Optimization control method for ice storage air-conditioning system based on dynamic programming algorithm

Abstract

The invention discloses an ice storage air-conditioning system optimization control method based on a dynamic programming algorithm, which comprises the following steps: acquiring operation data of an ice storage air-conditioning system in real time, and constructing a directed graph model, directed graph nodes and interconnection weights among the nodes; transferring and converting a cold storage and release operation mode of the ice storage air-conditioning system in a preset optimization time domain into a shortest path for solving the directed graph model; based on a directed graph model, solving a shortest path by using a dynamic programming algorithm; and on the basis of the shortest path, an ice storage air conditioning system control strategy is generated and issued to a system execution layer, and the control strategy comprises but is not limited to a switching decision of a cold storage and release operation mode in each time period of the optimization time domain and an equipment start-stop state. The cold storage and release operation mode is controlled through the dynamic planning method, efficient matching of cold storage air conditioner loads is achieved, the system energy efficiency is improved, the operation cost is reduced, and operation and maintenance operation is simplified.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an optimization control method for an ice storage air conditioning system based on a dynamic programming algorithm. Background Art

[0002] Ice storage air conditioning system is an advanced energy utilization technology. It uses the low electricity consumption period to store cold energy and releases it during the peak electricity consumption period. It is widely used in large commercial buildings, industrial facilities and public places. However, the operation mode of this type of system directly affects its energy efficiency and economy.

[0003] At present, most ice storage air conditioning systems still rely on experience to set operating strategies or adopt fixed storage and discharge modes, which makes it difficult to dynamically adjust according to real-time electricity price fluctuations, building cooling load changes and environmental conditions. These inflexible control methods easily lead to reduced operating efficiency and cannot fully tap the energy-saving potential of ice storage systems.

[0004] In addition, existing methods often ignore the collaborative optimization problem between ice storage air-conditioning equipment and other subsystems of the air-conditioning system, resulting in the utilization rate of the ice storage device and the overall system efficiency failing to reach the optimal level.

[0005] Therefore, there is an urgent need to develop a new control strategy for ice storage air-conditioning systems. Through precise optimization algorithms, the storage and release operation modes of the ice storage systems can be dynamically adjusted to improve the overall operating efficiency of the system and reduce operating costs. Summary of the invention

[0006] One of the purposes of the embodiments of the present invention is to provide an optimization method for an ice storage air conditioning system based on a dynamic programming algorithm, aiming to solve the problems of low energy efficiency, complex manual adjustment and high system operation and maintenance costs of traditional operation strategies. By controlling the storage and discharge operation mode by a dynamic programming method, efficient matching of cold storage and air conditioning load is achieved to improve system energy efficiency, reduce operating costs and simplify operation and maintenance operations, while ensuring a comfortable indoor environment, and minimizing energy consumption and electricity costs.

[0007] In order to solve the above technical problems, in a first aspect, an embodiment of the present invention provides an optimization control method of an ice storage air conditioning system based on a dynamic programming algorithm, the method comprising: Real-time collection of operating data of the ice storage air conditioning system, including but not limited to the current remaining ice volume, outdoor wet bulb temperature, current system load, electricity price curve and historical operating data; Constructing a directed graph model, the directed graph model including directed graph nodes and interconnection weights between the nodes, and converting the transfer of the ice storage and release operation mode of the ice storage air conditioning system within a preset optimization time domain into solving the shortest path of the directed graph model; Based on the directed graph model, solving the shortest path using a dynamic programming algorithm; Based on the shortest path, a control strategy for the ice storage air conditioning system is generated and sent to the system execution layer. The control strategy includes but is not limited to the switching decision of the storage and release cooling operation mode in each time period and the start and stop status of the equipment.

[0008] Furthermore, the method further comprises: The operating data is used to establish a state space of the ice storage air conditioning system to feed back system state information, wherein the state information includes but is not limited to the current remaining ice volume, outdoor wet bulb temperature, current load, energy consumption feedback value and electricity price.

[0009] Preferably, solving the shortest path based on the directed graph model using a dynamic programming algorithm specifically includes: Use the current state information of the system as the initial node; The preset optimization time domain is divided into several time periods, each time period is used as a stage in the dynamic programming algorithm calculation, and all possible system states in each time period are used as nodes in the path; Use dynamic programming algorithm to calculate the cumulative cost of each path that will occur at each moment; Starting from the terminal state, the dynamic programming algorithm is used to perform reverse recursive calculations to select the path with the lowest cost from multiple paths that will occur, and finally output the hourly optimal operating mode within the preset optimization time domain.

[0010] Preferably, the step of calculating the cumulative cost of each path to be taken place hour by hour by using a dynamic programming algorithm specifically includes: The dynamic programming algorithm is used to calculate the multi-step cumulative cost J of each path to be taken. The multi-step cumulative cost J is obtained by summing up the single-step cost g of each stage. The specific calculation formula is as follows:

[0011] Where k is the stage index, x k Indicates the system status, u k represents the decision variable, u k From the k stage, the given dependence on x k The decision set U k Select from; N represents the total number of stages and is equal to the total number of optimizations; The single-step cost g is calculated by the following formula:

[0012] Among them, g k,p represents the electricity price cost, which is obtained by multiplying the electricity price in stage k and the system energy consumption. k,com Represents the comfort cost, which is determined by the chiller chilled water outlet temperature or the building terminal temperature.

[0013] Furthermore, the method further comprises: The operating status of the system is monitored in real time, the state space is dynamically updated, and the operating cost of the system is recalculated. If the operating cost is higher than the preset cost threshold, the dynamic programming algorithm is immediately re-triggered to generate a new optimal operating mode.

[0014] Furthermore, the method further comprises: Set the hyperparameters of the dynamic programming algorithm, including: The current remaining ice volume, outdoor wet-bulb temperature and system load in the state space are taken as the main state variables; Adjust the weight parameters of the cost function according to the actual electricity price fluctuation curve; The constraint conditions of the control strategy are set, wherein the constraint conditions of the control strategy include: the system control time interval shall not be less than 60 minutes / time and the threshold value of the remaining ice volume is set according to the total cold storage capacity and demand of the system.

[0015] Preferably, the mathematical description of the dynamic programming algorithm is as follows: ; Among them, k is the time period index, x k Indicates the system status, u k represents the decision variable, u k From the k stage, the given dependence on x k The decision set U k Select from; f k It means (x k ,u k ), describing the mechanism of updating the state from stage k to stage k + 1; N represents the total number of time periods, which is equal to the total number of optimization times; The cost function accumulated over the stages is denoted as g k (x k ,u k ), g is the single-step cost, and J is the multi-step cumulative cost: ; Through the dynamic programming algorithm, all sequences {u0,…,u N-1} to minimize the cost function to obtain the optimal value: ; Among them, “*” represents the optimal solution of all states and decisions in the kth stage; g k Represents state x k Adopt a strategy k The single-step cost of J k *It represents the multi-step cumulative minimum cost from stage k to stage m. Before entering the next stage from stage k, all states x in stage k are calculated. k,i The multi-step cumulative minimum cost; Get function , the optimal control sequence starting from the initial state x0 is constructed by the following formula and the corresponding state trajectory : ; Among them, u i * represents the optimal control variable in the i-th stage, arg min means to find the control strategy u that minimizes the following expression i Value; g itoi+1 * Represents the cost from the current stage i to the next stage i+1, and the current control strategy u i Value and system status x i Related; J i+1 * (x i+1 ) starts from stage i+1, based on state x i+1 to the optimal cost function in the subsequent final stage.

[0016] In a second aspect, an embodiment of the present invention provides an optimization control system for an ice storage air conditioning system based on a dynamic programming algorithm, the system comprising: A data acquisition module, used to collect real-time operating data of the ice storage air conditioning system, the operating data including but not limited to the current remaining ice volume, outdoor wet bulb temperature, current system load, electricity price curve and historical operating data; A model building module is used to build a directed graph model, the directed graph model includes directed graph nodes and interconnection weights between nodes, and converts the transfer of the ice storage and release operation mode of the ice storage air conditioning system within a preset optimization time domain into a shortest path for solving the directed graph model; A calculation module, used for solving the shortest path by using a dynamic programming algorithm based on the directed graph model; The control module is used to generate an ice storage air conditioning system control strategy based on the shortest path and send it to the system execution layer. The control strategy includes but is not limited to the switching decision of the storage and release cooling operation mode in each time period and the start and stop status of the equipment.

[0017] In a third aspect, an embodiment of the present invention further provides a computing device, which includes a processor or a calculator and a memory, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor or calculator is configured to call the program instructions to execute the method as described above.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor or a computer, the processor or the computer executes the method as described above.

[0019] Compared with the prior art, the ice storage air conditioning system optimization control method based on the dynamic programming algorithm provided in the embodiment of the present invention has at least the following beneficial effects: The embodiment of the present invention is an optimization method for an ice-storage air-conditioning system based on a dynamic programming algorithm. The ice-storage system's storage and release modes are optimized through a dynamic programming algorithm, a state transition model is constructed, and factors such as electricity price fluctuations, building cooling load demand, and equipment operation restrictions are comprehensively considered to achieve global optimization of the system, thereby improving system energy efficiency, reducing operating costs, and simplifying operation and maintenance operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0021] Figure 1 This is a flow chart of an optimization control method for an ice storage air conditioning system based on a dynamic programming algorithm according to an embodiment of the present invention; Figure 2 A schematic diagram of a directed graph model according to an embodiment of the present invention; Figure 3 The figure is a schematic diagram of the computing device structure of the ice storage air conditioning system optimization control method based on the dynamic programming algorithm according to the embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0023] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0024] The following mainly takes some specific embodiments as examples to describe in detail the implementation of the technical solution of the present invention.

[0025] like Figure 1 As shown, in order to achieve the purpose of the present invention, an embodiment of the present invention provides an optimization control method for an ice storage air conditioning system based on a dynamic programming algorithm, the method comprising: S1. Real-time collection of operating data of the ice storage air conditioning system, including but not limited to the current remaining ice volume, outdoor wet-bulb temperature, current system load, electricity price curve and historical operating data; S2, constructing a directed graph model, the directed graph model including directed graph nodes and interconnection weights between the nodes, converting the transfer of the ice storage and release operation mode of the ice storage air conditioning system within a preset optimization time domain into solving the shortest path of the directed graph model; S3. Based on the directed graph model, solving the shortest path using a dynamic programming algorithm; S4. Based on the shortest path, generate an ice storage air conditioning system control strategy and send it to the system execution layer. The control strategy includes but is not limited to the switching decision of the storage and release cooling operation mode in each time period and the start and stop status of the equipment.

[0026] Wherein, in step S1, the operation data of the ice storage air conditioning system is collected in real time, and the operation data includes the operation parameters and environmental parameters of the system, and the operation data is used as the input of the dynamic programming model; That is, by installing sensors and other data acquisition modules, the operating parameters of the air-conditioning system can be collected in real time, including the storage capacity and release capacity of the ice storage device, as well as environmental parameters such as building cooling load, electricity price fluctuation curve, ambient temperature and humidity, etc.

[0027] These data are cleaned and preprocessed by edge computing devices to provide accurate input for the dynamic programming model.

[0028] In step S2, the state space and directed graph model of the ice storage air conditioning system for cold storage and release operation are established as follows: The operating data is used to establish a state space of the ice storage air-conditioning system to feedback the system's state information, wherein the state information includes but is not limited to the current remaining ice amount, outdoor wet-bulb temperature, current load, energy consumption feedback value and electricity price. That is, according to the operating characteristics of the ice storage air-conditioning system, a state space is established, including parameters such as the cold storage state, cold load demand, and electricity price range in different time periods.

[0029] You can use network modeling tools (such as Python's NetworkX library) to build the optimization problem into a directed graph model, such as Figure 2In this method, the optimization problem of the ice storage air conditioning system's cooling operation mode in the next 12 hours is transformed into the shortest path problem.

[0030] Building a directed graph model specifically includes the following steps: 1) Define state variables and establish directed graph nodes: Each node in the directed graph represents a state of the dynamic programming algorithm. The state consists of three parts: Current remaining ice volume: reflects the current cold storage capacity of the system; Outdoor wet bulb temperature: a key environmental variable that affects air conditioning load and energy consumption; Current system load: the cooling demand during the current period.

[0031] Directed edges between nodes represent the process of the system transitioning from one state to another.

[0032] 2) Define the interconnection weight between nodes: Define a weight for each directed edge. The weight is the operating cost (COST) corresponding to the state transition. The calculation method is the product of the system energy consumption and the electricity price at the current moment. The cost weight calculated by this calculation method can reflect the comprehensive impact of energy consumption and electricity price on the operating mode.

[0033] 3) Add constraints: ensure that the amount of remaining ice in the ice storage system at the end of operation (the end of the optimization cycle) is less than the preset threshold to ensure the feasibility of the optimization scheme; Ensure that the system's storage and cooling capacity in each period meets the current load demand while complying with the physical limitations of the system (such as the amount of ice cannot be negative).

[0034] Finally, a directed graph model with multiple layers of nodes is constructed through the above process, where each layer of nodes corresponds to a time period within the next 12 hours. Each path represents a possible operation mode from the initial state to the terminal state, including: Action set, i.e. the power range and duration of cooling storage or cooling release.

[0035] Construct an objective function with the minimization of electricity cost as the optimization goal, while constraining system operating conditions, such as maximum cooling capacity, equipment load limit, etc.

[0036] Design the optimal cooling storage and release strategy based on building cooling load forecast, electricity price curve and system operation constraints: That is, by modeling parameters such as load changes, cold storage status, and electricity price fluctuations during the system operation cycle, the global optimal decision path is dynamically generated to guide the system to adjust the cold storage and release mode in real time.

[0037] Preferably, solving the shortest path based on the directed graph model using a dynamic programming algorithm specifically includes: Use the current state information of the system as the initial node; The preset optimization time domain is divided into several time periods, each time period is used as a stage in the dynamic programming algorithm calculation, and all possible system states in each time period are used as nodes in the path; Use dynamic programming algorithm to calculate the cumulative cost of each path that will occur at each moment; Starting from the terminal state, the dynamic programming algorithm is used to perform reverse recursive calculations to select the path with the lowest cost from multiple paths that will occur, and finally output the hourly optimal operating mode within the preset optimization time domain.

[0038] For example, the next 12 hours are divided into several time periods (e.g., in hours), and the possible state changes in each time period are used as nodes in the path. The transfer of nodes is constrained by factors such as energy consumption, electricity prices, load changes, and cold storage capacity.

[0039] Among them, the dynamic programming algorithm is used to gradually solve the optimal decision-making plan for each time period through recursive calculation, which specifically includes the following process: Based on the building cooling load forecast and electricity price curve, dynamically calculate the optimal cooling storage and release strategy for each period; Recursively optimize decisions in stages to ensure that each step of the decision is in line with the overall optimal principle of the system; Output the final optimized operation mode, including cooling storage time, cooling release time and power allocation in each time period.

[0040] The dynamic programming algorithm is an optimization algorithm commonly used to solve multi-stage decision-making problems. It can decompose complex problems into several sub-problems, gradually solve them and finally obtain the global optimal solution. In the optimization problem of multi-constraint and multi-state ice storage air-conditioning system, the dynamic programming algorithm can effectively balance multiple operating objectives, such as minimizing electricity costs and maximizing system energy efficiency. By applying the dynamic programming algorithm to the optimization of the operation mode of the ice storage air-conditioning system, it can achieve real-time response to changes in building cooling load and electricity prices, dynamically adjust the system's storage and release strategies, significantly improve energy saving effects, and can be applied to a variety of load scenarios: During high-load scenarios during the day: cooling mode is used first to reduce peak energy consumption; Nighttime low-load scenario: cold storage mode is used first to reduce operating costs; Scenarios where electricity prices fluctuate significantly: Dynamically adjust the storage and cooling strategy to adapt to changes in peak and valley electricity prices.

[0041] Preferably, the step of calculating the cumulative cost of each path to be taken place hour by hour by using a dynamic programming algorithm specifically includes: The dynamic programming algorithm is used to calculate the multi-step cumulative cost J of each path to be taken. The multi-step cumulative cost J is obtained by summing up the single-step cost g of each stage. The specific calculation formula is as follows: ; Where k is the stage index, x k Indicates the system status, u k represents the decision variable, u k From the k stage, the given dependence on x k The decision set U k Select from; N represents the total number of stages and is equal to the total number of optimizations; The single-step cost g is calculated by the following formula: ; Among them, g k,p represents the electricity price cost, which is obtained by multiplying the electricity price in stage k and the system energy consumption. k,com Represents the comfort cost, which is determined by the chiller chilled water outlet temperature or the building terminal temperature.

[0042] Furthermore, the method further comprises: The operating status of the system is monitored in real time, the state space is dynamically updated, and the operating cost of the system is recalculated. If the operating cost is higher than the preset cost threshold, the dynamic programming algorithm is immediately re-triggered to generate a new optimal operating mode.

[0043] After the system executes the control strategy, it monitors the operating status in real time, dynamically updates the remaining ice volume, outdoor wet-bulb temperature and load data, and re-evaluates the operating cost. If the external conditions change significantly (such as a sharp increase in load or a significant increase in wet-bulb temperature), the dynamic programming algorithm is immediately re-triggered to generate a new optimal operating mode.

[0044] Furthermore, the method further comprises: Set the hyperparameters of the dynamic programming algorithm, including: The current remaining ice volume, outdoor wet-bulb temperature and system load in the state space are taken as the main state variables; Adjust the weight parameters of the cost function according to the actual electricity price fluctuation curve; The constraint conditions of the control strategy are set, wherein the constraint conditions of the control strategy include: the system control time interval shall not be less than 60 minutes / time and the threshold value of the remaining ice volume is set according to the total cold storage capacity and demand of the system.

[0045] For example: the system control time interval shall not be less than 60 minutes / time; the threshold value of the remaining ice volume is set according to the total cold storage capacity and demand of the system, and the general value range is 0%-15%.

[0046] The mathematical description of the dynamic programming algorithm is as follows: ; Among them, k is the time period index, x k Indicates the system status, u k represents the decision variable, u k From the k stage, the given dependence on x k The decision set U k Select from; f k It means (x k ,u k ), describing the mechanism of updating the state from stage k to stage k + 1; N represents the total number of time periods, which is equal to the total number of optimization times; Only state x k For example, if the k-th stage is in pure refrigeration mode, there is no frequency setting for the glycol pump in the decision set. If it is in ice melting mode, there is a frequency setting for the glycol pump. The cost function accumulated over the stages is denoted as g k (x k ,u k ), g is the single-step cost, and J is the multi-step cumulative cost: ; Through the dynamic programming algorithm, all sequences {u0,…,u N-1} to minimize the cost function to obtain the optimal value: ; In the formula, “*” represents the optimal solution of all states and decisions in the kth stage; g k Represents state x k Adopt a strategy k The single-step cost of J k * It represents the multi-step cumulative minimum cost from stage k to stage m. Before entering the next stage from stage k, all states x in stage k are calculated. k,i The multi-step cumulative minimum cost; k is the number of stages, g N (x N ) is k=N (i.e., the cost of the last optimization), indicating that the cumulative cost is calculated by adding up the single-step costs of each stage, and starts from the nth stage and accumulates in reverse order to the kth stage.

[0047] Get function , the optimal control sequence starting from the initial state x0 is constructed by the following formula and the corresponding state trajectory : ; Among them, u i * represents the optimal control variable in the i-th stage, arg min means to find the control strategy u that minimizes the following expression i Value; g itoi+1 * Represents the cost from the current stage i to the next stage i+1, and the current control strategy u i Value and system status x i Related; J i+1 * (x i+1 ) starts from stage i+1, based on state x i+1 to the optimal cost function in the subsequent final stage.

[0048] The state is the combination of the current remaining ice volume, outdoor wet bulb temperature, and current load. The first subscript of X indicates the stage, and the second subscript is the number of states. Assuming that there are k states in each stage, X 1,1 Indicates the first state of phase 1. In the embodiment of the present invention, the control time is set to 12 hours, the time step is 1 hour, and N=12.

[0049] In step S4, based on the shortest path, an ice storage air conditioning system control strategy is generated and sent to the system execution layer to dynamically adjust the operation mode to achieve the goals of minimizing electricity costs and maximizing system operation efficiency.

[0050] The operating status of the ice storage system is controlled in real time according to the optimization strategy. When environmental changes occur during actual operation (such as electricity price fluctuations, cooling load forecast deviations, etc.), the dynamic programming algorithm will recalculate the optimization strategy based on the latest data and adjust the operating mode.

[0051] Energy efficiency evaluation and feedback optimization: evaluate the energy efficiency and economy of the actual operation of the ice storage system, and further optimize the parameter settings of the dynamic programming model by collecting historical data, such as the granularity of state space division and the accuracy of cooling load prediction.

[0052] The present invention utilizes a dynamic programming algorithm to optimize the storage and discharge operation mode of the ice-storage air-conditioning system. Compared with existing fixed strategies, it has the following significant advantages: by dynamically adjusting the operation strategy, the electricity cost is significantly reduced, while the equipment operation efficiency is improved; the coordinated optimization of the ice-storage device and other subsystems of the air-conditioning system is achieved, and the overall energy-saving potential of the system is fully utilized; dynamic optimization is performed based on real-time data, so that the system can adapt to the complex changes in the building's cooling load and electricity prices; by flexibly adjusting the algorithm parameters and constraints, it can be widely applied to ice-storage air-conditioning systems of different sizes and types.

[0053] In the actual application of the embodiment of the present invention in the commercial complex, a mathematical model of the system is constructed, parameters such as ice storage, air conditioning load and electricity price are included in the state variables, and an optimization function with the goal of minimizing electricity cost is set. The dynamic programming algorithm is used, combined with the time discretization and state space discretization methods, to solve the optimization problem and generate the global optimal operation strategy of the system, that is, by dynamically adjusting the operation mode of the system (ice melting, semi-ice melting, chiller), efficient matching of cold storage and load demand is achieved, the system energy efficiency ratio (COP) is significantly improved, the electricity demand during peak electricity price periods is reduced, and a significant reduction in the comprehensive electricity cost is achieved.

[0054] The present invention has good applicability and can be extended to a variety of energy storage optimization scenarios, providing an intelligent solution for building energy conservation, being able to adapt to dynamic environmental changes and significantly reducing operating costs.

[0055] Take an air conditioning system with 300 RT (refrigeration tons) ice storage capacity as an example. The system includes: 1 centrifugal chiller, 2 chilled water pumps, 2 cooling water pumps, 1 cooling tower and 1 ice storage device. The main parameters of the system equipment are as follows: Chiller parameters: cooling water supply and return temperature is 37 / 32℃, chilled water supply and return temperature is 5 / 12℃, rated COP is 6.8, rated cooling capacity is 1050kW, and rated power is 154kW.

[0056] Cooling tower parameters: rated inlet and outlet temperature is 37 / 32℃, rated air volume is 280000 m 3 / h, fan rated frequency 50Hz, fan rated power 11kW.

[0057] Cooling water pump parameters: rated water flow 300m 3 / h, rated frequency 50 Hz, rated power 22 kW, rated head 25m.

[0058] Chilled water pump parameters: Rated water flow 200m 3 / h, rated frequency 50 Hz, rated power 18.5 kW, rated head 28m.

[0059] Ice storage device parameters: maximum ice storage capacity 300RT, cold storage efficiency 0.92, cooling efficiency 0.95.

[0060] First, system modeling is performed: mathematical modeling of the ice storage air conditioning system is performed, and a state space model containing the following variables is constructed: System state variables: state is defined as the current remaining ice volume ( , Outdoor wet bulb temperature ( and system load ( , described by the following symbols: ; Control decision variables: The decision variable is the system operation mode of the ice storage air conditioning system (melting mode, semi-melting mode, cooling mode), which is described by the following symbols: ; From stage k to stage k+1, the state transition must satisfy the constraints: First, the remaining ice amount in stage k+1 should be less than or equal to the remaining ice amount in stage k (if stage k is in cooling mode, they are equal, otherwise they are less than).

[0061] Secondly, the final stage (X 12 , that is, at the end of this optimization cycle) the remaining ice volume should be less than the set threshold.

[0062] ; Among them, formula (1) indicates that the remaining ice amount in the k+1th stage should be less than the remaining ice amount in the kth stage (at this time, the operation strategy corresponding to the kth stage is the ice melting mode or the semi-ice melting mode) or the remaining ice amount in the k+1th stage is equal to the remaining ice amount in the kth stage (at this time, the operation strategy corresponding to the kth stage is the pure refrigeration mode); formula (2) indicates that the remaining ice amount in the 12th stage (i.e., at the end of this optimization cycle) should be less than or equal to the set value.

[0063] The algorithm uses mathematical principles to find the optimal state trajectory to minimize the cost function: ; ; ; Among them, Power k represents the system energy consumption at stage k, Price k represents the electricity price at stage k, and multiplying them together gives the electricity price cost g k,p . g k,com It represents the comfort cost. In this case, if the chilled water outlet temperature is lower than the set value, this item is 0. Otherwise, it is calculated as the square of the chilled water outlet temperature exceeding the set value.

[0064] By applying the method of the embodiment of the present invention to a 300RT ice storage air-conditioning system in a commercial complex, the operating results show that the system energy efficiency ratio (COP) is improved by about 12%; the utilization rate of the peak-valley electricity price difference is increased by 20%, and the comprehensive electricity cost is reduced by about 15%.

[0065] The embodiment of the present invention provides an intelligent optimization operation solution for high-energy-consuming buildings by combining a dynamic programming algorithm with an ice storage air conditioning system, effectively improving the energy efficiency and economy of the system while significantly reducing the complexity of manual operation. It is not only applicable to ice storage air conditioning systems, but can also be extended to other types of energy storage systems, such as water storage systems or heat storage systems, to achieve energy-saving optimization goals in multiple scenarios.

[0066] In a third aspect, an embodiment of the present invention further provides a computing device, which includes a processor or a calculator and a memory, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor or calculator is configured to call the program instructions to execute the method as described above.

[0067] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor or a computer, the processor or the computer executes the method as described above.

[0068] like Figure 3 As shown, a computing device provided in an embodiment of the present application, the computing device 1000 includes a processor or a calculator (not shown in the figure) 1001 and a memory 1002, and the processor or the calculator 1001 and the memory 1002 can be interconnected through a communication bus 1003. The communication bus 1003 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1003 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the memory 1002 is used to store a computer program, and the computer program includes program instructions. The processor 1001 is configured to call the program instructions, and the above program includes a method for executing some or all of the steps in the aforementioned method.

[0069] The processor 1001 may be a general purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the above program.

[0070] The memory 1002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus. The memory may also be integrated with the processor.

[0071] The computing device 1000 may further include a communication module 1004 and a display 1005. The communication module 1004 may be connected to the optical tracking device for communication. The communication module 1004 may be a wireless communication module (eg, a WiFi module, a Bluetooth module, etc.) or a wired communication module.

[0072] In addition, the computing device 1000 may also include common components such as a communication interface (eg, a USB interface, a microphone interface, etc.), an antenna, etc., which will not be described in detail here.

[0073] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0074] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, the functional units in the various embodiments of the application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0078] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0079] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0080] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0081] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. An optimization control method for an ice storage air conditioning system based on a dynamic programming algorithm, characterized in that: The method comprises: Real-time collection of operating data of the ice storage air conditioning system, including but not limited to the current remaining ice volume, outdoor wet bulb temperature, current system load, electricity price curve and historical operating data; Constructing a directed graph model, the directed graph model including directed graph nodes and interconnection weights between the nodes, and converting the transfer of the ice storage and release operation mode of the ice storage air conditioning system within a preset optimization time domain into solving the shortest path of the directed graph model; Based on the directed graph model, solving the shortest path using a dynamic programming algorithm; Based on the shortest path, a control strategy for the ice storage air conditioning system is generated and sent to the system execution layer. The control strategy includes but is not limited to optimizing the switching decision of the storage and discharge cooling operation mode in each time period in the time domain and the start and stop status of the equipment.

2. The ice storage air conditioning system optimization control method based on dynamic programming algorithm according to claim 1, characterized in that: The method further comprises: The operating data is used to establish a state space of the ice storage air conditioning system to feed back system state information, wherein the state information includes but is not limited to the current remaining ice volume, outdoor wet bulb temperature, current load, energy consumption feedback value and electricity price.

3. The ice storage air conditioning system optimization control method based on dynamic programming algorithm according to claim 2, characterized in that: The method of solving the shortest path based on the directed graph model by using a dynamic programming algorithm specifically includes: Use the current state information of the system as the initial node; The preset optimization time domain is divided into several time periods, each time period is used as a stage in the dynamic programming algorithm calculation, and all possible system states in each time period are used as nodes in the path; Use dynamic programming algorithm to calculate the cumulative cost of each path that will occur at each moment; Starting from the terminal state, the dynamic programming algorithm is used to perform reverse recursive calculations to select the path with the lowest cost from multiple paths that will occur, and finally output the hourly optimal operating mode within the preset optimization time domain.

4. The ice storage air conditioning system optimization control method based on dynamic programming algorithm according to claim 3, characterized in that: The method of calculating the cumulative cost of each path to be taken place hour by hour by using the dynamic programming algorithm specifically includes: The dynamic programming algorithm is used to calculate the multi-step cumulative cost J of each path to be taken. The multi-step cumulative cost J is obtained by summing up the single-step cost g of each stage. The specific calculation formula is as follows: ； Where k is the stage index, x k Indicates the system status, u k represents the decision variable, u k From the k stage, the given dependence on x k The decision set U k Select from; N represents the total number of stages and is equal to the total number of optimizations; The single-step cost g is calculated by the following formula: ； Among them, g k,p represents the electricity price cost, which is obtained by multiplying the electricity price in stage k and the system energy consumption. k,com Represents the comfort cost, which is determined by the chiller chilled water outlet temperature or the building terminal temperature.

5. The ice storage air conditioning system optimization control method based on dynamic programming algorithm according to claim 2, characterized in that: The method further comprises: The operating status of the system is monitored in real time, the state space is dynamically updated, and the operating cost of the system is recalculated. If the operating cost is higher than the preset cost threshold, the dynamic programming algorithm is immediately re-triggered to generate a new optimal operating mode.

6. The ice storage air conditioning system optimization control method based on dynamic programming algorithm according to claim 2, characterized in that: The method further comprises: Set hyperparameters for the dynamic programming algorithm, including: The current remaining ice volume, outdoor wet-bulb temperature and system load in the state space are taken as the main state variables; Adjust the weight parameters of the cost function according to the actual electricity price fluctuation curve; The constraint conditions of the control strategy are set, wherein the constraint conditions of the control strategy include: the system control time interval shall not be less than 60 minutes / time and the threshold value of the remaining ice volume is set according to the total cold storage capacity and demand of the system.

7. The ice storage air conditioning system optimization control method based on dynamic programming algorithm according to claim 3, characterized in that: The mathematical description of the dynamic programming algorithm is as follows: ； Among them, k is the time period index, x k Indicates the system status, u k represents the decision variable, u k From the k stage, the given dependence on x k The decision set U k Select from; f k It means (x k ,u k ), describing the mechanism of updating the state from stage k to stage k + 1; N represents the total number of time periods, which is equal to the total number of optimization times; The cost function accumulated over the stages is denoted as g k (x k ,u k ), g is the single-step cost, and J is the multi-step cumulative cost: ； Through the dynamic programming algorithm, all sequences {u0,…,u N-1 } to minimize the cost function to obtain the optimal value: ； In the formula, * represents all states and optimal decision solutions in the kth stage; g k Represents state x k Adopt a strategy k The single-step cost of J k * It represents the multi-step cumulative minimum cost from stage k to stage m. Before entering the next stage from stage k, all states x in stage k are calculated. k,i The multi-step cumulative minimum cost; Get function , the optimal control sequence starting from the initial state x0 is constructed by the following formula and the corresponding state trajectory : ； Among them, u i * represents the optimal control variable in the i-th stage, arg min means to find the control strategy u that minimizes the following expression i Value; g itoi+1 * Represents the cost from the current stage i to the next stage i+1, and the current control strategy u i Value and system status x i Related; J i+1 * (x i+1 ) starts from stage i+1, based on state x i+1 to the optimal cost function in the subsequent final stage.

8. An optimization control system for ice storage air conditioning system based on dynamic programming algorithm, characterized in that: The system comprises: A data acquisition module, used to collect real-time operating data of the ice storage air conditioning system, the operating data including but not limited to the current remaining ice volume, outdoor wet bulb temperature, current system load, electricity price curve and historical operating data; A model building module is used to build a directed graph model, the directed graph model includes directed graph nodes and interconnection weights between nodes, and converts the transfer of the ice storage and release operation mode of the ice storage air conditioning system within a preset optimization time domain into a shortest path for solving the directed graph model; A calculation module, used for solving the shortest path by using a dynamic programming algorithm based on the directed graph model; The control module is used to generate an ice storage air conditioning system control strategy based on the shortest path and send it to the system execution layer. The control strategy includes but is not limited to the switching decision of the storage and release cooling operation mode in each time period and the start and stop status of the equipment.

9. A computing device, characterized in that The computing device comprises a processor and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is enabled to perform the method according to any one of claims 1 to 7.

Images