuation factor, and construct and analyze the energy consumption coefficient evaluation index model by real-time weighting based on the time proportion and average energy efficiency of each load rate interval.

Images