Control method, system, equipment and medium for participation of regional cooling system in emergency demand response of power grid

Abstract

The invention relates to the technical field of regional cooling system control, in particular to a control method, system and device for a regional cooling system to participate in power grid emergency demand response and a medium. According to the method, a theoretical feasible strategy matrix of emergency demand response of the regional cooling system is formed by acquiring a power grid emergency peak clipping scheduling instruction and operation state parameters of the regional cooling system, and a regional cooling system-building thermal environment coupling simulation model is called; simulating hourly electric power, chilled water flow and chilled water temperature of the system under an emergency demand response theoretical feasible control strategy of the cooling system in all regions, and the temperature and relative humidity of air in a user room to form a theoretical feasible strategy peak clipping effect matrix corresponding to the theoretical feasible strategy matrix; and the optimal regional cooling system emergency demand response strategy is obtained through screening by using an optimization solution algorithm with the maximum profit as the target.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of district cooling system control, in particular to a control method, system, device and medium for district cooling system participating in emergency demand response of power grid. BACKGROUND

[0002] Increasing the proportion of renewable energy power and terminal energy electrification brings great challenges to power supply and demand balance and safety and stability. Traditional peak shaving measures on the power generation side cannot meet the needs of new power systems with increasing green power proportion, and the phenomenon of "abandoning wind and light" occurs frequently. Building air conditioning systems are key factors in summer power shortages, and have significant energy flexibility characteristics, especially district cooling systems serving high-density buildings and building groups are high-quality power terminal user-side demand response resources. However, the existing district cooling system has the following shortcomings when participating in the emergency demand response of the power grid; 1. Unable to predict the quantitative relationship between peak shaving amount and user thermal comfort sacrifice; 2. Difficult to achieve rapid and accurate response to peak shaving instructions; 3. The degree of user comfort sacrifice is too large, and the system operation is unstable.

[0003] Therefore, there is a need for a control method that can fully realize the emergency peak shaving potential of the district cooling system while ensuring system operation stability. SUMMARY

[0004] The present application aims to solve the problems in the background art and provides a control method, system, device and medium for district cooling system participating in emergency demand response of power grid.

[0005] The technical solution of the present application provides a control method for district cooling system participating in emergency demand response of power grid, which includes the following specific steps: S1, obtaining the power grid emergency peak shaving dispatching instruction through the power grid operation dispatching platform; S2, obtaining the district cooling system operation state parameters through the district cooling system automatic control system; S3, determining all feasible control strategies based on the power grid emergency peak shaving dispatching instruction and the district cooling system operation state parameters to form a theoretical feasible strategy matrix; S4, calling the district cooling system-building thermal environment coupling simulation model to simulate the parameters of the district cooling system under the emergency demand response theoretical feasible control strategy to form a peak shaving effectiveness matrix corresponding to the theoretical feasible strategy matrix; S5, the decision module uses an optimization algorithm to filter the theoretical feasible strategy matrix based on the constraint conditions and the peak clipping effect matrix, to obtain an actual feasible strategy matrix; the user's income is calculated, and the best emergency demand response strategy is selected as the target of maximum income; S6, when the predetermined demand response start time is reached, the operation state of each device in the regional cooling system is adjusted through the automatic control system based on the best emergency demand response strategy provided by the decision module, and the system operation state is monitored in real time; S7, when the predetermined demand response end time is reached, the operation state of each device in the regional cooling system is adjusted to the normal operation mode through the automatic control system, and the hourly power consumption during the emergency demand response is fed back to the power grid operation dispatching platform as the basis for post-incentive calculation, completing a single emergency demand response.

[0006] Preferably, in step S2, the regional cooling system operation state parameters include: the start-stop state of the chiller unit and its auxiliary equipment, the cold storage device release cold / power storage, and the chilled water supply temperature set value of the chiller unit.

[0007] Preferably, in step S4, the parameters of the cooling system under the theoretical feasible control strategy of the emergency demand response include the hourly power consumption of the system, the chilled water flow, the chilled water temperature, and the temperature and relative humidity of the air in the user room.

[0008] Preferably, in step S4, the regional cooling system-building thermal environment coupling simulation model is used to calculate the peak clipping effect of the regional cooling system under different control strategies and to quantitatively evaluate the user comfort.

[0009] Preferably, in step S5, the optimization algorithm is performed according to the following steps: S51, establish a feasible strategy matrix of the regional cooling system; when participating in the emergency demand response, select the chilled water supply temperature set value and the number of chiller units n as the direct optimization parameters for responding to the emergency peak clipping demand of the power grid, and the operation state of each water pump is determined based on the interlocking relationship between the devices; S52, based on the regional cooling system-building thermal environment coupling simulation model, under the condition that the outdoor meteorological parameters are known, the peak clipping effect that can be achieved by each peak clipping instruction is obtained through simulation , thereby forming a peak clipping effect matrix B corresponding to the peak clipping strategy matrix A, which is a two-dimensional matrix corresponding to the elements in matrix A; wherein, is the number of chiller units and its auxiliary equipment determined in the kth instruction; is the chilled water supply temperature set value of the chiller unit determined in the kth instruction; is the average peak clipping power that can be generated by the kth instruction; is the maximum value of room air temperature in each building; the peak shaving strategy matrix A is a two-dimensional matrix composed of the number of chiller units n and the chilled water supply temperature set value ; S53, based on the emergency peak shaving assessment requirements, user comfort constraints, and device physical constraints, further filtering the peak shaving strategy matrix A and the peak shaving effect matrix B, eliminating elements in the matrix that do not meet the constraint requirements, obtaining a peak shaving strategy feasible instruction set Ac, and the corresponding feasible instruction peak shaving effect matrix Bc; S54, with the minimum "cost-benefit" ratio as the target, the feasible instructions are screened to obtain the best emergency demand response control instruction; based on the feasible instruction peak shaving effect matrix Bc, the "cost-benefit" ratio of each element in the peak shaving strategy feasible instruction set Ac is calculated , the minimum control instruction is selected as the best emergency demand response strategy of the regional cooling system, and the calculation method is as follows:

[0010] In the formula, is the peak shaving "cost-benefit" ratio of the kth control instruction; is the room temperature rise value in the most unfavorable loop caused by the kth control instruction; is the peak shaving power realized by the kth control instruction; S55, through the regional cooling system automatic control system communication protocol connection to the upper computer, the selected best demand response instruction is issued to the register address of the corresponding point of the regional cooling system, so as to realize the short-time accurate reduction of the power of the regional cooling system through the target device operating parameters.

[0011] Preferably, the peak shaving measures meet the following constraints: ① Peak shaving power constraint: the actual peak shaving power is in the range of , that is,

[0012] Wherein: is the target peak shaving power issued by the power grid; a is the peak shaving deviation allowable coefficient; is the actual peak shaving power of the kth strategy.

[0013] ② User comfort constraint: the indoor air temperature of the most unfavorable loop does not exceed the user's highest acceptable temperature , that is:

[0014] Wherein: is the user's highest acceptable temperature;​ predicted average dissatisfaction rate for the kth strategy; maximum dissatisfaction rate allowed by the user.

[0015] The second aspect of the present application provides a control system for participation of a district cooling system in emergency demand response of a power grid, using the above method for control, comprising: a district cooling system-building thermal environment coupling simulation module for calculating the peak shaving effect of the district cooling system under different control strategies and quantitatively evaluating user comfort; including a water chiller, a cooling tower, a cooling water pump, a primary chilled water pump, a secondary chilled water pump, a cold storage water pump, a cold storage device, and a building dynamic heat balance model; An emergency demand response decision module builds an optimization solution algorithm through Python, loads an FMU file based on an interface of a Python FunctionalMock-up Interface library, and realizes communication with an OPC UA server based on a python-opcua library; A communication module completes data interaction with a power grid operation scheduling platform and a district cooling system automatic control system based on a standard communication protocol.

[0016] Preferably, the communication module comprises: obtaining a peak shaving instruction, obtaining a running state of the district cooling system, issuing an emergency demand response control strategy of the district cooling system, and uploading an actual measured power consumption in an emergency demand response process. The communication module connects a SCADA host computer through an OPC UA protocol, issues the control strategy to a register address of a corresponding point of the district cooling system, and optimizes power consumption of the district cooling system through target device running parameters.

[0017] The third aspect of the present application provides a computer storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the above method.

[0018] The fourth aspect of the present application provides an electronic device comprising a memory and a processor: the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, and the computer executable instructions are executed by the processor to realize the steps of the above method.

[0019] Compared with the prior art, the present application has the following beneficial technical effects: 1. Achieve quantitative correlation of "peak shaving amount-comfort", accurately control user comfort sacrifice. Traditional peak shaving strategy only uses "indoor temperature does not exceed a certain threshold" as a fuzzy constraint, which cannot quantify the degree of comfort sacrifice; this patent establishes a quantitative relationship between "peak shaving power-indoor temperature-unhappiness rate" through a regional cooling system-building thermal environment coupled simulation model and a PMV-PPD thermal comfort evaluation model, and selects the optimal strategy through "cost-benefit" to avoid the problem of "excessive comfort sacrifice" in traditional strategies.

[0020] 2. Improve peak shaving response accuracy to meet the requirements of emergency dispatching of power grid. Traditional strategies rely on manual experience to set parameters and lack simulation verification and strategy screening; this patent improves the accuracy of actual peak shaving power control through a closed-loop decision-making mechanism of "feasible strategy matrix construction-coupled simulation verification-multi-constraint screening", which meets the core requirement of "accuracy" of emergency demand response of power grid.

[0021] 3. Improve user economic benefits, and consider both peak shaving effect and economy. Traditional strategies only aim to maximize peak shaving amount without optimizing benefits in combination with power grid incentive rules; this patent uses a double-layer optimization mechanism of "feasible strategy simulation verification+optimal strategy screening" to fully exploit the flexible regulation potential of regional cooling systems, achieving the dual goals of "optimal peak shaving effect and high economic benefits". BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The figure is a schematic diagram of the system architecture of the regional cooling system emergency demand response control method in the present application.

[0023] Figure 2 The figure is a schematic diagram of the implementation process of the regional cooling system emergency demand response strategy in the present application.

[0024] Figure 3 The figure is a comparison curve of the peak shaving effect of the selected case regional cooling station under different control strategies for different peak shaving instructions. DETAILED DESCRIPTION Example 1

[0025] As shown in Figure 2 The first aspect of the present application provides a control method for a regional cooling system participating in emergency demand response of a power grid, which includes the following specific steps: S1. Obtain the emergency peak shaving dispatching instruction of the power grid through the power grid operation dispatching platform; S2. Obtain the regional cooling system operation state parameters through the regional cooling system automatic control system; the regional cooling system operation state parameters include: start-stop state of the water chiller and its auxiliary equipment, cold release / power of the cold storage equipment, and chilled water supply temperature set value of the water chiller.

[0026] S3. The decision-making module determines all feasible control strategies based on the emergency peak shaving dispatch instructions of the power grid and the operating status parameters of the district cooling system, forming a theoretical feasible strategy matrix for emergency demand response of the district cooling system. S4. Call the district cooling system-building thermal environment coupled simulation model to simulate the hourly power consumption, chilled water flow rate, chilled water temperature, and indoor air temperature and relative humidity of the entire district cooling system under the theoretically feasible control strategy for emergency demand response. This forms a theoretically feasible strategy peak shaving effectiveness matrix corresponding to the theoretically feasible strategy matrix. The system parameters under the theoretically feasible control strategy for emergency demand response include the hourly power consumption, chilled water flow rate, chilled water temperature, and indoor air temperature and relative humidity of the user's room. The district cooling system-building thermal environment coupled simulation model is used to calculate the peak shaving effect of the district cooling system under different control strategies and to quantitatively evaluate user comfort.

[0027] S5. The decision module uses an optimization algorithm to filter the theoretically feasible strategy matrix for emergency demand response based on the constraints and the system peak shaving effectiveness matrix under theoretically feasible strategies, and then obtains the actual feasible strategy matrix for emergency demand response based on the pre-agreed emergency demand response incentive calculation method. Finally, it calculates the user benefits under all actual feasible strategies based on the pre-agreed emergency demand response incentive calculation method, and selects the best emergency demand response strategy for the district cooling system with the goal of maximizing benefits. The optimization algorithm in step S5 is performed as follows: S51. Establish a feasible strategy matrix for the district cooling system; when participating in emergency demand response, select the chilled water supply temperature setpoint. The two variables, n, which are the number of chiller units in operation, are used as direct optimization parameters to respond to the emergency peak shaving demand of the power grid. The operating status of each water pump is determined based on the interlocking relationship between the equipment. S52. Based on the coupled simulation model of district cooling system and building thermal environment, peak shaving commands are obtained through simulation when outdoor meteorological parameters are known. Peak reduction effect that can be achieved This forms the peak-shaving effect matrix B, which corresponds to the peak-shaving strategy matrix A. Matrix B is a two-dimensional matrix that corresponds one-to-one with the elements in matrix A; where, The number of chiller units and their auxiliary equipment to be started, as determined in the k-th instruction; The setpoint for the chilled water supply temperature of the chiller unit, as determined in the k-th instruction; This represents the average peak clipping power that the k-th instruction can generate; The peak shaving strategy matrix A is the highest room air temperature in each building; it is composed of the number of chiller units (n) in operation and the chilled water supply temperature setpoint. A two-dimensional matrix consisting of the feasible regions; S53, further filter the peak shaving strategy matrix A and the peak shaving effect matrix B based on the emergency peak shaving assessment requirements, user comfort constraints, and device physical constraints, eliminate elements in the matrix that do not meet the constraint requirements, obtain a peak shaving strategy feasible instruction set Ac, and a feasible instruction peak shaving effect matrix Bc corresponding thereto; S54, perform feasible instruction screening with the minimum "cost-benefit" ratio as the target, and obtain the best emergency demand response control instruction; calculate the "cost-benefit" ratio of each element in the peak shaving strategy feasible instruction set Ac based on the feasible instruction peak shaving effect matrix Bc , select the minimum control instruction as the best emergency demand response strategy of the regional cooling system, and the calculation method is as follows:

[0028] In the formula, is the peak shaving "cost-benefit" ratio of the kth control instruction; is the temperature rise value of the most unfavorable loop caused by the kth control instruction; is the peak shaving power achieved by the kth control instruction; S55, connect the upper computer through the regional cooling system automatic control system communication protocol, and issue the selected best demand response instruction to the register address of the corresponding point of the regional cooling system, so as to realize short-term accurate reduction of the power of the regional cooling system through the target device operating parameters; The peak shaving measures meet the following constraints: ① Peak shaving power constraint: the actual peak shaving power is in the range of , that is,

[0029] Among them: is the target peak shaving power issued by the power grid; a is the peak shaving deviation allowable coefficient; is the actual peak shaving power of the kth strategy.

[0030] ② User comfort constraint: the indoor air temperature of the most unfavorable loop does not exceed the user's highest acceptable temperature , that is:

[0031] Among them: is the user's highest acceptable temperature; is the predicted average dissatisfaction rate of the kth strategy; is the maximum dissatisfaction rate allowed by the user.

[0032] S6, when reaching the predetermined demand response start time, adjusting the operation state of each device in the regional cooling supply system based on the optimal emergency demand response strategy provided by the decision module, and monitoring the system operation state in real time; S7, when reaching the predetermined demand response end time, adjusting the operation state of each device in the regional cooling supply system to the normal operation mode through the automatic control system, and feeding back the hourly power consumption during the emergency demand response to the power grid operation scheduling platform as the basis for post-incentive calculation, completing a single emergency demand response.

[0033] To further verify the performance of the control method, experiments are conducted on a certain water storage cooling regional cooling system. The design cooling load of the air conditioner is 55000kW, the design supply and return water temperature of the chilled water is 5℃ / 12℃, the temperature of the chilled water provided by the storage device is 5℃, the maximum flow rate is 720kg / s, the design supply and return water temperature of the cooling water is 32℃ / 37℃, and the building set temperature range is [24, 26]℃. According to the requirements of the power grid department, the user needs to provide load reduction during peak electricity period (14:00-15:00), at which time the operation state of the regional cooling supply system can be obtained through the automatic control system: the number of open chillers is 8, the cooling tower, cooling water pump, primary chilled water pump and other auxiliary equipment are interlocked with the chiller to start and stop, the storage device operates at the maximum cooling power, the secondary variable frequency water pump is controlled by differential pressure variable frequency control, and the chilled water supply temperature set value of the chiller is 5℃, at which time the system can guarantee the thermal comfort of all users.

[0034] To verify the regional cooling system participating in the emergency demand response control method proposed in this paper, the actual peak shaving effect of the system is evaluated and compared with the conventional variable temperature peak shaving strategy and variable flow peak shaving strategy. In the above emergency peak shaving scenario, all theoretically feasible instructions under the three peak shaving strategies are obtained based on the operation state of the regional cooling system, and the demand response effect of each feasible instruction is obtained using the regional cooling system simulation model, as shown in Figure 3 Under the condition of guaranteeing the core regional thermal comfort (PPD≤10%), the variable temperature measure, variable flow measure, and the demand response control strategy proposed in this paper are obtained by screening the feasible instructions, and the best control instruction is: under the variable temperature strategy, the number of open chillers and their auxiliary equipment n is 8, the chilled water supply temperature set value is 9℃; under the variable flow strategy, the number of open chillers and their auxiliary equipment n is 5, the chilled water supply temperature set value is 5℃; under the emergency demand response strategy proposed in this paper, the number of open chillers and their auxiliary equipment n is 6, and the chilled water supply temperature set value The temperature was 7.5℃. Under the three peak shaving measures, the short-term maximum power reduction rates were 33.9%, 25.3%, and 35.1%, respectively; the peak shaving power deviation rates were 4%, 16%, and 1.2%, respectively; and the subsidy benefits were RMB 16,600, RMB 9,500, and RMB 17,200, respectively. The results show that the method proposed in this invention is significantly better than the traditional strategy in terms of peak shaving capability, control accuracy, and economic benefits. Example 2

[0035] like Figure 1 As shown, this embodiment provides a control system for a district cooling system to participate in grid emergency demand response, using the method in Embodiment 1 for control, including: The district cooling system-building thermal environment coupled simulation module is used to calculate the peak shaving effect of the district cooling system under different control strategies and to quantitatively evaluate user comfort; it includes chillers, cooling towers, cooling water pumps, primary chilled water pumps, secondary chilled water pumps, cold storage water pumps, cold storage equipment, and a building dynamic thermal balance model. The following is a scheme for constructing a coupled simulation model of a district cooling system and building thermal environment: The formula for calculating the power consumption of a district cooling system is as follows:

[0036] In the formula: The power consumption of the district cooling system is expressed in kW. This represents the start / stop status of the j-th host, with 1 for start and 0 for stop. The electrical power of the j-th chiller unit is kW; The power of the j-th cooling tower is kW; The power of the j-th chilled water primary circulation pump is kW; The power of the j-th cooling water circulating pump is kW; The power of the cold storage water pump is expressed in kW. The power of the chilled water circulating pump on the user side is kW.

[0037] The model expression for the chiller unit is:

[0038] In the formula: The power rating of the chiller unit is expressed in kW. The rated electrical power of the chiller unit is in kW; , , These are the cooling capacity coefficient, energy efficiency ratio coefficient, and energy efficiency ratio coefficient of the chiller unit as a function of operating temperature, respectively, and are all dimensionless constants. The setpoint for the chilled water supply temperature, in °C; The return water temperature is ℃; The chilled water supply temperature is ℃; These are the dimensionless model coefficients of the refrigeration unit, obtained based on fitting of operating data.

[0039] The cooling tower model expression is:

[0040] In the formula: The power of the cooling tower is expressed in kW. and These are the air mass flow rate and cooling water mass flow rate of the cooling tower, respectively, in kg / s; Cooling tower heat dissipation power, kW; The cooling water supply temperature, in K; Outdoor air wet-bulb temperature, K; is the specific heat capacity of cooling water, kJ / (kg·K); The coefficients are the dimensionless fitting model coefficients for the cooling tower, obtained based on measured data from the cooling tower.

[0041] The pump model expression is:

[0042] In the formula: The pump head is expressed in kPa. ω is the power of the water pump, in kW; ω is the ratio of the current speed of the water pump to its rated speed, dimensionless; V is the flow rate of the water pump, in L / s. All are dimensionless pump performance coefficients, obtained by fitting sample data or measured data.

[0043] The model expression for the cold storage device is:

[0044] In the formula: The power of the cold storage equipment is expressed in kW. The flow rate of the cold storage water pump is kg / s; The inlet water temperature of the cold water storage tank is ℃; The outlet temperature of the cold water storage tank is ℃.

[0045] The model expression for the transmission and distribution pipeline is:

[0046]

[0047] In the formula: The pressure drop between the inlet and outlet of the pipe section is expressed in kPa. The friction factor is kPa. For local resistance, kPa; The coefficient of friction is calculated based on Darcy's law. The local resistance coefficient is obtained based on measured data; D is the pipe diameter, in meters; L is the pipe length, in meters. The density of the fluid is expressed in kg / m³. t represents the velocity of the fluid, in m / s; Q represents the volumetric flow rate within the pipe section, in m³ / s; and t represents the time term. For pipe section The outlet temperature at that moment, in °C; Ta is the ambient temperature, in °C. Let be the delay time of the pipe segment, in seconds; for The inlet temperature of the pipe section is ℃; R is the thermal resistance of the pipe, K / W; C is the heat capacity of the pipe wall, J / K. These are the pipe's inner diameter, outer diameter, and insulation layer outer diameter, respectively, in meters (m). These are the thermal conductivity of the pipe wall and the thermal conductivity of the insulation layer, respectively. ; These are the heat transfer coefficients between the fluid inside the pipe and the inner wall of the pipe, and between the air and the outer surface of the pipe, respectively. m is the mass flow rate of the fluid in the pipe, kg / s.

[0048] The plate heat exchanger is constructed based on the efficiency-number of heat transfer units (ε-NTU) model to simulate the heat exchange process between the secondary circulating chilled water in the distribution network and the chilled water in each individual building in the heat exchange station. Terminal equipment such as fresh air handling units, air handling units and fan coil units are all modeled based on the heat exchange efficiency method to simulate the air handling process in building central air conditioning. The building heat balance model is established based on the heat transfer process of a typical room RC network. It is used to simulate the hourly air temperature, relative humidity, and indoor radiant temperature of the room under the influence of outdoor environment, air conditioning supply, and heat and moisture generation by indoor users and equipment. The user thermal comfort quantitative assessment model is based on the Predicted Mean Vote (PMV) - Predicted Mean Vote (PPD) model. It can quantitatively assess the thermal comfort of a room based on the hourly air temperature, humidity, and indoor radiant temperature output by the building thermal balance model, and obtain the PMV-PPD value.

[0049] Preferably, the district cooling system-building thermal environment coupled simulation model is constructed using the Modelica modeling language on platforms such as OpenModelica, Dymola, or MWORKS.Sysplorer, and the district cooling system-building thermal environment coupled simulation model is exported as an FMU file.

[0050] The emergency demand response decision module builds an optimization solution algorithm through Python, loads an FMU file based on an interface of a Python FunctionalMock-up Interface library, and realizes communication with an OPC UA server based on a python-opcua library; The communication module completes data interaction with a power grid operation and dispatch platform and a regional cooling system automatic control system based on a standard communication protocol.

[0051] The communication module includes: obtaining a peak clipping instruction, obtaining a regional cooling system operation state, issuing an emergency demand response control strategy of the regional cooling system, and uploading an emergency demand response process measured power consumption; The communication module connects a SCADA host computer through an OPC UA protocol, issues the control strategy to a register address of a corresponding point of the regional cooling system, and optimizes power consumption of the regional cooling system through target device operation parameters. Embodiment 3

[0052] The embodiment provides a computer storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the method in the embodiment 1. Embodiment 4

[0053] The embodiment provides an electronic device, including a memory and a processor: the memory is used for storing computer executable instructions, the processor is used for executing the computer executable instructions, and the computer executable instructions are executed by the processor to realize the steps of the method in the embodiment 1.

[0054] The embodiments of the application are described in detail above in combination with the drawings, but the application is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the application.

Claims

1. A control method for a district cooling system participating in emergency demand response of the power grid, characterized in that, The specific steps include the following: S1. Obtain emergency peak shaving dispatch instructions from the power grid through the power grid operation and dispatch platform; S2. Obtain the operating status parameters of the district cooling system through the district cooling system automatic control system; S3. The decision-making module determines all feasible control strategies based on the emergency peak shaving dispatch instructions of the power grid and the operating status parameters of the district cooling system, forming a theoretically feasible strategy matrix. S4. Call the district cooling system-building thermal environment coupled simulation model to simulate the parameters of the system under the theoretically feasible control strategy for emergency demand response of the entire district cooling system, and construct the peak shaving effect matrix corresponding to the theoretically feasible strategy matrix. S5. The decision-making module uses an optimization algorithm to filter the theoretically feasible strategy matrix based on the constraints and the peak-shaving effectiveness matrix to obtain the actual feasible strategy matrix. Calculate user benefits and select the best emergency response strategy with the goal of maximizing benefits. S6. When the predetermined demand response start time is reached, based on the best emergency demand response strategy provided by the decision module, the operating status of each device in the district cooling system is adjusted through the automatic control system, and the system operating status is monitored in real time. S7. When the predetermined demand response end time is reached, the operating status of each device in the district cooling system is adjusted to the normal operating mode through the automatic control system, and the hourly power consumption during the emergency demand response period is fed back to the power grid operation and dispatch platform as the basis for post-event incentive calculation to complete a single emergency demand response.

2. The control method for a district cooling system participating in emergency demand response of the power grid according to claim 1, characterized in that, In step S2, the operating status parameters of the district cooling system include: the start-up and shutdown status of the chiller unit and its auxiliary equipment, the cold release / storage power of the cold storage equipment, and the set value of the chilled water supply temperature of the chiller unit.

3. The control method for a district cooling system participating in emergency demand response of the power grid according to claim 1, characterized in that, In step S4, the system parameters under the theoretically feasible control strategy for emergency demand response of the cooling system include the system's hourly power consumption, chilled water flow rate, chilled water temperature, and the temperature and relative humidity of the air in the user's room.

4. The control method for a district cooling system participating in emergency demand response of the power grid according to claim 1, characterized in that, In step S4, the district cooling system-building thermal environment coupled simulation model is used to calculate the peak shaving effect of the district cooling system under different control strategies and to quantitatively evaluate user comfort.

5. The control method for a district cooling system participating in emergency demand response of the power grid according to claim 1, characterized in that, The optimization algorithm in step S5 is performed as follows: S51. Establish a feasible strategy matrix for the district cooling system; when participating in emergency demand response, select the chilled water supply temperature setpoint and the number of chiller units to be operated. Two variables serve as direct optimization parameters in response to the emergency peak shaving demand of the power grid, and the operating status of each water pump is determined based on the interlocking relationship between the equipment; S52. Based on the coupled simulation model of district cooling system and building thermal environment, peak shaving commands are obtained through simulation when outdoor meteorological parameters are known. Peak shaving effect that can be achieved This forms the peak-shaving effect matrix B, which corresponds to the peak-shaving strategy matrix A. Matrix B is a two-dimensional matrix that corresponds one-to-one with the elements in matrix A; where, The number of chiller units and their auxiliary equipment to be started, as determined in the k-th instruction; The setpoint for the chilled water supply temperature of the chiller unit, as determined in the k-th instruction; This represents the average peak clipping power that the k-th instruction can generate; The peak shaving strategy matrix A is the highest room air temperature in each building; it is composed of the number of chiller units (n) in operation and the chilled water supply temperature setpoint. A two-dimensional matrix consisting of the feasible regions; S53. Based on the emergency peak shaving assessment requirements, user comfort constraints, and equipment physical constraints, further filter the peak shaving strategy matrix A and peak shaving effect matrix B, remove elements in the matrix that do not meet the constraints, and obtain the feasible instruction set Ac for peak shaving strategies, and the corresponding feasible instruction peak shaving effect matrix Bc. S54. Select feasible instructions with the goal of minimizing the "cost-benefit" ratio to obtain the best emergency demand response control instructions; calculate the "cost-benefit" ratio of each element in the feasible instruction set Ac of the peak shaving strategy based on the peak shaving effect matrix Bc. ,choose The minimum control command is used as the optimal emergency demand response strategy for a district cooling system, and its calculation method is as follows: ； In the formula, The cost-benefit ratio for peak shaving of the k-th control command; This represents the room temperature increase in the most unfavorable loop caused by the k-th control command. The peak-shaving power achieved by the k-th control command; S55. Connect to the host computer through the communication protocol of the district cooling system automatic control system, and send the selected optimal demand response command to the register address of the corresponding point of the district cooling system, so as to achieve short-term and precise reduction of the power consumption of the district cooling system through the operating parameters of the target equipment.

6. The control method for a district cooling system participating in emergency demand response of the power grid according to claim 5, characterized in that, Peak shaving measures must meet the following constraints: ① Peak reduction power constraint: The actual peak reduction power is within Within the range, that is ； in: The target peak reduction power assigned by the power grid; 'a' is the allowable peak reduction deviation coefficient; This represents the actual peak-shaving power for the k-th strategy. ② User comfort constraints: Indoor air temperature in the most unfavorable loop Not exceeding the user's maximum acceptable temperature ,Right now: ； in: The maximum acceptable temperature for the user; Let be the average dissatisfaction rate predicted by the k-th strategy; The maximum dissatisfaction rate allowed by the user.

7. A control system for a district cooling system participating in emergency demand response of a power grid, wherein the control is performed using the method described in any one of claims 1-6, characterized in that, include: The district cooling system-building thermal environment coupled simulation module is used to calculate the peak shaving effect of the district cooling system under different control strategies and to quantitatively evaluate user comfort. This includes chillers, cooling towers, cooling water pumps, primary chilled water pumps, secondary chilled water pumps, cold storage water pumps, cold storage equipment, and building dynamic thermal balance models. The emergency demand response decision module uses Python to build an optimization solution algorithm, loads FMU files based on the Python FunctionalMock-up Interface library, and communicates with the OPC UA server based on the python-opcua library. The communication module, based on standard communication protocols, completes data interaction with the power grid operation and dispatch platform and the district cooling system's automatic control system.

8. The control system for a district cooling system participating in emergency demand response of the power grid according to claim 7, characterized in that, The communication module includes: acquiring peak shaving commands, acquiring the operating status of the district cooling system, issuing emergency demand response control strategies for the district cooling system, and uploading measured power consumption during the emergency demand response process. The communication module connects to the SCADA host computer via the OPC UA protocol and sends control strategies to the register addresses of the corresponding points in the district cooling system, so as to optimize the power consumption of the district cooling system based on the operating parameters of the target equipment.

9. A computer storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-6.

10. An electronic device, characterized in that, Includes a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method as described in any one of claims 1-6.

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