A temperature-flow type cold source system optimization scheduling method and system

Abstract

The application discloses a temperature-flow type cold source system optimization scheduling method and system in the technical field of central air conditioner energy-saving optimization control. The method comprises the following steps: establishing an energy input and output model of a cold source device; based on the established energy input and output model, a temperature-flow model of the cold source device is established; based on actual operation data of a cold machine, a cold machine performance coefficient characterization model with a temperature correction term is established; the cold machine performance coefficient characterization model with the temperature correction term is brought into the temperature-flow model of the cold source device, and a nonlinear term in a cold source system optimization scheduling model is eliminated, so that a temperature-flow linear model of the cold source device considering a variable cold machine performance coefficient is established; and based on the established temperature-flow linear model of the cold source device considering the variable cold machine performance coefficient, a temperature-flow type cold source system optimization scheduling plan is acquired. The application reduces the calculation complexity in the optimization scheduling process and improves the accuracy of the optimization scheduling result.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving optimization and control technology of central air conditioning, specifically relating to a method and system for optimizing and scheduling a temperature-flow type cold source system. Background Technology

[0002] The operation of different devices in a cooling system is interconnected and mutually influential. Under the same cooling load demand, the system can operate in multiple modes to meet it. Optimal scheduling of the cooling system is based on cooling load forecast data to find the best operating mode and optimal operating parameters. This allows for optimized scheduling of cooling capacity equipment and cold storage equipment to maintain efficient operation and thus meet cooling load demands at different times with minimal operating costs.

[0003] Existing optimization scheduling methods for cold source systems typically use energy (cooling capacity) as the optimization variable. However, energy (cooling capacity) cannot be directly issued as an instruction to the cold source equipment. The instructions that the cold source equipment can usually receive are control parameters such as flow rate and outlet temperature. In addition, the coefficient of performance (COP) of the chiller usually varies between 4 and 7, and the COP of the chiller usually varies greatly. Moreover, temperature also has a significant impact on the COP of the chiller. Existing optimization scheduling methods for cold source systems simply treat the COP of the chiller as a constant, or the existing optimization scheduling methods do not include temperature / flow rate variables, thus failing to consider the impact of temperature on the COP of the chiller. All of these will affect the accuracy of the optimization scheduling results. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing the scheduling of temperature-flow type cold source systems, which reduces the computational complexity of the optimization scheduling process and improves the accuracy of the optimization scheduling results.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] Firstly, a method for optimizing the scheduling of a temperature-flow type cold source system is provided, comprising: establishing an energy input-output model of the cold source equipment; establishing a temperature-flow model of the cold source equipment based on the established energy input-output model; establishing a performance coefficient characterization model of the chiller with a temperature correction term based on the actual operating data of the chiller; substituting the performance coefficient characterization model of the chiller with a temperature correction term into the temperature-flow model of the cold source equipment to eliminate the nonlinear terms in the optimized scheduling model of the cold source system, and establishing a linear temperature-flow model of the cold source equipment considering the performance coefficient of the chiller; and obtaining an optimized scheduling plan for the temperature-flow type cold source system based on the established linear temperature-flow model of the cold source equipment considering the performance coefficient of the chiller.

[0007] Furthermore, the cold source equipment includes a chiller and a cold storage device; the operating constraints of the energy input and output model of the cold source equipment include start-stop constraints, ramp-up rate constraints, upper and lower limits of cooling power constraints, mutual exclusion constraints of operating modes, capacity constraints of the cold storage device, and cold supply and demand balance constraints.

[0008] Furthermore, based on the established energy input-output model, the temperature-flow model of the cold source equipment is established by: expanding the outlet temperature of the cold source equipment into several state variables in binary format; decomposing the cooling capacity of the cold source equipment as a product of flow and temperature variables into several product terms of analog and state variables; linearizing the product terms of analog and state variables; and establishing the temperature-flow model of the cold source equipment.

[0009] Furthermore, the establishment of the refrigeration unit performance coefficient characterization model with a temperature correction term specifically involves the following: the fitted characterization form of the refrigeration unit performance coefficient COP is:

[0010]

[0011] in, This indicates that the EC outlet temperature of the chiller is set to... Time-cooled machine input electrical energy The nonlinear functional relationship with COP This indicates the lower limit of the refrigeration unit outlet temperature as indicated on the nameplate. This represents the outlet temperature of the i-th chiller. The term α represents the correction term for the COP caused by the chiller outlet temperature, and α represents the temperature correction coefficient, which is used to characterize the degree of influence of the chiller outlet temperature on the COP. Based on the actual operating data of the chiller, the COP is fitted and modeled in this characterization form to establish a chiller performance coefficient characterization model with temperature correction term.

[0012] Furthermore, the chiller performance coefficient characterization model with temperature correction term is incorporated into the cold source equipment temperature-flow model to eliminate nonlinear terms in the optimization scheduling model, establishing a cold source equipment temperature-flow linear model considering the variable chiller performance coefficient. Specifically, the chiller output cooling capacity Q EC The representation form is as follows:

[0013]

[0014] Introducing intermediate variables By piecewise linear fitting Linearization; outlet temperature of the i-th chiller The binary code is expanded into several state variables, and the chiller outputs cooling capacity Q. EC The representation only contains the analog quantity Q EC,base By multiplying the analog quantity with several state variables, the product terms of the analog quantity and the state variables are linearized, and a linear temperature-flow model of the cold source equipment considering the performance coefficient of the variable chiller is established.

[0015] Furthermore, the step of obtaining an optimal scheduling plan for a temperature-flow type cold source system based on the established temperature-flow linear model of the cold source equipment considering the performance coefficient of the variable chiller is as follows: taking the minimization of operating cost as the objective function, the optimal scheduling problem of the cold source system is transformed into a 0-1 mixed integer linear optimization problem based on the temperature-flow linear model of the cold source equipment considering the performance coefficient of the variable chiller, and the optimal scheduling plan of the temperature-flow type cold source system that directly includes temperature and flow rate is obtained after solving the problem.

[0016] Secondly, a temperature-flow type cold source system optimization scheduling system is provided, comprising: a first module for establishing an energy input-output model of the cold source equipment; a second module for establishing a temperature-flow model of the cold source equipment based on the established energy input-output model; a third module for establishing a chiller performance coefficient characterization model with a temperature correction term based on the actual operating data of the chiller; a fourth module for inputting the chiller performance coefficient characterization model with a temperature correction term into the cold source equipment temperature-flow model, eliminating nonlinear terms in the optimization scheduling model, and establishing a cold source equipment temperature-flow linear model considering the variable chiller performance coefficient; and a fifth module for obtaining an optimized scheduling plan for the temperature-flow type cold source system based on the established cold source equipment temperature-flow linear model considering the variable chiller performance coefficient.

[0017] Compared with the prior art, the beneficial effects achieved by this invention are as follows: This invention expands the temperature analog quantity into several state quantities in binary form, establishes a chiller performance coefficient characterization model with temperature correction terms, eliminates nonlinear terms in the optimal scheduling model of the cold source system, establishes a linear temperature-flow model of the cold source equipment considering the variable chiller performance coefficient, directly solves the operating control parameters such as temperature and flow of the refrigeration / cold storage equipment, reduces the computational complexity in the optimal scheduling process, improves the accuracy of the optimal scheduling results, keeps the chiller performance coefficient at a high level, and improves the operating economy of the cold source system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the main process of a temperature-flow type cold source system optimization scheduling method provided in an embodiment of the present invention;

[0019] Figure 2 This is a cooling load prediction diagram in an embodiment of the present invention;

[0020] Figure 3 This is a graph showing the variation of the chiller's coefficient of performance (COP) with load rate and outlet temperature in an embodiment of the present invention.

[0021] Figure 4 This is a graph showing the change in chiller flow rate / outlet temperature as a result of optimized scheduling of the cold source system in an embodiment of the present invention.

[0022] Figure 5 The graph shows the change in the coefficient of performance (COP) of the chiller as a result of the optimized scheduling of the cold source system in an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0024] Example 1:

[0025] like Figure 1 As shown, a temperature-flow type cold source system optimization scheduling method includes: establishing an energy input-output model of the cold source equipment; establishing a temperature-flow model of the cold source equipment based on the established energy input-output model; establishing a performance coefficient characterization model of the cold source equipment with a temperature correction term based on the actual operating data of the cold source equipment; substituting the performance coefficient characterization model of the cold source equipment with a temperature correction term into the temperature-flow model of the cold source equipment to eliminate the nonlinear terms in the cold source system optimization scheduling model, and establishing a linear temperature-flow model of the cold source equipment considering the performance coefficient of the cold source equipment; and obtaining an optimized scheduling plan for the temperature-flow type cold source system based on the established linear temperature-flow model of the cold source equipment considering the performance coefficient of the cold source equipment.

[0026] Step 1: Establish the energy input and output model of the cold source equipment, which includes chillers and cold storage equipment. The operating constraints of the energy input and output model of the cold source equipment include start-up and shutdown constraints, ramp-up rate constraints, upper and lower limits of cooling power constraints, mutual exclusion constraints of operating modes, capacity constraints of cold storage devices, and supply and demand balance constraints of cold energy.

[0027] Taking an electrical chiller (EC) as an example, based on the operating principle of the chiller, its input-output relationship can be modeled as follows:

[0028]

[0029] Its corresponding operating limitations are:

[0030]

[0031] in, This represents the electrical energy consumed by the i-th chiller at time t. This represents the cooling energy produced by the i-th chiller at time t. This represents the performance coefficient of the i-th chiller. This indicates the maximum amount of cooling energy produced by the chiller. This represents the minimum amount of cooling energy produced by the chiller, which can be obtained from the chiller's nameplate, in μ. EC This represents the chiller power ramp-up rate factor. This represents a 0-1 variable indicating the on / off state of the i-th chiller at time t. This is a 0-1 variable indicating whether the i-th chiller is in the start-up process at time t.

[0032] In addition to production equipment, cold source systems also include cold storage equipment to smooth out peak demand and fill valleys. Taking a water-based cold storage tank (TA) as an example, its model is as follows:

[0033]

[0034] Among them, S TA (t) represents the cooling capacity of the water tank at time t. Q represents the initial cooling capacity of the water tank. TA,ch (t) represents the cooling capacity of the water tank at time t, Q TA,dis (t) represents the amount of cooling released from the water tank at time t, δ TA,ch (t) represents the 0-1 variable indicating cold storage at time t, δ TA,dis (t) represents a 0-1 variable indicating the release of cold at time t. This represents the minimum cooling capacity of the water tank at time t. This represents the maximum cooling capacity of the water tank at time t.

[0035] The chiller and the chilled water storage tank together meet the terminal cooling load demand Q load (t), excess cooling capacity is sent to the water tank for storage, that is:

[0036]

[0037] Step 2: Based on the established energy input-output model, establish a temperature-flow model for the cold source equipment. Specifically, expand the outlet temperature of the cold source equipment into several state variables in binary format, and decompose the cooling capacity of the cold source equipment into several product terms of analog and state variables. Linearize the product terms of analog and state variables to establish a temperature-flow model for the cold source equipment.

[0038] Based on the energy input and output model of the cold source equipment established in step one, temperature and flow rate variables are introduced, based on the cooling capacity of the i-th refrigeration unit. Energy conservation is established with inlet and outlet flow rates / temperature differences to create a temperature-flow scheduling model. This is achieved by expanding the analog outlet temperature into several state variables using binary representation, with the outlet water temperature of the i-th chiller as an example. For example:

[0039]

[0040] Where, ΔT EC This indicates the chiller outlet water temperature resolution, where K represents the number of bits in the binary expansion. Let k represent the 0-1 expansion quantity of the k-th bit, where k = 1, 2, ..., K. Then the chiller temperature-flow rate model can be expressed as:

[0041]

[0042] in, This represents the chilled water flow rate of the i-th chiller at time t. This represents the pump power consumed by the chilled water flow rate at time t for the i-th chiller. This indicates the rated power on the nameplate of the chilled water pump. This indicates the rated flow rate (T) of the chilled water pump. ret (t) represents the chilled water inlet temperature of the chiller at time t, which is also the total return water temperature of the chilled water system. p The specific heat capacity of water, As an auxiliary variable, it represents the equivalent flow rate through the k-segment temperature difference.

[0043] Similarly, the temperature-flow rate model for a cold water storage tank can be expressed as:

[0044]

[0045] Where, q TA,ch (t) represents the flow rate of chilled water entering the cold water storage pipe, q TA,dis (t) represents the flow rate of chilled water flowing out of the chilled water storage pipe, and its temperature is T. TA It should be a fixed value; otherwise, it would disrupt the natural stratification of water for cooling. (T) ret (t) represents the total return water temperature of the chilled water system at time t. The required chilled water temperature in the water tank during the cold storage phase is T. TA Therefore, either the water tank is in the cooling release phase, or the chilled water temperature produced by the chiller must be T. TA ,Right now:

[0046]

[0047] Step 3: Based on the actual operating data of the chiller, establish a chiller performance coefficient characterization model with a temperature correction term; considering the variation of the chiller performance coefficient COP with load rate and outlet temperature, the characterization form of the chiller performance coefficient COP is as follows:

[0048]

[0049] in, This indicates that the chiller outlet temperature is set to... Input electrical energy The nonlinear functional relationship with COP This indicates the lower limit of the refrigeration unit outlet temperature as indicated on the nameplate. This represents the outlet temperature of the i-th chiller. The term characterizing the correction of COP by the chiller outlet temperature represents an increase in the outlet temperature setpoint to... At that time, the coefficient of performance of the refrigeration unit is increased to the reference state. Based on actual operating data of the chiller, the COP is fitted and modeled in this representation form to establish a chiller performance coefficient representation model with temperature correction term.

[0050] Step 4: Substitute the chiller performance coefficient characterization model with temperature correction term into the cold source equipment temperature-flow model to eliminate the nonlinear term in the cold source system optimization scheduling model, and establish a cold source equipment temperature-flow linear model considering the variable chiller performance coefficient; the chiller output cooling capacity is represented as follows:

[0051]

[0052] Introducing intermediate variables By piecewise linear fitting Linearization; outlet temperature of the i-th chiller The binary code is expanded into several state variables, and the chiller outputs cooling capacity Q. EC The representation only contains the analog quantity Q EC,base By multiplying the analog quantity with several state variables, the product terms of the analog quantity and the state variables are linearized, and a linear temperature-flow model of the cold source equipment considering the performance coefficient of the variable chiller is established.

[0053] By adopting a performance coefficient characterization model for the chiller with a temperature correction term, and substituting equation (8-1) into equation (1), we can obtain:

[0054]

[0055] By introducing the following intermediate variable:

[0056]

[0057] in, This indicates that the chiller outlet temperature is set to... The input electrical energy and output cooling energy function is derived from actual data fitting and considers the variation of the chiller's coefficient of performance under different partial load rates. This function can be approximated by fitting a three-segment linear function. Through linear fitting and substituting the intermediate variables introduced in equation (10), equation (8) can be reorganized as:

[0058]

[0059] in, The x-coordinates of the two segment points are respectively The slopes of the three segments are respectively And passed through point A three-segment linear piecewise function is used to approximate the partial load characteristic function of the chiller. This can be understood as the consumption of the i-th chiller. The electrical energy and chiller outlet temperature are set to The baseline cold energy generated at that time, when consuming the same amount of electricity, results in a temperature increase of [missing value]. At that time, the coefficient of performance (COP) of the chiller increases, and the final cooling capacity produced is Equation (11) is the linearized form of equation (1) under the premise of considering the performance coefficient of the chiller.

[0060] Step 5: Based on the established temperature-flow linear model of the cold source equipment considering the performance coefficient of the variable chiller, obtain the optimal scheduling plan for the temperature-flow type cold source system. Specifically, with minimizing the operating cost as the objective function, based on the temperature-flow linear model of the cold source equipment considering the performance coefficient of the variable chiller, the optimal scheduling problem of the cold source system is transformed into a 0-1 mixed integer linear optimization problem. After solving the problem, the optimal scheduling plan for the temperature-flow type cold source system that directly includes temperature and flow rate is obtained.

[0061] This paper proposes an optimization problem for a linearized temperature-flow model of energy equipment, using a 0-1 mixed-integer linear optimization approach, with the objective of minimizing operating costs or other types of objective functions. Its formal expression is as follows:

[0062]

[0063] Where, λ e (t) represents the time-of-use electricity price at time t, λ EC This represents the start-up cost of the chiller, used to prevent frequent start-ups and shutdowns. Nr represents the number of scheduling cycles, and nEC represents the number of chillers. An optimization solution yields a temperature-flow-based scheduling plan for the cooling system.

[0064] This embodiment expands the analog temperature quantity into several state quantities using binary expansion. By establishing a chiller performance coefficient characterization model with a temperature correction term, the nonlinear terms in the cold source system optimization scheduling model are eliminated. A linear temperature-flow model of the cold source equipment considering the variable chiller performance coefficient is established. The operating control parameters such as temperature and flow of the refrigeration / cold storage equipment are directly solved, reducing the computational complexity in the optimization scheduling process, improving the accuracy of the optimization scheduling results, keeping the chiller performance coefficient at a high level, and improving the operating economy of the cold source system.

[0065] The invention will now be described in further detail with reference to specific examples.

[0066] Step 1: Based on the energy input and output relationships of the cold source system equipment, establish an energy input and output model for the cold source equipment. In this example, the cold source system contains three identical chillers (nEC=3), with upper and lower limits for their cooling capacity output. The power is [760, 3500] kW. In addition, a cold water storage tank with a capacity of [missing information] is configured. The upper and lower limits of the cold storage rate are [2000, 47000] kWh. The upper and lower limits of the cooling rate are [0, 10000] kW. The initial cold storage capacity of the cold water storage tank is [0, 10000] kW. The capacity is 2000 kWh. The cooling load Q for a complete scheduling cycle from 23:00 to 23:00 the following day, with a resolution of 1 hour. load (t) The prediction results are attached. Figure 2 .

[0067] Substitute the above values ​​into equations (2) to (4) to complete step one.

[0068] Step two: Based on the energy input / output model of the chilled water source equipment, establish a temperature-flow model for the chilled water source equipment by establishing the energy conservation relationship between the output cooling capacity and the inlet / outlet water temperature / flow rate. For the chiller, the chilled water return main pipe temperature T... ret (t) Assuming a stable temperature of 13℃, the upper and lower limits of the outlet temperature setpoint. Given a temperature range of [4.5, 8.5]℃, and taking a binary expansion bit depth K = 3, the chiller outlet water temperature resolution is 0.5℃. ΔT EC Rated power and rated flow rate of chilled water pump The specific heat capacity of water is 200kW and 180kg / s respectively. p The value is taken as 4.2 kJ / (kg·℃). The storage / release temperature T of the cold water tank is... TA The temperature was set at 5℃.

[0069] Substitute the above values ​​into equations (5) to (8) to complete step two.

[0070] Step 3: Based on actual operating data or factory test data of the chiller, considering the relationship between the chiller's coefficient of performance (COP) and load rate and outlet temperature, establish a COP characterization model with a temperature correction term. This example is based on measured chiller data; the relationship between the COP and different load rates and different outlet temperature setpoints is shown in the attached figure. Figure 3 .

[0071] Step 4: Based on the temperature-flow model of the cold source equipment, a linear temperature-flow model of the cold source equipment with variable chiller performance coefficients is established by adopting a chiller performance coefficient characterization model with a temperature correction term to eliminate the nonlinear term in the optimization scheduling model. This is achieved by fitting a three-segment linear piecewise function [(260,410),(7.0602,7.8012,5.6795),(160,670)] (representing a three-segment linear piecewise function with x-coordinates of 260 and 410, slopes of 7.0602, 7.8012, and 5.6795, and passing through the point (160,670)). Then, α...EC Set to 0.03 (i.e., in) Within the range, for every 1°C increase in the outlet set temperature, the coefficient of performance of the chiller increases by 3%. Substitute the above values ​​into equation (11) to complete step three.

[0072] Step 5: Based on the temperature-flow model of the cooling source equipment using the coefficient of performance (COP) of the variable chiller, and with the objective function of minimizing operating costs or other types of revenue, the optimal scheduling problem of the cooling source system is transformed into a 0-1 mixed integer linear optimization mathematical problem. Solving this problem yields the optimal scheduling plan for the cooling source system. In this example, the scheduling resolution is 1 hour, the number of scheduling cycles Nr is 24, the electricity price is 0.38 yuan / kWh during the off-peak period from 23:00 to 7:00 the next day, the peak period is 1.13 yuan / kWh from 9:00 to 12:00 and 19:00 to 23:00, and the remaining time periods are flat periods with a price of 0.73 yuan / kWh. The chiller's start-up cost λ EC The price is set at 100 yuan per time.

[0073] Substituting the above values ​​into the objective function (12), the linear optimization solver is used to solve this 0-1 mixed integer linear optimization problem, and the final optimized scheduling result is obtained. During the off-peak electricity price period, the optimized scheduling result shows that in addition to meeting the basic cooling load demand, the excess cooling capacity is stored in the cold storage tank; during the flat electricity price period, the main cooling capacity is released from the cold storage tank, and the insufficient portion is supplemented by the chiller; during the peak electricity price period, the main cooling capacity is released from the cold storage tank.

[0074] The optimized outlet temperature / flow rate of the chiller at various times is shown in the appendix. Figure 4 The calculated temperature and flow rates are parameters that can be directly controlled by the cold source equipment, and therefore can be directly sent to the device through the control system. Furthermore, this method optimizes the scheduling of COP values ​​for each chiller at various times, as shown in the appendix. Figure 5 It can be seen that because this method takes into account the variable COP characteristics of the chiller, the optimized scheduling results can enable each chiller unit to maintain a high COP value.

[0075] The above-mentioned temperature-flow type cold source system optimization scheduling method considering the coefficient of performance of variable chillers takes into account the changes of chiller coefficient of performance with load rate and outlet temperature, and establishes a linear optimization model of the cold source system that can directly obtain temperature / flow rate. It realizes the rational planning of the cold source system with low computational complexity and avoids resource waste caused by human experience error.

[0076] Example 2:

[0077] Based on the temperature-flow type cold source system optimization scheduling method described in Embodiment 1, this embodiment provides a temperature-flow type cold source system optimization scheduling system, including: a first module for establishing an energy input-output model of the cold source equipment; a second module for establishing a temperature-flow model of the cold source equipment based on the established energy input-output model; a third module for establishing a chiller performance coefficient characterization model with a temperature correction term based on the actual operating data of the chiller; a fourth module for inputting the chiller performance coefficient characterization model with a temperature correction term into the cold source equipment temperature-flow model, eliminating nonlinear terms in the optimization scheduling model, and establishing a cold source equipment temperature-flow linear model considering the variable chiller performance coefficient; and a fifth module for obtaining an optimized scheduling plan for the temperature-flow type cold source system based on the established cold source equipment temperature-flow linear model considering the variable chiller performance coefficient.

[0078] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the scheduling of a temperature-flow type cold source system, characterized in that, include: Establish an energy input / output model for the cold source equipment; Based on the established energy input-output model, a temperature-flow model for the cold source equipment is established. Based on the actual operating data of the chiller, a performance coefficient characterization model of the chiller with a temperature correction term is established; By incorporating the performance coefficient characterization model of the chiller with a temperature correction term into the temperature-flow model of the cold source equipment, the nonlinear term in the optimal scheduling model of the cold source system is eliminated, and a linear temperature-flow model of the cold source equipment considering the performance coefficient of the chiller is established. Based on the established linear temperature-flow model of the cold source equipment that considers the coefficient of performance of the variable chiller, an optimized scheduling plan for the temperature-flow type cold source system is obtained. The establishment of the coefficient of performance characterization model for the chiller with a temperature correction term is specifically as follows: The fitted characterization form of the coefficient of performance (COP) of the chiller is as follows: in, This indicates that the EC outlet temperature of the chiller is set to... Time-cooled machine input electrical energy The nonlinear functional relationship with COP This indicates the lower limit of the refrigeration unit outlet temperature as indicated on the nameplate. This represents the outlet temperature of the i-th chiller. This represents the correction term for the COP caused by the chiller outlet temperature, where α represents the temperature correction coefficient, used to characterize the degree of influence of the chiller outlet temperature on the COP. Based on actual operating data of the chiller, the COP is fitted and modeled using this representation form to establish a chiller performance coefficient representation model with a temperature correction term.

2. The temperature-flow type cold source system optimization scheduling method according to claim 1, characterized in that, The cold source equipment includes a chiller and a cold storage device; the operating constraints of the energy input and output model of the cold source equipment include start-stop constraints, ramp-up rate constraints, upper and lower limits of cooling power constraints, mutual exclusion constraints of operating modes, capacity constraints of the cold storage device, and cold supply and demand balance constraints.

3. The optimized scheduling method for a temperature-flow type cold source system according to claim 1, characterized in that, Based on the established energy input-output model, a temperature-flow model for the cold source equipment is established, specifically as follows: The outlet temperature of the cold source equipment is binary-expanded into several state variables. The cooling capacity of the cold source equipment is decomposed into several analog and state variable product terms. The analog and state variable product terms are linearized to establish a temperature-flow model of the cold source equipment.

4. The temperature-flow type cold source system optimization scheduling method according to claim 1, characterized in that, The process involves incorporating the chiller performance coefficient characterization model with a temperature correction term into the cold source equipment temperature-flow model to eliminate nonlinear terms in the optimization scheduling model and establish a linear temperature-flow model for the cold source equipment considering the variable chiller performance coefficient. Specifically: The chiller outputs cooling capacity Q EC The representation form is as follows: Introducing intermediate variables By piecewise linear fitting Linearization; outlet temperature of the i-th chiller The binary code is expanded into several state variables, and the chiller outputs cooling capacity Q. EC The representation only contains the analog quantity Q EC,base By multiplying the analog quantity with several state variables, the product terms of the analog quantity and the state variables are linearized, and a linear temperature-flow model of the cold source equipment considering the performance coefficient of the variable chiller is established.

5. The temperature-flow type cold source system optimization scheduling method according to claim 1, characterized in that, Based on the established linear temperature-flow model of the cold source equipment considering the coefficient of performance of the variable chiller, the optimized scheduling plan for the temperature-flow type cold source system is obtained, specifically as follows: With minimizing operating costs as the objective function, and based on the temperature-flow linear model of the cold source equipment that considers the performance coefficient of the variable chiller, the optimal scheduling problem of the cold source system is transformed into a 0-1 mixed integer linear optimization problem. After solving the problem, a temperature-flow type optimal scheduling plan for the cold source system that directly includes temperature and flow rate is obtained.

6. A temperature-flow type cold source system optimization scheduling system, characterized in that, include: The first module is used to establish the energy input and output model of the cold source equipment; The second module is used to establish a temperature-flow model for the cold source equipment based on the established energy input-output model. The third module is used to establish a performance coefficient characterization model for the chiller with a temperature correction term based on the actual operating data of the chiller. The fourth module is used to input the performance coefficient characterization model of the chiller with temperature correction terms into the temperature-flow model of the cold source equipment, eliminate the nonlinear terms in the optimization scheduling model, and establish a linear temperature-flow model of the cold source equipment that considers the performance coefficient of the chiller. The fifth module is used to obtain an optimized scheduling plan for the temperature-flow type cold source system based on the established temperature-flow linear model of the cold source equipment that takes into account the coefficient of performance of the variable chiller. The establishment of the coefficient of performance characterization model for the chiller with a temperature correction term is specifically as follows: The fitted characterization form of the coefficient of performance (COP) of the chiller is as follows: in, This indicates that the EC outlet temperature of the chiller is set to... Time-cooled machine input electrical energy The nonlinear functional relationship with COP This indicates the lower limit of the refrigeration unit outlet temperature as indicated on the nameplate. This represents the outlet temperature of the i-th chiller. This represents the correction term for the COP caused by the chiller outlet temperature, where α represents the temperature correction coefficient, used to characterize the degree of influence of the chiller outlet temperature on the COP. Based on actual operating data of the chiller, the COP is fitted and modeled using this representation form to establish a chiller performance coefficient representation model with a temperature correction term.

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