Commercial building solar photovoltaic central air conditioning system control method

Abstract

The application claims a kind of based on commercial building solar photovoltaic central air conditioning system control method, comprising the following steps: step 1) collecting the real-time output power data of commercial building solar photovoltaic power generation system and central air conditioning system load data, establish the prediction model of solar production and central air conditioning system load;Step 2) using prediction model to predict the solar production and central air conditioning system load of future one day;Step 3) based on the prediction results of solar production and central air conditioning system load, formulate solar priority strategy, energy storage management strategy and dynamic load balancing strategy;Step 4) according to the optimization control strategy, the operation mode of central air conditioning system is adjusted, to achieve the best control strategy.The application realizes commercial building solar photovoltaic central air conditioning system according to the prediction results of solar production and building central air conditioning load, improves energy utilization efficiency and operation economy.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of air conditioning energy utilization planning, and particularly relates to a control method for a solar photovoltaic central air conditioning system of a commercial building. BACKGROUND

[0002] To meet the development needs of the "double carbon" target, renewable energy power generation has become an important force for China to reduce carbon emissions. In large commercial or office buildings, the energy consumption of the central air conditioning system accounts for 40% to 60% of the total building energy consumption. Therefore, the energy saving of the central air conditioning system is the key to improving the energy efficiency of large commercial and office buildings. The solar photovoltaic technology uses solar energy to convert light into electricity, providing clean and renewable energy for commercial buildings. The installation capacity of photovoltaic systems is increasing, enabling commercial buildings to make greater use of solar energy to meet their energy needs.

[0003] For most central air conditioning systems, to meet the maximum cooling load at the building end, the selection and configuration of the central air conditioning system is generally designed with a certain margin under full load conditions. With the development of Internet of Things, big data and artificial intelligence technology, the solar photovoltaic central air conditioning system of a commercial building can be intelligently controlled, predicting future solar energy production and building load through prediction models and data analysis techniques, and optimizing control according to these predictions, thereby reducing energy consumption while ensuring the comfortable temperature of the human body inside the building.

[0004] CN115378023A considers a photovoltaic grid-connected system operation optimization method considering photoelectricity and photo-thermal and energy storage, including the following steps: step 1: design a photovoltaic grid-connected system; step 2: mathematically model the devices in the system; step 3: establish a target function and constraints with the minimum total cost and the minimum light rejection rate as the target; step 4: use an improved particle swarm algorithm to solve the target function in step 3. The present application designs a photovoltaic grid-connected system that meets the user's cooling and heating demand according to the user's actual load, adds energy storage devices in the system, thereby improving the consumption capacity of the photovoltaic power generation system, combines the photovoltaic array with the solar heat collector, realizes the cogeneration of solar energy, can meet the user's load demand, uses the economic grid-connected mode, realizes the peak clipping and valley filling of the system, and improves the energy utilization rate and the stability of the system operation.

[0005] The patent CN115378023 does not consider the dynamic balance of the load in the system equipment and the comfort constraints of the users. The photovoltaic power generation and the energy demand of the users change with the environment, and the patent ignores this point, only optimally calculates the known variables, and separately calculates the future and different situations, which is not intelligent enough. Moreover, although the particle swarm algorithm used in the patent has a faster calculation speed than the traditional particle swarm algorithm, it can only obtain the optimal solution, but cannot calculate the values of other parameters in the system, thus having limitations for system analysis.

[0006] The present application uses an LSTM neural network model to perform short-term prediction on the cooling load and solar parameters, and formulates corresponding optimization strategies based on the prediction results to improve the economic efficiency and environmental friendliness of the air conditioning system and ensure user environmental comfort. The system is optimized in real time through the formulated strategies to obtain the optimal solution of the constraint conditions. A hybrid algorithm combining the improved particle swarm algorithm and the Hooke-Jeeves algorithm is used, the improved particle swarm algorithm improves the calculation speed of the algorithm, and the Hooke-Jeeves algorithm can calculate the optimal solutions of the four continuous optimization variables, i.e., the cooling water temperature of the water chiller, the cooling tower outlet water temperature, the power supply of the energy storage device, and the power supply of the power grid, to further improve the air conditioning system. SUMMARY

[0007] The present application aims to solve the problems of the prior art. A control method for a solar photovoltaic central air conditioning system in a commercial building is proposed. The technical scheme of the present application is as follows:

[0008] A control method for a solar photovoltaic central air conditioning system in a commercial building, comprising the following steps:

[0009] S1, collecting real-time output power data of a solar photovoltaic power generation system and building central load data of a commercial building, and establishing a prediction model for solar energy production and central air conditioning system load;

[0010] S2, using an LSTM neural network model to perform short-term prediction on the cooling load and solar heat parameters, and performing statistical prediction on the solar energy production and central air conditioning system load for the next day;

[0011] S3, based on the prediction results of the solar energy production and central air conditioning load, formulating a solar priority strategy, an energy storage management strategy, and a dynamic load balancing strategy;

[0012] S4, adjusting the operation mode of the air conditioning system according to the specified solar priority strategy, energy storage management strategy, and dynamic load balancing strategy to achieve the best control strategy.

[0013] Further, the step S1 collects real-time output power data of a solar photovoltaic power generation system of a commercial building and building central load data, and establishes a prediction model of solar energy production and central air conditioning system load, specifically including:

[0014] A target function is established with economic cost, solar photovoltaic utilization rate, and environmental comfort, the target function including a solar energy priority strategy, an energy storage management strategy, and a dynamic load balancing strategy, wherein the economic cost target function is:

[0015]

[0016] Wherein F eco is the total economic cost of the building central air conditioning system, C EM,j,t represents the operation and maintenance cost of the air conditioning energy storage and solar photovoltaic device at time j, C BE,t represents the electricity purchase price at time t, P buy,t represents the electricity purchase amount of the scheduling period at time t.

[0017] The demand for using the power grid is minimized, that is, the air conditioning load supplied by solar energy is maximized. The solar photovoltaic utilization rate target function is:

[0018]

[0019] P PV,t represents the output power of the photovoltaic at time t, kW. P buy,t represents the electricity purchase amount of the scheduling period at time t. P BES,t is the charge and discharge power of the battery energy storage at time t, kW.

[0020] A target function is established with environmental comfort, and the target function is:

[0021]

[0022] In the formula, Δl t is the change in electricity consumption of the central air conditioning system in each period before and after the user adjusts the energy consumption; l t is the total electricity consumption of the central air conditioning system in each period before the user adjusts the energy consumption, T t is the target indoor temperature of the user at time t, ΔT t is the difference between the indoor temperature and the target temperature of the user at time t.

[0023] Further, the step S3, based on the prediction results of the solar energy production and the central air conditioning load, formulates a solar energy priority strategy, an energy storage management strategy, and a dynamic load balancing strategy, specifically including:

[0024] The solar photovoltaic, energy storage device, and power grid direct power supply satisfy the following relationship with the electricity consumed by the multi-frequency air conditioning load:

[0025] P PV (t)+P buy (t)+P BES (t)=P ce,i (t)

[0026] 0≤P ce,i (t)≤P ce,e

[0027] In the formula P ce,i (t) represents the power consumption of the i-th refrigeration unit at time t, P PV (t) represents the photovoltaic power output at time t, P buy (t) represents the amount of electricity purchased during the scheduling cycle at time t, P BES P(t) is the charging and discharging power of the battery at time t. ce,e This represents the rated power of the air conditioning system; for energy storage devices, the following relationship must be satisfied at any given moment:

[0028] E(t+1)=E(t)+η C ·Q C (t)·Δt-Q D (t) / η D ·Δt

[0029] E min ≤E(t)≤E max

[0030] Where E represents the electrical energy stored in the energy storage system, and η C and η D The respective efficiencies of the system during charging and discharging; Q C (t) represents the electrical energy, Q, that is charged into the energy storage system. D (t) represents the electrical energy released from the energy storage system, and Δt represents the time interval between charging and discharging. E min and E max These are the upper and lower limits of the energy storage capacity of the energy storage system; the upper and lower limits of the power and rate of solar photovoltaic power generation are:

[0031] α(i)·P EL·min ≤P EL (i)≤α(i)·P EL·max

[0032] |P EL (i)-P EL (i-1)|≤ΔP EL·max

[0033] α is a logical variable, where α(i) represents the energy storage device being in operation at time i; P EL·max P EL·minrespectively represent the upper and lower limits of the power generation of the energy storage device EL·max is the upper limit of the power generation rate;

[0034] The building temperature constraint is:

[0035]

[0036]

[0037] wherein, T air,h and T air,c are the room temperature during heating and refrigeration, respectively;

[0038] The minimum start-stop time constraint is:

[0039]

[0040]

[0041] wherein, is the minimum start-up holding time, that is, the system must remain in the working state for a period of time after starting and cannot be shut down; is the minimum shutdown holding time, that is, the system must remain in the non-working state for a period of time after shutdown and cannot be started.

[0042] Further, in the step S2, the cold load parameter is short-term predicted by using the LSTM neural network model.

[0043] Further, in the step S4, the operation mode of the air conditioning system is adjusted according to the optimization control strategy to achieve the best control strategy, specifically including:

[0044] According to the hybrid algorithm combining the Hooke-Jeeves mode search algorithm and the particle swarm optimization algorithm (PSO), the four continuous optimization variables of the chilled water chiller set outlet water temperature, the cooling tower set outlet water temperature, the energy storage device power supply power, and the power grid power supply power are sought for the optimal solution; wherein the particle swarm optimization algorithm is improved in iteration speed as follows:

[0045] v i (k+1) = χ[v i (k) + c1ρ1(k)(p l,i (k) - x i (k)) + c2ρ2(k)(p a,i (k) - x i (k))]

[0046]

[0047]

[0048] v i (k+1) represents the speed information in the iteration process, χ represents a controllable constant introduced in the improved particle swarm algorithm, c1 represents a cognitive accelerated particle, x i (k) represents the state of the particle after iteration, that is, the optimal solution obtained at present, c2 represents a social accelerated particle, ρ1(k) and ρ2(k) represent random numbers, κ represents an adjustment parameter, represents the sum of the particles.

[0049] According to the optimized control strategy, the adjusted air conditioning system operation mode is implemented.

[0050] Further, the pattern search algorithm Hooke-Jeeves specifically includes:

[0051] The extreme point of the target function change specified by the strategy is found. For a problem of multi-dimensional optimization variables, the pattern search algorithm finds a base along a direction in which the function decreases, and if not found, switches to the next base, until all base directions are traversed, and then the initial point and search step are adjusted, until the algorithm converges to obtain the required solution.

[0052] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the business building solar photovoltaic central air conditioning system control method based on any one of the embodiments when executing the program.

[0053] A non-transitory computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the business building solar photovoltaic central air conditioning system control method based on any one of the embodiments.

[0054] A computer program product includes a computer program, and the computer program is executed by a processor to implement the business building solar photovoltaic central air conditioning system control method based on any one of the embodiments.

[0055] The advantages and beneficial effects of the present application are as follows:

[0056] The present application proposes a central air conditioning system control method based on solar photovoltaic power generation of a business building, which first predicts the solar energy output and the central air conditioning system load of the next day based on a prediction model of the solar energy output and the central air conditioning system load, then formulates a solar energy priority strategy, an energy storage management strategy, and a dynamic load balancing strategy according to the prediction results of the solar energy output and the central air conditioning load, and finally adjusts the air conditioning system operation mode according to an optimized control strategy to achieve the best control strategy.

[0057] The existing building solar photovoltaic power generation central air conditioning system can only directly formulate an energy storage strategy by determining the relationship between the heat power output and the indoor temperature, and the commercial building solar photovoltaic power generation central air conditioning system provided by the application is based on the establishment of a prediction model of solar energy production and central air conditioning system load to perform real-time control on the heat power output and the indoor temperature, can dynamically adjust to different solar energy production and load demand, adapt to complex operating environments and changing energy supply and demand conditions, maximize the use of solar energy to supply air conditioning load, reduce dependence on traditional power, and improve energy utilization efficiency.

[0058] In view of the problems that the traditional air conditioning system often has energy waste and low efficiency, the energy management of commercial buildings faces complex and variable challenges, the solar photovoltaic energy production is affected by weather and sunlight conditions, and the building load changes with time and personnel activity, the optimization control method provided by the application can dynamically adjust and optimally operate according to real-time solar energy production and load demand, and cope with complex operating environments and energy supply and demand changes. The method combines a hybrid algorithm optimization control strategy of a pattern search algorithm (Hooke-Jeeves algorithm) and an improved particle swarm optimization algorithm (PSO algorithm), based on the application of a prediction model and an optimization algorithm, the commercial building solar photovoltaic central air conditioning system can realize flexible scheduling and control, dynamically adjust according to different solar energy production and load demand, adapt to complex operating environments and changing energy supply and demand conditions, maximize the use of solar energy to supply air conditioning load, reduce dependence on traditional power, and improve energy utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is the control method flow chart of the commercial building solar photovoltaic power generation central air conditioning system provided by the preferred embodiment of the application.

[0060] Figure 2 It is the hybrid algorithm flow chart of the pattern search algorithm (Hooke-Jeeves algorithm) and the particle swarm optimization algorithm (PSO algorithm) combined in the control method of the commercial building solar photovoltaic power generation central air conditioning system provided by the preferred embodiment of the application.

[0061] Figure 3 It is the model and algorithm joint simulation flow chart in the control method of the commercial building solar photovoltaic power generation central air conditioning system provided by the preferred embodiment of the application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the application will be described in detail below with reference to the drawings of the embodiments of the application. The described embodiments are only some of the embodiments of the application.

[0063] The technical solution of the application to solve the above technical problems is:

[0064] Please see Figure 1 As shown in the figure, this application provides a control method for a central air conditioning system based on solar photovoltaic power generation in commercial buildings, including the following specific steps:

[0065] S1. Collect real-time output power data of solar photovoltaic power generation systems and building central load data of commercial buildings, and establish a predictive model for solar energy output and central air conditioning system load;

[0066] S2. Use a forecasting model to predict solar energy output and central air conditioning system load for the next day;

[0067] S3. Based on the forecast results of solar energy production and central air conditioning load, formulate a solar energy priority strategy, an energy storage management strategy, and a dynamic load balancing strategy;

[0068] S4. Adjust the operating mode of the air conditioning system according to the optimized control strategy to achieve the best control strategy.

[0069] This application first considers a solar photovoltaic central air conditioning system consisting of six sets of commercial building solar photovoltaic power generation systems and six central air conditioning units. Real-time output power data and building load data of the commercial building's solar photovoltaic power generation systems are collected. Step S1, establishing a predictive model for solar energy output and central air conditioning system load, comprises the following steps:

[0070] For the solar energy production and central air conditioning system prediction model, firstly, based on the solar radiation intensity q on the surface of the PV / T module... solar ; PV / T module surface area A c The solar thermal energy was obtained.

[0071] Q solar =q solar A c

[0072] Then, the load size is estimated using the power of the central air conditioning system. After the model is determined, the load is calculated based on the meter reading P. chiller The cooling load Q is obtained from the COP (coefficient of performance) under its operating conditions. i The specific formula is as follows:

[0073] Q i =P chiller ×COP

[0074] The acquired cooling load and solar thermal data are then processed, and the data exceeding the maximum increment difference P during the measurement period are identified. max The load is taken as an isolated point. As shown in the following equation:

[0075] ΔQ d,j =Qd,j - Q d,j-1

[0076] ΔQ i,j = Q i,j - Q i,j-1

[0077] P j = |ΔQ d,j - ΔQ i,j |

[0078]

[0079] where ΔQ d,j and ΔQ i,j are the jth increments of Q d and Q i , P j is the difference between ΔQ d,j and ΔQ i,j , and P max is the maximum acceptable difference in increments. The sequence P j is compared to P max , and increments that exceed the acceptable range are treated as outliers and replaced with a revised value.

[0080] Q f,j = Q d,j-1 + ΔQ i,j

[0081] After the outliers are removed, the noise in the measurement process is removed by

[0082]

[0083]

[0084]

[0085] Q f is the fused cooling load and solar heat, where is a vector composed of increments of the indirectly measured cooling load and solar heat, and W is a weight vector. The fused cooling load and solar heat are shifted in sequence with the indirectly measured cooling load and solar heat according to the above equation to reduce errors, and points that are too far apart are identified as systematic error outliers and corrected, resulting in a final cooling load result.

[0086] The obtained results are used for short-term prediction of cold load and solar heat parameters through an LSTM neural network model. The sampling interval of each data is set to 1 minute. The collected cold load, solar heat, outdoor temperature, outdoor humidity and time data are used as input, and the future 20-minute cold load and solar heat prediction results are used as output. In this way, the cold load and solar heat are predicted every 20 minutes, and the control strategy for the next 20 minutes is executed according to the prediction results.

[0087] The solar energy priority strategy, energy storage management strategy and dynamic load balancing strategy are formulated based on the prediction results of the solar energy production and central air conditioning load of S3. The target function includes the device solar energy priority strategy, energy storage management strategy and dynamic load balancing strategy of claim 2. The economic cost target function is:

[0088]

[0089] Where F eco is the total economic cost of the building central air conditioning system, C EM,j,t represents the operation and maintenance cost of the air conditioning energy storage and solar photovoltaic device at time t, yuan; C BE,t represents the electricity purchase price at time t, yuan / (kW·h), P buy,t represents the electricity purchase quantity in the dispatching period at time t, kW·h.

[0090] The solar energy supply air conditioning load is maximized, and the demand for the power grid is reduced. The solar photovoltaic utilization rate target function is:

[0091]

[0092] P PV,t represents the output power of the photovoltaic at time t, kW. P buy,t represents the electricity purchase quantity in the dispatching period at time t. P BES,t is the charge and discharge power of the battery energy storage at time t, kW.

[0093] The target function is established based on environmental comfort. The target function is:

[0094]

[0095] In the formula, Δl t is the change in electricity consumption of the central air conditioning system before and after the user adjusts the energy consumption; l t is the total electricity consumption of the central air conditioning system before the user adjusts the energy consumption, T t is the target indoor temperature of the user at time t, ΔT t is the difference between the indoor temperature and the target temperature of the user at time t.

[0096] Where the solar photovoltaic, energy storage device and grid direct power supply and the power consumed by the multi-frequency air conditioning load should meet the following relationship:

[0097] P PV (t) + P buy (t) + P BES (t) = P ce,i (t)

[0098] 0 ≤ P ce,i (t) ≤ P ce,e

[0099] P ce,i (t) represents the power consumption of i electric refrigeration unit at t time, kW. P PV (t) represents the output power of photovoltaic at t time, kW. P buy (t) represents the power purchase at t time, KW. P BES (t) is the charge and discharge power of the battery storage at t time, kW. P ce,e represents the rated power of the air conditioning system. For the energy storage device, it should meet the following relationship at each time:

[0100] E(t+1) = E(t) + η C · Q C (t) · Δt - Q D (t) / η D · Δt

[0101] E min ≤ E(t) ≤ E max

[0102] Where E is the stored energy of the energy storage system, η C and η D are the efficiencies of the system charging and discharging, respectively. E min and E max are the upper and lower limits of the energy storage capacity of the energy storage system. The upper and lower limits of the solar photovoltaic power and rate are:

[0103] α(i) · P EL·min ≤ P EL (i) ≤ α(i) · P EL·max

[0104] P EL (i) - P EL (i-1) ≤ ΔP EL·max

[0105] P EL (i-1) - P EL (i) ≤ ΔP EL·max

[0106] α is a logic variable, α(i) represents the energy storage device is in operation at i time. P EL·max , P EL·min represent the upper and lower limits of the power generation of the energy storage device, ΔP EL·max is the upper limit of the power generation rate. The building temperature constraint is:

[0107]

[0108]

[0109] Where, T air,h and T air,c are the room temperature when heating and cooling. The minimum start-stop time constraint is:

[0110]

[0111]

[0112] In the formula, is the minimum start-up holding time, that is, the system must remain in working condition for a period of time after starting and cannot stop; is the minimum shutdown holding time, that is, the system must remain in non-working condition for a period of time after shutdown and cannot start.

[0113] For S4, according to the optimal control strategy, the operation mode of the air conditioning system is adjusted to achieve the best control strategy. According to Figure 2 the flow chart of the hybrid algorithm combining the Hooke-Jeeves algorithm and the particle swarm optimization (PSO) algorithm, the four continuous optimization variables of the chilled water chiller outlet temperature, the cooling tower outlet temperature, the energy storage device power supply power and the grid power supply power are set to seek the optimal solution. Among them, the particle swarm optimization algorithm is improved for the iteration speed:

[0114] v i (k+1) = χ[v i (k) + c1ρ1(k)(p l,i (k) - x i (k)) + c2ρ2(k)(p a,i (k) - x i (k))]

[0115]

[0116]

[0117] According to the optimized control strategy, the adjusted operation mode of the air conditioning system is implemented. At the same time, the solar energy production, building load and system performance index are continuously monitored for real-time feedback and adjustment. The specific model and algorithm joint simulation flowchart is as follows Figure 3 The entire control is cyclically controlled with a period of 20 minutes.

[0118] The system, device, module or unit illustrated in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions.

[0119] The computer readable medium includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer readable medium does not include transitory computer readable medium such as modulated data signals and carriers.

[0120] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0121] The above embodiments should be understood as only for illustrating the present application and not for limiting the protection scope of the present application. After reading the content of the present application, the skilled in the art can make various changes or modifications to the present application, and these equivalent changes and modifications also fall within the scope defined by the claims of the present application.

Claims

1. A control method for a solar photovoltaic central air conditioning system based on a commercial building, characterized in that, The method comprises the following steps: S1, collecting real-time output power data of a solar photovoltaic power generation system and building central load data of a commercial building, specifically including: collecting cold load, solar heat, outdoor temperature, outdoor humidity and time data, and establishing a prediction model of solar energy production and central air conditioning system load; the prediction model is an LSTM neural network model; S2, using the LSTM neural network model to perform short-term prediction on the cold load and solar heat, and to perform prediction and statistics on the solar energy production and central air conditioning system load of the next day; S3, based on the prediction results of the solar energy production and central air conditioning load, formulating a solar priority strategy, an energy storage management strategy and a dynamic load balancing strategy; establishing a target function of economic cost, solar photovoltaic utilization rate and environmental comfort, the target function including the solar priority strategy, the energy storage management strategy and the dynamic load balancing strategy, wherein the economic cost target function is: wherein is the total economic cost of the building central air conditioning system, represents the operation and maintenance cost of the air conditioning energy storage and solar photovoltaic device at time t, represents the electricity purchase price at time t, represents the electricity purchase quantity of the scheduling period at time t; the minimum demand for using the power grid, i.e. maximizing the use of solar energy to supply air conditioning load, the solar photovoltaic utilization rate target function is: Ppv(t) represents the output power of the photovoltaic at time t, kW; Pb(t) represents the power purchase at time t in the dispatch cycle; Pb(t) represents the power purchase at time t in the dispatch cycle; a target function is established according to the environmental comfort, and the target function is: In the formula is the user's ability to adjust the power consumption of the central air conditioning system before and after each period; is the user's ability to adjust the total power consumption of the central air conditioning system before each period, is the user's target indoor temperature at time t, is the difference between the user's indoor temperature at time t and the target temperature; the power consumed by the solar photovoltaic, energy storage device and power grid direct power supply and the multi-frequency air conditioning load satisfies the following relationship: wherein represents the power consumption of i electric refrigeration unit at time t, represents the output power of photovoltaic at time t, represents the electricity purchase amount of the dispatch cycle at time t, is the charge and discharge power of the battery energy storage at time t, represents the rated power of the air conditioning system; for the energy storage device, the following relationship is satisfied at each time: wherein, E is the electrical energy stored by the energy storage system, and are the efficiencies of the system when charging and discharging, respectively; Ei represents the electrical energy charged into the energy storage system, Ed represents the electrical energy discharged from the energy storage system; Ti represents the time interval of charging and discharging; and Emin and Emaz are the lower and upper limits of the energy storage capacity of the energy storage system, respectively, and the lower and upper limits of the solar photovoltaic power and rate are: is a logical quantity, represents that the energy storage device is in operation at time i; , respectively represent upper and lower limits of the power generated by the energy storage device, is an upper limit of the power generation rate of change. the building temperature constraint is: wherein, and T and T are the room temperature during heating and cooling, respectively; the minimum start-stop time constraint is: In the formula, Tmin is the minimum start-up holding time, i.e. the system must remain in the working state for a certain period of time after starting up and cannot be shut down; Tmin is the minimum start-up holding time, i.e. the system must remain in the working state for a certain period of time after starting up and cannot be shut down; S4, adjusting the air conditioning system operation mode according to the specified solar priority strategy, energy storage management strategy and dynamic load balancing strategy to achieve the best control strategy; specifically including: combining the mixed algorithm of the mode search algorithm Hooke-Jeeves and the particle swarm algorithm PSO to seek the optimal solution of the four continuous optimization variables of the outlet water temperature of the cold water chiller unit, the outlet water temperature of the cooling tower, the power supply power of the energy storage device and the power supply power of the power grid; wherein the particle swarm algorithm is improved in iteration speed: denotes the velocity information in the iteration process, denotes the controllable constant introduced in the improved particle swarm algorithm, denotes the cognitive accelerated particle, denotes the state of the particle after iteration, i.e. the optimal solution obtained at present, denotes the social accelerated particle, and denotes a random number, denotes an adjustment parameter, denotes the sum of particles; according to the optimized control strategy, the adjusted air conditioning system operation mode is implemented; the mode search algorithm Hooke-Jeeves specifically includes: finding the extreme point of the target function according to the change direction of the target function; for a multi-dimensional optimization variable problem, the mode search algorithm finds a base along a direction in which the function decreases, and if no base is found, it switches to the next base, until all base directions are traversed, and the initial point and search step are adjusted, until the algorithm converges to obtain the required solution.

2. An electronic device, comprising: The computer program is stored in the memory and can be run on the processor, and when the processor executes the program, the method for controlling a solar photovoltaic central air conditioning system of a commercial building according to claim 1 is realized.

3. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is stored in the memory and can be run on the processor, and when the processor executes the program, the method for controlling a solar photovoltaic central air conditioning system of a commercial building according to claim 1 is realized.

4. A computer program product comprising a computer program, characterized in that, The computer program is stored in the memory and can be run on the processor, and when the processor executes the program, the method for controlling a solar photovoltaic central air conditioning system of a commercial building according to claim 1 is realized.

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