CN116068886B - Optimal control device of cooling water system of efficient refrigeration machine room - Google Patents
Optimal control device of cooling water system of efficient refrigeration machine room Download PDFInfo
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- 239000000498 cooling water Substances 0.000 title claims abstract description 50
- 238000005057 refrigeration Methods 0.000 title claims abstract description 16
- 238000001816 cooling Methods 0.000 claims abstract description 59
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 10
- 238000004891 communication Methods 0.000 claims abstract description 7
- 230000002787 reinforcement Effects 0.000 claims abstract description 6
- 230000009471 action Effects 0.000 claims description 15
- 239000003795 chemical substances by application Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 description 7
- 238000004378 air conditioning Methods 0.000 description 6
- 238000000034 method Methods 0.000 description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000017525 heat dissipation Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F3/00—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
- F24F3/06—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the arrangements for the supply of heat-exchange fluid for the subsequent treatment of primary air in the room units
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F28—HEAT EXCHANGE IN GENERAL
- F28F—DETAILS OF HEAT-EXCHANGE AND HEAT-TRANSFER APPARATUS, OF GENERAL APPLICATION
- F28F27/00—Control arrangements or safety devices specially adapted for heat-exchange or heat-transfer apparatus
- F28F27/003—Control arrangements or safety devices specially adapted for heat-exchange or heat-transfer apparatus specially adapted for cooling towers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Combustion & Propulsion (AREA)
- Chemical & Material Sciences (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
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- Feedback Control In General (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention belongs to the technical field of energy efficiency of a refrigeration machine room, and discloses an optimization control device of a cooling water system of a high-efficiency refrigeration machine room, which is characterized in that: the communication between the cloud server and the control unit of the cooling water system is realized through the edge computing gateway, an energy efficiency optimization control program is loaded in the cloud server, and the energy efficiency optimization control program dynamically adjusts the number of the cooling towers which are started and the frequency of fans and the frequency of the cooling water pump by adopting a deep reinforcement learning DQN algorithm so as to optimize the energy efficiency of the cooling side system.
Description
Technical Field
The invention relates to the technical field of energy efficiency of a refrigeration machine room, in particular to an optimal control device of a cooling water system of a high-efficiency refrigeration machine room.
Background
The equipment in the central air-conditioning cooling water system mainly comprises a cooling water pump and a cooling tower, and compared with the energy consumption of a central air-conditioning main machine, the total energy consumption of the cooling water pump and the cooling tower is lower, but the operation parameters of the central air-conditioning cooling water system have great influence on the energy efficiency of the central air-conditioning main machine, so that the energy-saving and optimized operation of the central air-conditioning cooling water system needs to comprehensively consider the total energy consumption of each equipment of the cold source system under specific operation conditions.
From the energy-saving angle of the cooling water pump, the cooling water flow can be adjusted in a variable-frequency operation mode of the cooling water pump, but the adjustment of the cooling water flow is not smaller and better, and the too low cooling water flow influences the heat dissipation effect of the central air conditioner host, so that the energy efficiency of the host is influenced, and the energy consumption of the central air conditioner is increased.
The specific gravity of the energy consumption of the cooling tower in the central air-conditioning cold source system is smaller, but the heat dissipation capacity of the cooling tower has larger influence on the energy consumption of the water chilling unit. The energy saving of the cooling tower is realized by frequency conversion of cooling tower fans or changing the number of cooling tower operation, the cooling effect of the cooling tower has a great relationship with the flow of cooling water and the temperature and humidity of outside air, and the difference of the temperature of the backwater of the cooling water can be caused by different cooling effects of the cooling tower under the same cooling water flow, so that the energy efficiency of a host is influenced, and therefore, the influence of the cooling tower operation mode on the energy consumption of a water chilling unit should be fully considered when the total time consumption of a cooling water system is analyzed.
At present, in an industrial refrigeration machine room, the frequency and the number of fans of a cooling tower are controlled through the outlet water temperature of the cooling tower, and the frequency of a cooling water pump is controlled by utilizing the temperature difference of the water supply and return of the cooling water pump, but the outlet water temperature of the cooling tower and the temperature difference set value of the water supply and return of the cooling water pump are difficult to determine and are usually a determined value, so that the energy efficiency of a cooling water system cannot be guaranteed to be optimal; meanwhile, the group control technology of the refrigerating machine room is biased to the realization of a communication function, the control efficiency of the refrigerating machine room is low, the energy waste is serious, the response is not timely, the intelligent control algorithm is not more involved, the architecture is simpler, the local computing capacity is weaker, the complex big data analysis and artificial intelligent algorithm are difficult to expand and deploy, and the complex computing capacity requirement and the real-time control are difficult to balance.
Disclosure of Invention
In order to solve the problems, the invention provides an optimal control device of a cooling water system of a high-efficiency refrigeration machine room.
The invention can be realized by the following technical scheme:
The utility model provides an optimal control device of high-efficient refrigeration computer lab cooling water system, realizes the communication between cloud ware and the control unit of cooling water system through the edge computing gateway, in the cloud ware is loaded with energy efficiency optimal control program, energy efficiency optimal control program adopts degree of depth reinforcement to learn DQN algorithm dynamic adjustment cooling tower to open number, fan frequency, cooling water pump frequency to realize the optimization to cooling side system energy efficiency.
Further, the energy efficiency optimization program takes the system cooling load CL system and the environmental wet bulb temperature value T wet as state information, takes the cooling tower number N tower, the fan frequency F fan and the frequency F cwps of the cooling pump as action values, takes the energy efficiency COP of the cooling side system as rewarding information, and outputs the action value cooling tower number N tower, the fan frequency F fan and the frequency F cwps of the cooling pump to change the output state of the cooling water system after Q network learning according to the collected state information system cooling load CL system, the environmental wet bulb temperature value T wet and the energy efficiency COP of the rewarding information cooling side system, so that the state information and rewarding information of the next step are acquired, the Q network is input again for learning, and the energy efficiency optimization of the cooling side system is realized.
Further, the energy efficiency optimization program comprises the following steps:
(1) After the intelligent agent interacts with the environment to obtain current state information CL system,Twet, inputting the current state information into a Q network, outputting an action value into the environment by using an epsilon-greedy strategy, and obtaining next state information CL system,Twet and rewarding information COP from the environment;
(2) Storing the current state information CL system,Twet, the action value N tower、Ffan、Fcwps, the next state information system,Twet and the rewarding information COP as samples into a memory playback unit of the intelligent agent;
(3) When the memory playback unit stores a certain volume of samples, extracting part of the samples in the memory playback unit, and inputting the current state information in the samples into the Q network to obtain the Q value of the current state information.
(4) Inputting the next state information in the sample into the Q network, and obtaining the estimated value of the current state information through calculation
(5) Gradient descent training is performed through a loss function formula, and weight parameters of the Q network are updated.
Further, the upper computer corresponding to the control unit is provided with a manual mode, an automatic mode and a cloud optimal control mode, and when the control unit is in the automatic mode and the cloud optimal control mode, the control unit can communicate with the cloud server.
The beneficial technical effects of the invention are as follows:
1) Solves the problem that the set value of the cooling water system of the high-efficiency refrigeration machine room is difficult to adjust or optimize, and effectively improves the energy efficiency of the refrigeration machine room
2) The problem of the refrigerating machine room group control system computational power weak is solved, the computational power of the cloud and the control power of the edge are innovatively utilized, so that the stability of the refrigerating machine room group control system is ensured, and the energy efficiency can be improved to the greatest extent.
3) The invention avoids the problem that the set value of the cooling water system of the high-efficiency refrigerating machine room is optimized, a large number of sensors are required to be installed on site for the cooling water system of the refrigerating machine room, the work of establishing an equipment model is easy to occur, and the model precision can not meet the requirement, thereby being beneficial to being widely applied to different cooling water system projects of the refrigerating machine room.
Drawings
FIG. 1 is a schematic diagram of an optimized control device for a cooling water system of a high-efficiency refrigeration machine room based on DQN (direct current) of the invention;
Fig. 2 is a flow chart of an energy efficiency optimization control process of the cooling water system optimization control device of the efficient refrigeration machine room based on DQN.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings and preferred embodiments.
As shown in fig. 1 and 2, the invention provides an optimized control device for a cooling water system of a high-efficiency refrigeration machine room, communication between a cloud server and a control unit of the cooling water system is realized through an edge computing gateway, an energy efficiency optimized control program is loaded in the cloud server, and the energy efficiency optimized control program dynamically adjusts the number of opening stages of a cooling tower and the frequency of a fan and the frequency of a cooling water pump by adopting a deep reinforcement learning DQN algorithm so as to realize optimization of energy efficiency of a cooling side system. The method comprises the following steps:
The computing capability of the edge computing gateway is expanded by utilizing a cloud platform through the architecture of 'cloud + edge + end', the obstruction of network delay to task timeliness is avoided, and the complex computing capability requirement of equipment optimization and the real-time requirement of control are met; and the edge computing gateway is used for establishing communication with a control unit such as a PLC of a cooling water system of the refrigerating machine room, so that the uploading and the issuing of data are controlled, and the flow chart in figure 1 is shown.
The edge computing gateway establishes communication with a PLC of the refrigerating machine room through a Modbus TCP protocol, and a manual mode, an automatic mode and a cloud optimal control mode are designed in an upper computer corresponding to the PLC; the manual mode and the automatic mode in the PLC are conventional design of automatic control, the PLC cloud optimal control mode is different from a common refrigerating machine room automatic control system, and when the PLC is in the cloud optimal control mode, data uploading and control command issuing between the edge computing gateway and the PLC can be realized; and when the cloud control mode is abnormal, the cloud control mode can be switched to the automatic mode at any time.
The upper computer can switch the PLC control authority into manual control, automatic control and cloud optimal control, the cloud platform can switch the PLC control authority into automatic control and cloud optimal control, and when the local is in a manual control mode, the cloud platform cannot acquire the control authority of the PLC; the upper computer and the cloud platform can synchronously control the authority state in real time, the upper computer can switch the control authority, the cloud platform can display the changed control authority in real time, the cloud platform can switch the control authority, and the upper computer can display the changed control authority in real time.
Aiming at the optimal cooling tower starting number and fan frequency of the cooling water system of the refrigerating machine room, the frequency value of the cooling water pump is difficult to set, the cooling tower starting number and fan frequency are dynamically adjusted through deep reinforcement learning DQN, the cooling water pump frequency optimizes the cooling side system energy efficiency, the optimization process is realized through model algorithm micro-service deployed on a cloud platform, and an agent of the reinforcement learning algorithm DQN continuously improves algorithm precision through interactive learning with a digital twin simulation environment of a physical refrigerating machine room until the accuracy requirement of engineering-level intelligent energy efficiency optimization application is met. For the intelligent body capable of meeting engineering application, the energy efficiency optimization control and learning process for the cooling tower starting number and fan frequency and the cooling water pump frequency on line is as follows:
the energy efficiency optimization program takes a system cooling load CL system, an environment wet bulb temperature value T wet as state information, takes a cooling tower number N tower, a fan frequency F fan and a cooling pump frequency F cwps as action values, takes an energy efficiency COP of a cooling side system as rewarding information, and outputs the action values of the cooling tower number N tower, the fan frequency F fan and the cooling pump frequency F cwps after Q network learning according to the collected state information system cooling load CL system, the environment wet bulb temperature value T wet and the energy efficiency COP of the rewarding information cooling side system, so as to change the output state of a cooling water system, acquire the next state information and rewarding information, input a Q network again for learning, and continuously learn in sequence, thereby realizing the optimization of the energy efficiency of the cooling side system.
The method specifically comprises the following steps:
1 rewards information (forward)
The optimization target is the COP of the energy efficiency of the cooling side system, and the calculation formula is as follows:
CL system is the system cooling load, P chillers is the total power of all chillers, and P cwps is the total power of all cooling water pumps.
2 State information (state)
System cold load (CL system), ambient wet bulb temperature value (T wet)
3 Action value (action)
The number of cooling towers (N tower), the fan frequency (F fan) and the frequency of the cooling pump (F cwps).
4 Agent
The intelligent agent is a control algorithm capable of outputting the action value cooling tower number N tower, the fan frequency F fan and the cooling pump frequency F cwps.
5 Optimization procedure
(1) After the intelligent agent interacts with the environment to obtain current state information CL system,Twet, inputting the current state information into a Q network, outputting an action value into the environment by using an epsilon-greedy strategy, and obtaining next state information CL system,Twet and rewarding information COP from the environment;
(2) Storing the current state information CL system,Twet, the action value N tower、Ffan、Fcwps, the next state information CL system,Twet, the bonus information COP as samples into a memory playback unit of the agent;
(3) When the memory playback unit stores a certain volume of samples, extracting part of the samples in the memory playback unit, and inputting the current state information in the samples into the Q network to obtain the Q value of the current state information.
(4) Inputting the next state information in the sample into the Q network, and obtaining the estimated value of the current state information through calculation
(5) By the Q value and the estimated valueAnd performing gradient descent training on the calculated loss function, and updating to obtain the weight parameter of the Q network.
It will be appreciated by those skilled in the art that these are merely illustrative and that many changes and modifications may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims.
Claims (2)
1. An optimization control device of a cooling water system of a high-efficiency refrigeration machine room is characterized in that: communication between a cloud server and a control unit of a cooling water system is realized through an edge computing gateway, an energy efficiency optimization control program is loaded in the cloud server, and the energy efficiency optimization control program dynamically adjusts the number of cooling towers which are started and the frequency of fans and the frequency of a cooling water pump by adopting a deep reinforcement learning DQN algorithm so as to optimize the energy efficiency of a cooling side system;
The energy efficiency optimization program takes a system cooling load CL system, an environment wet bulb temperature value T wet as state information, takes a cooling tower number N tower, a fan frequency F fan and a cooling pump frequency F cwps as action values, takes an energy efficiency COP of a cooling side system as rewarding information, and outputs the action values of the cooling tower number N tower, the fan frequency F fan and the cooling pump frequency F cwps to change the output state of a cooling water system after Q network learning according to the acquired state information system cooling load CL system, the environment wet bulb temperature value T wet and rewarding information, so that the next state information and rewarding information are acquired, and the Q network is input again for learning, and the continuous learning is performed in sequence, so that the energy efficiency of the cooling side system is optimized;
the energy efficiency optimization program comprises the following steps:
(1) After the intelligent agent interacts with the environment to obtain current state information CL system、Twet, inputting the current state information into a Q network, outputting an action value into the environment by using an epsilon-greedy strategy, and obtaining next state information CL system、Twet and rewarding information from the environment;
(2) Storing the current state information CL system、Twet, the action value N tower、Ffan、Fcwps, the next state information CL system、Twet and the rewarding information as samples into a memory playback unit of the intelligent agent;
(3) When the memory playback unit stores a certain volume of samples, extracting part of the samples in the memory playback unit, and inputting the current state information in the samples into a Q network to obtain the Q value of the current state information;
(4) Inputting the next state information in the sample into the Q network, and obtaining the estimated value of the current state information through calculation
(5) Gradient descent training is performed through a loss function formula, and weight parameters of the Q network are updated.
2. The optimal control device for a cooling water system of a high-efficiency refrigeration machine room according to claim 1, wherein: the upper computer corresponding to the control unit is provided with a manual mode, an automatic mode and a cloud optimal control mode, and when the control unit is in the automatic mode and the cloud optimal control mode, the control unit can communicate with the cloud server.
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| CN114279042B (en) * | 2021-12-27 | 2024-01-26 | 苏州科技大学 | Central air conditioner control method based on multi-agent deep reinforcement learning |
| CN115017817A (en) * | 2022-06-17 | 2022-09-06 | 上海碳索能源服务股份有限公司 | Method, system, terminal and medium for optimizing energy efficiency of refrigeration machine room |
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