Photovoltaic air conditioner room temperature self-adaptive regulation method and system based on passive energy storage

Abstract

The present application relates to photovoltaic air conditioning technical field, solved the problem that photovoltaic in-situ consumption and energy balance is difficult to effectively improve in the prior art, provide a kind of photovoltaic air conditioner room temperature self-adaptive regulation and control method and system based on passive energy storage.The method comprises: obtaining the photovoltaic power generation of target building in current control period corresponding and the real-time power load of target building;According to photovoltaic power generation and the real-time power load of target building, obtain the matching relationship index of photovoltaic and load;According to temperature regulation criterion and preset adaptive temperature setting strategy, determine the set temperature of air conditioner in current control period;The temperature of air conditioner in target building is set to adjust room temperature.The present application realizes the collaborative optimization control of photovoltaic power generation and building power load under the premise of not increasing additional energy storage equipment and not significantly improving system complexity, and considers photovoltaic consumption rate, building energy balance and the stability of air conditioner operation, and has higher engineering application value.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic air conditioning technology, and in particular to a method and system for adaptive temperature control of photovoltaic air conditioning based on passive energy storage. Background Technology

[0002] With the widespread application of distributed photovoltaic (PV) power generation in the building sector, the proportion of renewable energy generation on the building side is continuously increasing. However, due to the randomness and intermittency of PV power generation, its output is significantly affected by factors such as sunlight conditions and weather conditions. Meanwhile, building electricity load changes dynamically with factors such as human activity and equipment operating status. These two factors often cannot naturally match over time, easily leading to situations where PV power generation is either excessive or insufficient.

[0003] To improve the local consumption of photovoltaic power generation, existing technologies typically balance the difference between photovoltaic power generation and building electricity load by configuring electrochemical energy storage devices or actively dispatching electricity loads. However, electrochemical energy storage devices are costly, have limited lifespans, and are difficult to deploy in existing buildings; load-side active dispatching schemes often require coordinated control of multiple electrical devices, resulting in high system complexity and implementation costs.

[0004] For example, Chinese patent CN120819888A proposes a photovoltaic air conditioner and its control method. By predicting the deviation between photovoltaic power generation and air conditioning load power, it adjusts the air conditioning fan speed and outlet temperature in stages when the deviation exceeds a threshold, and, when necessary, uses energy storage devices to store or release energy, thereby improving photovoltaic absorption capacity and system operational stability. However, in practical applications, the above technical solutions still mainly rely on active power regulation and explicit energy storage devices to cope with fluctuations in photovoltaic output. Their control logic focuses on power regulation on the power generation side and the air conditioning side, underutilizing the inherent thermal inertia of the building itself and its buffering role in energy balance. On the one hand, frequent adjustments to air conditioning operating parameters or reliance on electrochemical energy storage easily increase system complexity and equipment costs; on the other hand, when other electrical loads within the building change, controlling solely based on photovoltaic output or air conditioning power makes it difficult to reflect the actual impact of air conditioning regulation on the building's overall electricity balance, thus affecting the effectiveness and stability of local photovoltaic absorption.

[0005] Therefore, the key technical problem that urgently needs to be solved is: how to obtain the regulatory basis that can reflect the real-time energy consumption status of the target building and support control decisions when photovoltaic output fluctuates and building electricity demand changes dynamically, so that the local consumption of photovoltaic power and the balance of building energy consumption can be improved in a coordinated manner, while reducing the system's dependence on additional energy storage devices. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method and system for adaptive temperature control of photovoltaic air conditioning based on passive energy storage, in order to solve the problem that it is difficult to coordinate the improvement of local photovoltaic consumption and energy balance in the prior art.

[0007] In a first aspect, embodiments of the present invention provide a method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage, the method comprising: Obtain the photovoltaic power generation of the target building in the current control cycle and the real-time power load of the target building; Based on the photovoltaic power generation and the real-time electricity load of the target building, obtain the matching relationship index between photovoltaic and load; Based on temperature control criteria and preset adaptive temperature setting strategy, the air conditioning set temperature for the current control cycle is determined, wherein the temperature control criteria include at least one of photovoltaic power generation, the matching relationship index, and time-of-use electricity price information of the target building's area; Based on the air conditioner's set temperature, set the temperature of the air conditioner in the target building to regulate the room temperature; Wherein, the air conditioner is set to a temperature within a preset temperature range, the preset temperature range includes at least a first temperature level and a second temperature level, and the first temperature level is lower than the second temperature level; When the air conditioner is set to the first temperature level, the building envelope and / or interior components of the target building absorb cold energy to form cold energy storage; when the air conditioner is set to the second temperature level, the target building releases cold energy.

[0008] Secondly, embodiments of the present invention provide a photovoltaic air conditioning room temperature adaptive control system based on passive energy storage. The system includes at least a photovoltaic power generation device, an air conditioner, a power monitoring sensor, and a controller. The controller is connected to the photovoltaic power generation device, the air conditioner, and the power monitoring sensor, respectively, and is configured to execute the photovoltaic air conditioning room temperature adaptive control method based on passive energy storage described in the first aspect.

[0009] In summary, the beneficial effects of the present invention are as follows: The photovoltaic air conditioning room temperature adaptive control method and system based on passive energy storage provided in this invention obtains the photovoltaic power generation of the target building in the current control cycle and the real-time power load of the target building, and obtains the matching relationship index between photovoltaic and load based on the two, so that the determination of the air conditioning set temperature can reflect the actual coupling state between photovoltaic output and building energy demand, thereby avoiding the control deviation caused by adjusting only based on photovoltaic power generation.

[0010] Meanwhile, by introducing photovoltaic power generation, photovoltaic and load matching indicators, and time-of-use electricity price information as temperature control criteria, and combining them with a preset adaptive temperature setting strategy, the air conditioning set temperature is adaptively adjusted under different operating conditions, so that the air conditioning operation is coordinated with the fluctuation of photovoltaic output and the changes in building electricity demand, which is conducive to improving the local photovoltaic absorption capacity and the building energy balance level.

[0011] Furthermore, by limiting the air conditioning set temperature to a preset temperature range including a first temperature level and a second temperature level, and resetting the air conditioning set temperature to different temperature levels in different control cycles, the building envelope and / or interior components of the target building absorb cold energy to form cold energy storage at low temperature levels and release cold energy at high temperature levels to reduce the air conditioning load. This utilizes the building's thermal inertia to achieve the time migration of cold energy, achieving the effect of passive energy storage load transfer without the need for additional energy storage devices.

[0012] Therefore, this invention achieves coordinated optimization control of photovoltaic power generation and building electricity load without adding additional energy storage equipment or significantly increasing system complexity. It takes into account photovoltaic absorption rate, building energy balance and air conditioning operation stability, and has high engineering application value. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0014] Figure 1 This is a schematic flowchart of the photovoltaic air conditioning room temperature adaptive control method based on passive energy storage described in Embodiment 1 of the present invention; Figure 2 This is a schematic flowchart of the photovoltaic air conditioning room temperature adaptive control method based on passive energy storage described in Embodiment 2 of the present invention; Figure 3 This is a schematic flowchart of the photovoltaic air conditioning room temperature adaptive control method based on passive energy storage described in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram comparing the monthly energy characteristics under the baseline operating conditions and four control strategies during the cooling season in Embodiment 7 of the present invention. Figure 5a This is a schematic diagram comparing the monthly photovoltaic self-consumption rate under the baseline operating condition and the four control strategies in Embodiment 7 of the present invention; Figure 5b This is a schematic diagram comparing the monthly photovoltaic self-sufficiency rate under the baseline operating condition and the four control strategies in Embodiment 7 of the present invention; Figure 6a This is a schematic diagram showing the changes in photovoltaic power, building load, and room temperature over a continuous working day under the baseline operating conditions in Embodiment 7 of the present invention. Figure 6b This is a schematic diagram showing the changes in photovoltaic power, building load, and room temperature during a continuous working day under control strategy I in Embodiment 7 of the present invention. Figure 6c This is a schematic diagram showing the changes in photovoltaic power, building load, and room temperature during a continuous working day under control strategy II in Embodiment 7 of the present invention. Figure 6d This is a schematic diagram showing the changes in photovoltaic power, building load, and room temperature during a continuous working day under control strategy III in Embodiment 7 of the present invention. Figure 6e This is a schematic diagram showing the changes in photovoltaic power, building load, and room temperature during a continuous working day under control strategy IV in Embodiment 7 of the present invention. Figure 7a This is a schematic diagram comparing the photovoltaic self-consumption rate under different photovoltaic installed capacity ratios in various operating conditions in Embodiment 7 of the present invention; Figure 7b This is a schematic diagram comparing the photovoltaic self-sufficiency rate under different photovoltaic installation capacity ratios in various operating conditions in Embodiment 7 of the present invention; Figure 8 This is a schematic diagram showing the annual operating cost and investment payback period of the baseline operating condition and various control strategies under different electricity price schemes in Embodiment 7 of the present invention; Figure 9 This is a schematic diagram comparing the investment payback period of various control strategies under different photovoltaic installed capacity ratios and feed-in tariffs in Embodiment 7 of the present invention; Figure 10 This is a schematic diagram showing the PMV distribution under the baseline operating conditions and various control strategies in Embodiment 7 of the present invention. Detailed Implementation

[0015] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.

[0016] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0017] It should be noted that all actions involving the acquisition of signals, information, or data in this invention are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0018] Example 1 Please see Figure 1 This invention provides a method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage, the method comprising: S1. Obtain the photovoltaic power generation of the target building in the current control cycle and the real-time power load of the target building; Photovoltaic power generation refers to the instantaneous output of the rooftop photovoltaic array during the current control cycle, representing the scale of electricity available for local consumption. Real-time power load refers to the total power demand of the target building during the same control cycle, including at least air conditioning power and basic power consumption for building lighting and equipment, representing the current absorption capacity and power gap. The purpose of this step is to establish a unified input for subsequent temperature adaptive control, enabling the adjustment of the air conditioning setpoint temperature to be based on real-time supply and demand conditions rather than relying on predictive models. This involves periodically sampling the control step size and using the sampled values ​​as input for the control calculations of the current cycle, thus providing a data foundation for subsequent matching relationship calculations and setpoint temperature reset.

[0019] Preferably, the photovoltaic AC output power value for the current control cycle is obtained through the inverter or grid-connected controller on the photovoltaic side. This power value can be read from the inverter's real-time power register or sampled by an energy meter configured with the inverter. Alternatively, a power metering unit can be installed at the photovoltaic combiner or grid-connected point to collect the voltage and current at the photovoltaic grid-connected point and calculate the instantaneous power. Then, a sampling or average value is taken once within the control cycle as the photovoltaic power generation for that cycle. To improve robustness, the sampled power can be limited or filtered using a moving average to suppress the impact of instantaneous spikes on subsequent temperature resets.

[0020] Photovoltaic power generation and real-time building electricity load are sampled at the same time within the same control cycle, or the average power within the same control cycle is used to ensure comparability between the two under the same time reference. At the end of each control cycle, the control module uses the photovoltaic power generation and real-time electricity load collected in that cycle as inputs to calculate the air conditioning set temperature for the next control cycle, thus forming a real-time adaptive control process that iteratively updates according to the control cycle.

[0021] In an optional embodiment, the photovoltaic power generation and building electricity load can also be obtained through a simulation platform. The simulation platform simulates the actual operating state of the building and photovoltaic system, and predicts the dynamic changes in photovoltaic power generation and building electricity consumption based on the system model and external environment (such as meteorological data, grid load, etc.), and uses this as the control input. This method is suitable for scenarios where real-time data collection is not possible, or for verifying and optimizing the implementation effect of control strategies.

[0022] S2. Based on the photovoltaic power generation and the real-time power load of the building, obtain the matching relationship index between photovoltaic and load; The matching relationship index is used to characterize the relative relationship between photovoltaic (PV) output and building load in the current control cycle, reflecting whether PV is in surplus or deficit. The purpose of this index is to convert the comparison between PV output and building load into a criterion for temperature control, enabling the air conditioning setpoint to adaptively adjust to changes in the direction or degree of deviation between PV and load. In practice, the matching relationship index can be characterized by the difference between PV power and load power, as suggested in the paper, or by their ratio, and used as input to the temperature reset strategy, thus providing a unified calculation interface for different adaptive strategies.

[0023] S3. Determine the air conditioning set temperature for the current control cycle based on the temperature control criteria and the preset adaptive temperature setting strategy. The temperature control criteria include one of the following: photovoltaic power generation, the matching relationship index, and the time-of-use electricity price information of the area where the target building is located. Temperature control criteria are the basis for triggering and determining the direction and magnitude of set temperature adjustments. These criteria include photovoltaic power generation, matching relationship indicators, and time-of-use pricing information. The purpose of this step is to achieve synchronous matching of air conditioning load and photovoltaic output while ensuring deployability and considering peak-shifting requirements driven by electricity prices. In practice, when the criterion is time-of-use pricing information, different set temperature levels can be reset according to peak and off-peak periods; when the criterion is photovoltaic power generation, different set temperatures can be selected according to photovoltaic output levels; when the criterion is matching relationship indicators, the set temperature can be gradually adjusted in a fixed step size according to the difference direction, or adjusted proportionally in multiple levels according to the ratio range. To avoid frequent switching due to small fluctuations, the set temperature reset hysteresis logic in the document can be used to keep the set temperature constant within the equilibrium range, thereby improving control stability.

[0024] S4. Set the temperature of the air conditioner in the target building according to the air conditioner's set temperature to regulate the room temperature; The air conditioning setpoint temperature is the target control variable of the air conditioning system. By resetting the setpoint temperature, the cooling load is adjusted, thereby changing the building's ability to absorb photovoltaic power or its dependence on grid power. The purpose of this step is to translate the setpoint temperature obtained in S3 into actual control actions, causing the building load to increase when there is a photovoltaic surplus to absorb more photovoltaic power, and to decrease when there is a photovoltaic shortage to reduce grid input. In practice, the controller sends the air conditioning setpoint temperature for the current control cycle to the air conditioning system, and the air conditioner operates at the setpoint temperature and continues to update the setpoint temperature in subsequent control cycles, thus forming a closed-loop regulation effect based on real-time supply and demand.

[0025] Wherein, the air conditioner is set to a temperature within a preset temperature range, the preset temperature range includes at least a first temperature level and a second temperature level, and the first temperature level is lower than the second temperature level; When the air conditioner's set temperature is less than or equal to the first temperature level, the building envelope and / or interior components of the target building absorb cold energy to form cold energy storage; when the air conditioner's set temperature is greater than or equal to the second temperature level, the target building releases cold energy.

[0026] Specifically, the air conditioner's set temperature is within a preset temperature range, and includes at least a first temperature level and a second temperature level, with the first temperature level being lower than the second temperature level. This limitation is used to ensure that the control action has sufficient load adjustment space without exceeding the acceptable range of thermal comfort.

[0027] When the set temperature is below the first temperature level, the indoor environment is cooled more thoroughly. Driven by the temperature difference, the building envelope and interior components gradually absorb cold energy to form cold energy storage, which is equivalent to converting the electricity generated during periods of photovoltaic surplus into cold energy reserves in the building's thermal mass. When the set temperature switches to a level above the second temperature level, the demand for air conditioning decreases, and the building envelope and interior components release the previously stored cold energy to support the indoor thermal environment. This is equivalent to outputting cold energy to achieve load shifting during periods of insufficient photovoltaic power or high electricity prices. This mechanism enables the system to achieve improved local energy absorption and reduced grid dependence without additional energy storage equipment, and provides a unified passive energy storage effect for the four types of strategies.

[0028] Preferably, the preset temperature range is determined based on a preset acceptable range of the predicted average thermal sensation index.

[0029] Specifically, the preset temperature range is determined based on a preset acceptable range of the predicted average thermal sensation index. The predicted average thermal sensation index is used to characterize the human body's overall subjective feeling of the indoor thermal environment; the closer its value is to zero, the closer the thermal sensation is to a neutral state. In this embodiment, the acceptable range of the predicted average thermal sensation index is set to -1 to +1 to ensure that the indoor environment is within the range of thermal comfort acceptable to the human body.

[0030] Based on the acceptable range of the predicted average thermal sensation index, the air conditioner set temperature is constrained to ensure that it meets the requirements of passive energy storage regulation without exceeding the thermal comfort boundary. Specifically, the preset temperature range can be limited to 21°C to 28°C. Adaptive resetting of the air conditioner set temperature within this range can achieve load transfer and passive energy storage while maintaining the predicted average thermal sensation index within the acceptable range, thus balancing energy regulation effectiveness and indoor thermal comfort.

[0031] Preferably, after setting the temperature of the air conditioner in the target building according to the air conditioner's set temperature to regulate the room temperature, the method further includes: S5. Obtain the fan speed level based on the air conditioner set temperature, wherein the fan speed level is positively correlated with the air conditioner set temperature; The fan speed level characterizes the fan's operating level in the current control cycle and can be set to discrete levels such as low, medium, and high. This step uses the air conditioner set temperature as a trigger to establish a linkage between the fan and the set temperature, ensuring a positive correlation between the fan speed level and the set temperature; that is, the higher the set temperature, the higher the fan speed. The purpose is to maintain human thermal comfort by increasing indoor air velocity when raising the air conditioner set temperature to reduce the cooling load, thereby expanding the range of adjustable air conditioner set temperatures and improving the flexibility of adaptive control.

[0032] In practice, a mapping table between the set temperature and the fan speed level can be pre-established, and the fan speed level can be obtained by looking up the table based on the current air conditioner set temperature in each control cycle. For example, when the air conditioner set temperature is in the lower range of the preset temperature range, the fan speed level is set to low; when the air conditioner set temperature is in the middle range, the fan speed level is set to medium; and when the air conditioner set temperature is close to a higher temperature level, the fan speed level is set to high. The mapping table can also be implemented using a piecewise function, so that the fan speed level increases monotonically with the set temperature. To avoid frequent fan speed changes, a switching hysteresis can be set at the set temperature threshold, so that the original fan speed remains unchanged when the set temperature fluctuates near the threshold.

[0033] S6. Control the fan of the target building to rotate according to the fan speed level.

[0034] The control module outputs a speed control signal to the fan control unit, which then drives the fan motor according to the signal, causing the fan to rotate at the corresponding speed. For fans with multi-speed adjustment capabilities, the speed control signal can be a discrete speed command; for fans with stepless speed adjustment capabilities, the speed control signal can be a PWM duty cycle or a target speed value, and the fan control unit performs closed-loop speed regulation to achieve the target speed corresponding to the fan speed level.

[0035] In one specific embodiment, the fan speed level can be determined by the following formula: In the formula, T set Set the temperature for the air conditioner.

[0036] Through the aforementioned fan-linked control, during the control cycle when the air conditioner's set temperature increases, the fan operates at a higher speed to increase indoor air velocity and enhance heat dissipation from the human body. This counteracts the rising trend in perceived heat caused by the increased set temperature, maintaining indoor thermal comfort within an acceptable range. Conversely, during the control cycle when the air conditioner's set temperature decreases, the fan operates at a lower speed to avoid excessive cooling. Thus, the fan speed linkage mechanism, in conjunction with the air conditioner's set temperature adaptive reset mechanism, allows the system to improve local photovoltaic power consumption or reduce grid power purchases while simultaneously ensuring thermal comfort, further expanding the temperature regulation range allowed by passive energy storage control.

[0037] Example 2 See Figure 2Based on the scheme of Embodiment 1, in this embodiment, the temperature control criterion is selected as the matching relationship index between photovoltaic power generation and load. The matching relationship index is determined by the difference between photovoltaic power generation and real-time electricity load, and is used to characterize the surplus or deficiency of photovoltaic power supply relative to building electricity demand. By gradually setting the temperature reset based on the difference, the air conditioning set temperature is adjusted slightly with a preset adjustment step size in each control cycle, so that the air conditioning load gradually approaches the match with the photovoltaic supply and demand relationship, and avoids indoor thermal comfort fluctuations caused by large jumps in the set temperature.

[0038] Specifically, when the temperature control criterion includes the matching relationship index, the matching relationship index is determined based on the difference between the photovoltaic power generation and the real-time power load. Determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: S2-31. Obtain the initial set temperature for the current control cycle based on the air conditioning set temperature of the previous control cycle. The air conditioning setpoint temperature of the previous control cycle is the target temperature actually executed by the air conditioner in the previous cycle, reflecting the system's control state in the previous cycle. The initial setpoint temperature of the current control cycle is preferably directly inherited from the air conditioning setpoint temperature of the previous control cycle, ensuring the setpoint temperature is continuous in the time series and avoiding abrupt control changes caused by recalculating from a fixed temperature in each control cycle. The purpose of this step is to provide a reference value for gradual adjustment, allowing subsequent calculations of the adjustment direction and magnitude to be based on the currently executed setpoint temperature, thus forming a closed-loop update process iteratively according to the control cycle.

[0039] S2-32. Based on the matching relationship index and the preset adjustment step size, obtain the adjustment direction and adjustment range of the initial set temperature; The matching relationship index is derived from the difference between photovoltaic power generation and real-time electricity load. A positive difference indicates that photovoltaic output exceeds the load, resulting in a photovoltaic surplus; a negative difference indicates that photovoltaic output is less than the load, resulting in a photovoltaic deficit. The preset adjustment step size is used to limit the minimum adjustment granularity of the set temperature within each control cycle, ensuring that the adjustment is gradual rather than a one-time large jump.

[0040] This step transforms the supply-demand difference into the direction and magnitude of the temperature control action. The direction determines whether the set temperature needs to be increased or decreased, while the magnitude determines the specific amount of increase or decrease. In this embodiment, the magnitude can be directly set to a preset adjustment step size, ensuring consistent granularity in each adjustment, facilitating stable control.

[0041] S2-33. Adjust the initial set temperature according to the adjustment direction and the adjustment range to obtain the air conditioning set temperature for the current control cycle.

[0042] After determining the adjustment direction and magnitude, the initial set temperature is adjusted by addition and subtraction to obtain the air conditioning set temperature for the current control cycle. This set temperature is then used as the target control variable for the air conditioning within the current control cycle. The purpose of this step is to translate the supply-demand relationship criterion into an executable set temperature reset result, allowing the air conditioning load to gradually increase when there is a photovoltaic surplus to enhance local consumption, and gradually decrease when there is a photovoltaic shortage to reduce the need for electricity purchases. To ensure safety and comfort, the air conditioning set temperature for the current control cycle is preferably still limited to the preset temperature range to avoid exceeding the adjustment limits.

[0043] Preferably, obtaining the adjustment direction and adjustment range of the initial set temperature based on the matching relationship index and the preset adjustment step size includes: S2-321. When the difference between the photovoltaic power generation and the real-time power load is greater than the upper limit of the first preset balance range, the initial set temperature is reduced by the preset adjustment step size. The first preset balance range is used to characterize the approximate balance state between photovoltaic (PV) power and load within an acceptable error range. When the difference exceeds the upper limit of the balance range, it indicates that there is a significant surplus of PV output relative to the load, and without intervention, PV power transmission to other areas is more likely. In this case, the initial set temperature is reduced by the preset adjustment step size, which is equivalent to lowering the set temperature to increase cooling demand, allowing the air conditioner to absorb more electricity in subsequent control cycles, thereby improving the local consumption of PV power.

[0044] This strategy employs a gradual reduction in temperature over small steps, which makes the load increase process smoother, reduces sudden changes in indoor thermal sensation caused by abrupt temperature drops, and facilitates the gradual absorption of cold energy by the building envelope and interior components to form cold energy storage.

[0045] S2-322. When the difference between the photovoltaic power generation and the real-time power load is less than the lower limit of the first preset balance range, the initial set temperature is increased by the preset adjustment step size. When the difference is less than the lower limit of the balance range, it indicates that the photovoltaic output is significantly insufficient relative to the load, and the building's dependence on grid power purchases increases. In this case, increasing the initial set temperature by the preset adjustment step size is equivalent to raising the set temperature to reduce cooling demand, causing the air conditioning load to decrease in subsequent control cycles, thereby reducing grid power purchases and improving the system's adaptability to photovoltaic supply and demand fluctuations.

[0046] By gradually increasing the set temperature, the indoor temperature changes can be made more gradual, and the cold energy stored in the building envelope and indoor components during the earlier lower set temperature stage can be used to support the indoor thermal environment during the set temperature increase stage, thus demonstrating the load transfer function of passive energy storage.

[0047] S2-323. When the difference between the photovoltaic power generation and the real-time power load is within the first preset balance range, the initial set temperature is kept constant. When the difference falls within the first preset equilibrium range, it indicates that the photovoltaic output and the building load are approximately matched within the allowable deviation range. If the set temperature is adjusted further at this point, frequent temperature fluctuations due to small disturbances can easily occur, leading to control oscillations and reduced comfort stability. Therefore, maintaining the initial set temperature allows the system to enter a stable holding state within the equilibrium range.

[0048] This hold logic is equivalent to introducing a dead zone in the supply and demand matching criterion, which can significantly suppress frequent temperature switching caused by instantaneous fluctuations, metering noise, or small load changes, thereby improving the stability and deployability of the control.

[0049] The upper and lower limits of the first preset balance interval are located on both sides of the first preset balance threshold.

[0050] The first preset balance threshold is used to characterize the ideal balance point between photovoltaic power and load. The upper and lower limits of the first preset balance interval are located on both sides of the first preset balance threshold, forming an allowable error band around the balance point. By setting the balance interval, the control strategy does not respond to small supply and demand deviations, but performs gradual corrections for significant deviations exceeding the interval, thus balancing responsiveness and stability. In a specific embodiment, the preset balance threshold is set to 0.

[0051] Example 3 See Figure 3 Based on the scheme of Example 1, the temperature control criterion is selected as a matching relationship index, which is determined based on the ratio of photovoltaic power generation to real-time electricity load. The ratio characterizes the relative sufficiency of photovoltaic supply relative to load demand; a larger ratio indicates a relatively sufficient photovoltaic supply, while a smaller ratio indicates a relatively insufficient photovoltaic supply. Ratio-based zoned regulation can quickly adjust the set temperature when the supply-demand deviation is large, and maintain a stable set temperature when supply and demand are close to equilibrium, thus balancing response speed and control stability.

[0052] Specifically, when the temperature control criterion includes the matching relationship index, the matching relationship index is determined based on the ratio of the photovoltaic power generation to the real-time power load. Determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: S3-31. Obtain the initial set temperature for the current control cycle based on the air conditioning set temperature of the previous control cycle. The air conditioning setpoint temperature of the previous control cycle is the target temperature that the air conditioner operated at in the previous cycle. The initial setpoint temperature of the current control cycle is preferably taken from the air conditioning setpoint temperature of the previous control cycle, ensuring continuity of control over time and avoiding abrupt changes caused by recalculating from a fixed reference temperature in each control cycle. This step provides a reference value for subsequent adjustments to the setpoint temperature based on proportional indicators, allowing the temperature reset to gradually converge to a state that is compatible with the photovoltaic supply and demand relationship during continuous iteration.

[0053] S3-32. Determine the adjustment direction and adjustment range of the initial set temperature according to the preset ratio range to which the matching relationship index belongs; The matching relationship index is a proportional index, preferably expressed as the ratio of photovoltaic power generation to real-time electricity load. This ratio reflects the relative magnitude of photovoltaic supply and load demand, and is used to determine whether the air conditioning set temperature should be increased or decreased, and by how much.

[0054] This step maps proportional indicators to control actions, preferably through a preset proportional range. This involves dividing the range of proportional indicators into multiple intervals and configuring a corresponding adjustment direction and / or adjustment amplitude for each interval. This interval-based mapping transforms continuous proportional changes into finite-level set temperature adjustments, facilitating engineering implementation and reducing control jitter caused by frequent small fluctuations.

[0055] S3-33. Adjust the initial set temperature according to the adjustment direction and the adjustment range to obtain the air conditioning set temperature for the current control cycle.

[0056] After determining the adjustment direction and magnitude, the initial set temperature is adjusted by addition or subtraction to obtain the air conditioning set temperature for the current control cycle, which serves as the target temperature for the air conditioning operation in the current control cycle. Through this update method, when the proportional indicator indicates relatively abundant photovoltaic power, the set temperature is adjusted downwards to increase the cooling load and enhance local photovoltaic power consumption; conversely, when the proportional indicator indicates relatively insufficient photovoltaic power, the set temperature is adjusted upwards to reduce the cooling load and decrease grid power purchases. The air conditioning set temperature for the current control cycle is preferably still limited to the preset temperature range to ensure comfort and controllability.

[0057] Preferably, determining the adjustment direction and adjustment range of the initial set temperature based on the preset ratio range to which the matching relationship index belongs includes: S3-321. Divide the value range of the matching relationship index into at least one second preset balance interval and several preset ratio intervals, with different adjustment directions and / or adjustment ranges for different preset ratio intervals. The second preset balance interval is used to represent a state where photovoltaic supply and load demand are approximately balanced. Its center is characterized by a second preset balance threshold. The upper and lower limits of the second preset balance interval are distributed on both sides of this threshold, forming an allowable deviation band. Several preset ratio intervals are distributed on both sides of the balance interval to represent different degrees of photovoltaic surplus or photovoltaic deficit.

[0058] The adjustment direction and / or adjustment range are different for different preset ratio ranges. This means that when the photovoltaic power is relatively abundant, the set temperature is lowered, and when the photovoltaic power is relatively insufficient, the set temperature is raised. A larger adjustment range can be configured when the degree of deviation from the balance increases, so as to improve the control responsiveness.

[0059] S3-322. When the matching relationship index is within the second preset balance range, the initial set temperature remains unchanged; When the proportional indicator falls into the second preset equilibrium range, it indicates that the photovoltaic supply and load demand are relatively matched within the allowable deviation range. At this point, continuing to adjust the set temperature can easily lead to ineffective regulation and may cause frequent temperature rises and falls due to metering errors or instantaneous fluctuations. By keeping the initial set temperature unchanged, it is equivalent to setting a dead zone on the proportional indicator, allowing the system to remain stable in a near-equilibrium state, improving control stability and reducing frequent operation of the air conditioner and controller.

[0060] S3-323. When the matching relationship index is located in any of the preset ratio ranges, the adjustment direction and adjustment range of the initial set temperature are determined according to the preset ratio range to which the matching relationship index belongs. When the proportional index falls within any preset proportional range outside the equilibrium range, it indicates that the system is in a state of significant photovoltaic surplus or significant photovoltaic deficit. In this case, the initial set temperature is adjusted by selecting the appropriate adjustment direction and amplitude based on the range to which the proportional index belongs.

[0061] For example, when the ratio index falls into the range indicating relatively abundant photovoltaic power, the direction of lowering the set temperature is chosen to increase the cooling load, thereby enhancing local photovoltaic power consumption; when the ratio index falls into the range indicating relatively insufficient photovoltaic power, the direction of raising the set temperature is chosen to reduce the cooling load, thereby reducing the amount of electricity purchased from the grid. The adjustment range can be set to multiple levels according to the range level, so that the more obvious the deviation from the balance, the larger the single-cycle temperature adjustment, thereby improving the tracking capability.

[0062] The upper and lower limits of the second preset balance range are located on both sides of the second preset balance threshold, and the adjustment range is positively correlated with the degree of deviation between the preset ratio range and the second preset balance threshold.

[0063] The second preset balance threshold corresponds to the ideal matching point, and the second preset balance interval sets an allowable error band around this threshold. The adjustment amplitude and the degree of deviation between the preset ratio interval and the second preset balance threshold are positively correlated, indicating that the farther the ratio index is from the balance threshold, the greater the supply-demand mismatch, and the greater the adjustment amount of the set temperature within a single control cycle. In a specific embodiment, the second preset balance threshold is 1.

[0064] This mechanism allows for small adjustments to avoid over-control in cases of minor mismatch, and larger, faster corrections in cases of severe mismatch, improving the system's adaptability to photovoltaic fluctuations and load changes, while reducing the lag problem that requires multiple control cycles to bring the system back to balance.

[0065] Example 4 Based on the scheme of Example 1, in this Example 4, the temperature control criterion is selected as the photovoltaic power generation power itself, without relying on real-time electricity load for judgment. The photovoltaic power ratio is obtained by normalizing the ratio of the photovoltaic power generation power in the current control cycle to the reference photovoltaic power under standard operating conditions. Then, based on the preset output level to which the photovoltaic power ratio belongs, a target temperature level is selected from multiple preset temperature levels as the air conditioning set temperature. This method directly maps the photovoltaic output level to the air conditioning set temperature, is logically simple, has low implementation cost, and is easy to deploy in scenarios where only photovoltaic power acquisition is available.

[0066] Specifically, when the temperature control criterion includes the photovoltaic power generation, determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: S4-31. Obtain the photovoltaic power ratio based on the ratio of the photovoltaic power generation power to the reference photovoltaic power under standard operating conditions; Photovoltaic power generation refers to the real-time output of the target building's photovoltaic system during the current control period, reflecting the current scale of electricity available for local consumption. Reference photovoltaic power is the baseline output under standard operating conditions, used to standardize the photovoltaic power output across different dates, installed capacities, or weather conditions. By comparing the real-time power with the reference power, a dimensionless photovoltaic power ratio can be obtained, ensuring that subsequent output level classification and temperature level selection are not affected by absolute power magnitude. This facilitates the reuse of the same set of control parameters across different buildings or photovoltaic installation scales.

[0067] In practice, the reference photovoltaic power can be preset as the rated output power of the photovoltaic system under standard operating conditions, or as the typical peak power obtained from historical statistics, or it can be configured according to the nominal capacity of the photovoltaic system. The photovoltaic power ratio is preferably calculated as follows: the photovoltaic power ratio is obtained by dividing the photovoltaic power generation in the current control cycle by the reference photovoltaic power, and the ratio value can be limited to fall within the range of zero to one or zero to a preset upper limit to avoid distortion of control commands caused by abnormal sampling.

[0068] In one specific embodiment, the photovoltaic power ratio is calculated using the following formula: In the formula, The photovoltaic power ratio is mentioned above. Instantaneous photovoltaic power generation, η represents the total solar radiation incident on the surface of the photovoltaic module under the condition of measuring reference photovoltaic efficiency, in W / m²; A represents the area of ​​the photovoltaic array, in m²; η represents the rated efficiency of the photovoltaic module.

[0069] S4-32. Based on the photovoltaic power ratio, select the target temperature level corresponding to the photovoltaic power ratio from multiple preset temperature levels as the air conditioning set temperature for the current control cycle.

[0070] Multiple preset temperature levels are used to provide discrete candidate values ​​for a set temperature. Each candidate value lies within a preset temperature range and may include at least a first temperature level and a second temperature level. By dividing the photovoltaic power ratio into multiple preset output level intervals, a mapping from photovoltaic output level to set temperature level is achieved.

[0071] Specifically, pre-set rules for dividing photovoltaic power ratio into ranges are used. For example, the photovoltaic power ratio is divided into low-output, medium-output, and high-output ranges, corresponding to higher, middle, and lower temperature levels, respectively. When the photovoltaic power ratio is in the high-output range, a lower temperature level is selected as the air conditioning set temperature to increase the cooling load and enhance the local absorption capacity of photovoltaic power. When the photovoltaic power ratio is in the low-output range, a higher temperature level is selected as the air conditioning set temperature to reduce the cooling load and reduce grid power purchases. When the photovoltaic power ratio is in the medium-output range, a middle temperature level is selected to balance comfort and energy consumption.

[0072] The above-mentioned tiered selection mechanism enables the air conditioner's set temperature to be quickly adjusted according to changes in photovoltaic output. This simplifies control and avoids frequent minor changes caused by continuous adjustment, thereby improving deployability and operational stability.

[0073] Example 5 Based on the scheme of Embodiment 1, in this Embodiment 5, the temperature control criterion is selected as time-of-use electricity price information. By identifying whether the current control cycle is in a preset peak period, the air conditioner set temperature is differentially reset. The core purpose of this embodiment is to use the time-series characteristics of electricity prices to guide the transfer of air conditioning load in the time dimension, reduce the electricity demand of air conditioning during high electricity price periods, and start cooling operation in advance during low electricity price periods. Thus, without introducing additional energy storage equipment, and in conjunction with the thermal inertia of the building envelope and / or interior components, peak shaving and passive energy storage can be achieved.

[0074] Specifically, when the temperature control criterion includes the time-of-use electricity price, determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: S5-31. Based on the time-of-use electricity price information, determine whether the current control cycle belongs to a preset peak period; Time-of-use (TOU) pricing information is used to characterize the price level of electricity at different times of the day. It is usually preset by the power sector or energy management system and at least distinguishes between peak and off-peak periods. The preset peak period is a high-price time interval determined according to the TOU rules of the target building's area, and its start and end times can be stored in the control module in advance.

[0075] Within the current control cycle, the control module acquires the corresponding time information and compares it with the start and end times of the preset peak period. If the current control cycle's time falls within the preset peak period, it is determined that the current control cycle is within the preset peak period; otherwise, it is determined that the current control cycle is not within the preset peak period. This judgment process identifies the electricity price attributes of the current operating period, providing a basis for subsequent temperature reset settings.

[0076] S5-32. If the current control cycle is in the preset peak period, reset the air conditioner set temperature to the first preset temperature value; When the current control cycle is determined to be within a preset peak period, the control module resets the air conditioner's set temperature to the first preset temperature value. The first preset temperature value is a higher temperature level, and its purpose is to reduce the cooling demand of the air conditioner, thereby reducing electricity consumption and electricity purchase costs during periods of high electricity prices.

[0077] Under this control state, the air conditioner operates at a higher set temperature, and the compressor's cooling load decreases accordingly. Simultaneously, the cooling capacity stored in the target building during off-peak hours by operating at a lower set temperature can support the indoor thermal environment during peak hours through the release of cold from the building envelope and / or interior components, keeping indoor temperature fluctuations within an acceptable range. Thus, peak-hour electricity consumption is reduced while maintaining the stability of the indoor thermal environment.

[0078] S5-33. If the current control cycle is not in the preset peak period, reset the air conditioner set temperature to the second preset temperature value; The first preset temperature value is higher than the second preset temperature value.

[0079] When the current control cycle is determined not to be within the preset peak period, the control module resets the air conditioning set temperature to a second preset temperature value. The second preset temperature value is a lower temperature level, and its function is to enhance the air conditioning cooling operation during off-peak periods, so that the building envelope and / or interior components absorb cold energy and form cold energy storage.

[0080] By initiating cooling operations during off-peak hours, some cooling load can be shifted from high-electricity-price periods to low-electricity-price periods, thus creating conditions for load reduction during subsequent peak hours. This cooling capacity is stored and gradually released as the set temperature rises during peak hours to maintain the indoor thermal environment, demonstrating a passive energy storage and control mechanism based on building thermal inertia.

[0081] The first preset temperature value is higher than the second preset temperature value, which is used to create a clear set temperature difference between peak and off-peak periods. Both are preferably within the preset temperature range to ensure that thermal comfort constraints are not exceeded during the implementation of time-of-use pricing-oriented temperature reset.

[0082] The aforementioned binary temperature reset strategy based on time-of-use pricing enables air conditioning operation to match the timing of electricity prices. Under the premise of simple control logic and low implementation cost, it achieves load shifting and passive energy storage effects, and can serve as a basic control mode or alternative implementation method for more complex photovoltaic matching strategies.

[0083] Example 6 Based on the solutions in embodiments 1-5 above, this embodiment presents a photovoltaic air conditioning temperature setting method that combines multiple control strategies. Its purpose is to achieve a balance between photovoltaic self-consumption, energy efficiency optimization, and economic costs. By integrating strategies based on photovoltaic output levels, time-of-use pricing, and the difference between photovoltaic power generation and load, this system can dynamically adjust the air conditioning set temperature according to different photovoltaic power generation conditions, building load demands, and electricity price timing, thereby improving system operating efficiency and comfort.

[0084] Because the output of photovoltaic power generation systems is highly volatile, and building load demands vary significantly across different time periods, a single control strategy often cannot meet the complex needs of actual operation. Therefore, combining multiple strategies to adapt to different operating scenarios and changing demands is key to achieving efficient system operation.

[0085] Specifically, the strategies in Examples 2 to 5 focus on tiered control of photovoltaic output levels, gradual adjustment of the difference between photovoltaic output and load, and proportional setting adjustment of the photovoltaic-to-load ratio, respectively. Each control strategy has its specific advantages and applicable scenarios. When used in combination, the air conditioning temperature setting can be flexibly adjusted under different photovoltaic power generation conditions, building load demands, and electricity price periods to optimize system performance.

[0086] In practical applications, these control strategies can be effectively integrated based on different photovoltaic power generation conditions and electricity price schedules to improve photovoltaic self-consumption capacity, reduce grid power purchase demand, and optimize system energy efficiency. The following are some possible strategy integration methods, presented as examples rather than limitations: The strategy in Example 2 adjusts the air conditioning temperature gradually based on the difference between photovoltaic (PV) power generation and building load, flexibly responding to fluctuations in both. It achieves precise temperature control, maintaining a balance between air conditioning load and PV power generation, and avoiding drastic indoor temperature fluctuations caused by excessive adjustments. The strategy in Example 4 divides the air conditioning setpoint into multiple fixed temperature levels based on real-time PV power generation. When PV power generation is sufficient, a lower temperature level is selected to increase PV self-consumption; conversely, when PV power generation is insufficient, a higher temperature level is selected to reduce grid power purchases. This strategy has strong responsiveness and stability, but due to the dispersion of its temperature setpoint, it cannot adjust the temperature as precisely as a progressive strategy.

[0087] Combining the progressive setpoint step strategy of Embodiment 2 and the tiered setpoint strategy of Embodiment 4, when the temperature control criterion includes both photovoltaic power generation and the matching relationship index, and the matching relationship index is determined based on the difference between photovoltaic power generation and real-time building load, a dynamic threshold mechanism is introduced. When the difference is small, the system prioritizes the tiered setpoint strategy to quickly respond to load demand; while when the difference exceeds the preset dynamic threshold range, the system switches to a progressive adjustment strategy to more finely adjust the temperature setting. This innovative threshold adjustment mechanism enables the system to quickly respond to load demand when photovoltaic power generation is stable, and to optimize energy consumption through progressive adjustment when the difference between photovoltaic power generation and load is large.

[0088] Secondly, combining the progressive setpoint stepping strategy of Embodiment 2 and the proportional setpoint stepping strategy of Embodiment 3, when the temperature control criterion simultaneously includes photovoltaic power generation and the matching relationship index, and the matching relationship index is determined based on the difference between photovoltaic power generation and building load, the system introduces a multi-level response control mechanism. When the difference is small, the progressive setpoint stepping strategy is used to finely adjust the temperature; while when the difference exceeds a preset threshold, the system automatically switches to the proportional setpoint stepping strategy to adjust the temperature through larger steps. This multi-level response mechanism ensures the system's temperature control accuracy when load fluctuations are small, and can quickly restore the matching between photovoltaic power generation and load when load fluctuations are large.

[0089] Furthermore, combining the progressive setpoint stepping strategy of Embodiment 2 and the proportional setpoint stepping strategy of Embodiment 3, when the temperature control criterion simultaneously includes photovoltaic power generation and the matching relationship index, and the matching relationship index is determined based on the difference between photovoltaic power generation and the building's real-time load, and this difference exceeds a third preset threshold, the proportional setpoint stepping strategy is used to quickly adjust the air conditioning temperature; when the difference does not exceed the third preset threshold, a progressive strategy is used to adjust the temperature. This combination allows for flexible adjustment of the air conditioning temperature based on the magnitude of the difference between photovoltaic power generation and the load, ensuring precise matching between photovoltaic self-consumption and building load.

[0090] Finally, combining the tiered setpoint strategy of Example 4 and the time-of-use pricing setpoint switching strategy of Example 5, i.e., when the temperature control criterion simultaneously includes photovoltaic power generation and the matching relationship index, and the matching relationship index is determined based on the ratio or difference between photovoltaic power generation and building load, the system introduces a dynamic adjustment mechanism based on energy consumption optimization. When photovoltaic power generation is sufficient and the load is low, the tiered setpoint strategy is used for rapid response to improve photovoltaic self-consumption; while when photovoltaic power generation is insufficient, the system automatically switches to the time-of-use pricing strategy to reduce cooling demand during peak electricity price periods and reduce grid power purchases. This dynamic adjustment mechanism ensures the system's flexibility and enables optimized operation under different electricity price periods and photovoltaic power generation conditions.

[0091] Example 7 In this embodiment, to verify the feasibility and effectiveness of the photovoltaic air conditioning room temperature adaptive control method based on passive energy storage described in Embodiments 1 to 5, a simulation platform for the target building is constructed, and a comparative analysis and effect evaluation of the set temperature reset strategy under different temperature control criteria are conducted based on the simulation platform.

[0092] On the one hand, a simulation model is established to couple the photovoltaic power generation and building electricity consumption of the target building. The simulation model includes at least an air conditioning model, a ceiling fan model, a roof photovoltaic array model, a building model, and control strategy components. Each model is coupled and calculated at the same time step, which is set to 5 minutes.

[0093] Using the fixed-set temperature operation mode of the air conditioner as the baseline, multiple sets of comparative operating conditions are formed by introducing the differential progressive adjustment strategy of Example 2, the proportional zoning adjustment strategy of Example 3, the photovoltaic power ratio tiering strategy of Example 4, and the time-of-use electricity price binary reset strategy of Example 5 under the same meteorological and power consumption conditions. Each comparative operating condition iteratively updates the set temperature according to the control cycle, and ensures that the air conditioner set temperature is constrained by the preset temperature range to reflect the actual deployable operating boundary.

[0094] On the other hand, performance verification indicators were set for quantitative evaluation. In terms of energy, changes in the building's electricity consumption from the grid and related indicators of local photovoltaic (PV) consumption were statistically analyzed to verify the degree to which temperature resetting improved the timing matching of PV and load. In terms of economy, electricity costs were calculated and payback period changes were estimated using time-of-use pricing information to verify the strategy's contribution to economic efficiency. In terms of comfort, the predicted average thermal comfort index was used to assess indoor thermal comfort, verifying that the indoor thermal environment remained within an acceptable range while improving local PV consumption. In terms of passive energy storage, the cold storage and release characteristics of the building envelope were statistically analyzed to verify whether the absorption and release of cold by the building envelope and / or interior components at different set temperature stages resulted in effective load transfer.

[0095] Specifically, the air conditioning model adopts the performance curve model of a split-type air conditioner. It is used to calculate the total cooling capacity correction coefficient and energy consumption input correction coefficient of the air conditioner when the outdoor dry-bulb temperature, evaporator inlet wet-bulb temperature, and partial load rate change. Combined with the partial load rate correction relationship, it obtains the cooling capacity and power consumption of the air conditioner at the current time step. Specifically, the total cooling capacity correction coefficient F... cap With energy input correction factor F EIR It can be calculated using the following formula: To ensure the model's applicability across different temperature ranges, the operating point can be divided into high-temperature and low-temperature regions using a boundary equation. This boundary equation can be determined according to the following relationship. Furthermore, a partial load factor correction coefficient (PFPLR) is introduced to describe the energy consumption correction of the air conditioner when it is not operating at full load. The PPFLR can be determined according to the following formula: Among them, Fcap F represents the total cooling capacity correction factor; EIR T represents the energy input ratio correction factor; in-wb T represents the wet-bulb temperature of the air at the evaporator inlet, in °C. amb The outdoor air dry-bulb temperature is expressed in °C; PLR represents the partial load factor, which is the ratio of actual cooling demand to the maximum cooling capacity of the air conditioner; EIR represents the energy input ratio; PFPLR represents the correction factor for PLR; Boundary represents the boundary equation used to divide the high-temperature zone and the low-temperature zone. Coefficients a to f, as well as a1 to a4 and b1 to b4, are empirical coefficients obtained based on the characteristics of the air conditioning system.

[0096] Based on the above air conditioning model, the simulation platform reads T during each control cycle. in-wb T amb as well as PLR The cooling capacity and power consumption at the corresponding time step are calculated based on the above formula, thereby realizing the dynamic simulation of energy consumption and cooling capacity output on the air conditioning side, and providing a consistent calculation basis for the subsequent comparison of the effects of different temperature setting strategies.

[0097] The ceiling fan model is designed to establish the relationship between the power of a brushless DC ceiling fan and the average indoor wind speed. The relationship satisfies the form of a cubic function, and the wind speed is discretized into three levels: low, medium, and high, to compensate for thermal comfort under different temperature settings.

[0098] The cubic function is shown below: In the formula, v represents the indoor air velocity in m / s; Pcf represents the energy consumption of the ceiling fan in W. Based on the calculation results, the power ratings of ceiling fans corresponding to different average indoor air velocities are listed in Table 1.

[0099] Table 1. Indoor air velocity and energy consumption at different fan speed levels.

[0100]

[0101] The building model and thermal parameter settings are based on a typical medium-sized office building. A three-story building model is established, with each floor divided into five thermal zones. Since the zoning of each floor is consistent, this embodiment uses a standard floor for scaled-down modeling, with each floor having a building area of ​​96 m². The building geometry model is first built in SketchUp and then imported into the TRNSYS Type56 module. The thermal parameters of the building envelope are set according to local design standards, as shown in Table 2, including at least the multi-layer structure of the exterior walls and window parameters.

[0102] Table 2 Thermal performance of office building envelope

[0103] The equipment power density is set at 15 W / m², the lighting power density at 6 W / m², and the personnel density at 1 person / 10 m². Weekday operating hours are Monday to Friday, 08:30 to 18:00, with a typical lunch break from 12:00 to 14:00 during which the internal load is reduced. The cooling season is set from March 8th to November 28th. Meteorological data uses typical Guangzhou meteorological year data as input for outdoor temperature and solar irradiance.

[0104] The rooftop photovoltaic array is built using TRNSYS Type 562d modules. Based on the functional relationship between photovoltaic efficiency and cell temperature and irradiance, the photovoltaic output power at each moment is calculated. Electrical conversion parameters such as inverter efficiency are superimposed to obtain the real-time photovoltaic output power that can be used for building load matching.

[0105] The mathematical model for photovoltaic module efficiency is shown below: Where η represents the overall efficiency of the photovoltaic array; η T,coef η represents the coefficient describing the change in photovoltaic array efficiency with temperature. I,coef T represents the coefficient describing the variation of photovoltaic array efficiency with incident solar radiation; PV T represents the temperature of a photovoltaic cell, expressed in °C. ref This indicates the cell temperature under measured reference photovoltaic efficiency conditions, in °C; I T,ref This represents the total solar radiation incident on the surface of the photovoltaic collector under the condition of measuring the reference photovoltaic efficiency, expressed in W / m². Table 3 lists the specific parameters of the photovoltaic power generation system.

[0106] Table 3 Photovoltaic Parameters for Office Buildings

[0107] The four control strategies described in the previous embodiments are embedded into TRNSYS as self-developed control components. The relationship between photovoltaic power and total building load in the previous time step is read at every 5-minute time step, and the air conditioning temperature setpoint and ceiling fan speed are output. In order to suppress frequent resets caused by small fluctuations, a temperature setpoint reset dead zone is set in the control logic, and the setpoint variation range is limited to 21°C to 28°C.

[0108] This embodiment sets a baseline operating condition and four control strategy operating conditions: The baseline operating condition is that the air conditioning temperature setting is fixed and the ceiling fan is not enabled; Strategy I is the time-of-use pricing-based setting switching described in Example 5; Strategy II is the tiered setting based on photovoltaic output level described in Example 4; Strategy III is the progressive setting step based on the difference between photovoltaic and load as described in Example 2; Strategy IV is the proportional setting step based on the photovoltaic-to-load ratio as described in Example 3, with a step size of ±0.5℃, ±1.0℃, or ±1.5℃.

[0109] Evaluation indicators and statistical standards include energy performance indicators, economic indicators, thermal comfort indicators, and cold storage and release analysis indicators of building envelope; Among them, the photovoltaic self-consumption rate represents the proportion of photovoltaic power generation directly consumed by buildings, and is used to characterize the effectiveness of on-site photovoltaic consumption; the photovoltaic self-sufficiency rate is used to quantify the degree to which a building's total energy consumption demand is met by its own photovoltaic power generation, and is used to reflect the building's electricity independence. The photovoltaic self-consumption rate (SCR) and photovoltaic self-sufficiency rate (SSR) are calculated by the following formulas: in, Photovoltaic power directly consumed by building load (unit: kilowatt-hour); This is the total power generation of the photovoltaic system (unit: kilowatt-hours). It is the building's total electricity consumption (including air conditioning, lighting, electrical equipment, and ceiling fans, etc.) (unit: kilowatt-hour).

[0110] The energy balance model of the external wall and the efficiency of external wall cold energy storage and release are calculated using the following formulas: Among them, DQ wall Q represents the change in energy within the surface of a wall, expressed in W. comi Q represents the total heat flux towards the interior ("+" for inflow into the interior, "-" for outflow into the wall), in W; como Q represents the total heat flux to the outside ("-" for flow to the outside, "+" for flow into the wall), in W; radgi Q represents the total radiant heat gain on the interior wall surface, expressed in W. radgo η represents the total radiant heat gain on the exterior wall surface, expressed in W. cool The efficiency of cold storage and release of external wall is represented by Q. comi The negative value of DQ wall The ratio of negative values.

[0111] Economic performance is primarily assessed by analyzing total annual operating costs and the payback period of the photovoltaic-driven air conditioning system. The calculation formula is as follows: Where Cop represents the annual operating cost, in yuan; P grid-i E represents the amount of electricity purchased from the grid at time step i, in kWh; λi represents the electricity price at time step i, in yuan / kWh; i represents the total number of time steps in the year; export This represents the total surplus photovoltaic power transmitted to the grid, expressed in kWh; F ITexport The unit represents the grid-connected electricity price for surplus photovoltaic power, expressed in yuan / kWh; I represents the initial investment cost of the photovoltaic-driven air conditioning system, including components such as photovoltaic modules, controllers, and inverters, expressed in yuan; P P This indicates the payback period for a photovoltaic-driven air conditioning system, expressed in years.

[0112] Thermal comfort levels are quantitatively assessed using the PMV index, defined by ISO 7730 (International Organization for Standardization, 2005). PMV is a widely accepted indicator of thermal comfort, used to predict the average thermal sensation of a large population on a seven-level scale, ranging from -3 (cold) to +3 (hot), where 0 represents a neutral thermal sensation. Generally, in indoor environments, a PMV value between -1 and +1 is considered acceptable for thermal comfort.

[0113] During the cooling season, the simulation results of the baseline operating condition and four control strategies are summarized in Table 4. Table 4 includes at least the following indicators: photovoltaic power generation, building energy consumption, surplus photovoltaic power fed into the grid, local photovoltaic consumption, grid-purchased electricity, photovoltaic self-consumption rate, and photovoltaic self-sufficiency rate. The baseline operating condition is a fixed air conditioning temperature setpoint with ceiling fans not activated. Strategy I corresponds to the time-of-use pricing-based setpoint switching scheme described in Example 5; Strategy II corresponds to the tiered setpoint scheme based on photovoltaic output level described in Example 4; Strategy III corresponds to the progressive setpoint stepping scheme based on the difference between photovoltaic and load described in Example 2; and Strategy IV corresponds to the proportional setpoint stepping scheme based on the photovoltaic-to-load ratio described in Example 3, where the step size of the proportional setpoint stepping scheme can be ±0.5℃, ±1.0℃, or ±1.5℃.

[0114] Table 4. Simulation results of the baseline case and four control strategies.

[0115]

[0116] As shown in Table 4, among the four control strategies, strategies IV and III perform better in terms of photovoltaic self-consumption rate and self-sufficiency rate. Compared with the baseline condition, strategy III, which adopts the progressive setpoint stepping scheme described in Example 2, increases the photovoltaic self-consumption rate and self-sufficiency rate by 14.1% and 16.5%, respectively; strategy IV, which adopts the proportional setpoint stepping scheme described in Example 3, increases the photovoltaic self-consumption rate and self-sufficiency rate by 14.4% and 16.5%, respectively, thereby enhancing the local consumption of photovoltaic power and reducing the transmission of surplus photovoltaic power to the grid. Strategy II, which adopts the tiered setpoint scheme described in Example 4, has a smaller increase in photovoltaic self-consumption rate, but it has a certain effect on improving the self-sufficiency rate, and correspondingly, the transmission of surplus photovoltaic power to the grid decreases. In contrast, strategy I, which adopts the time-of-use pricing setpoint switching scheme described in Example 5, has a photovoltaic self-consumption rate lower than the baseline condition, the self-sufficiency rate does not change significantly, and the transmission of surplus photovoltaic power to the grid increases by 25.1% compared with the baseline condition. The reason is that there is a mismatch between the time-of-use electricity price period and the photovoltaic power generation output curve in the time dimension. This causes the scheme to reduce the cooling load during the peak electricity price period, reduce the local consumption of photovoltaic power, and increase the surplus electricity fed into the grid. However, when the cooling load is increased during the off-peak electricity price period, the photovoltaic output is insufficient to cover the demand, and the electricity purchased by the grid increases accordingly.

[0117] In terms of energy consumption, all four control strategies achieved an overall reduction in energy consumption compared to the baseline operating condition. Among them, Strategy I, employing the time-of-use pricing setpoint switching scheme described in Example 5, and Strategy II, employing the tiered setpoint scheme described in Example 4, showed the most significant reductions, decreasing total energy consumption by 7.0% and 8.1%, respectively. The total energy consumption of Strategy III, employing the progressive setpoint stepping scheme described in Example 2, and Strategy IV, employing the proportional setpoint stepping scheme described in Example 3, was also lower than the baseline operating condition, but slightly higher than Strategy I and Strategy II. Based on the indicators in Table 4, the progressive setpoint stepping scheme described in Example 2 and the proportional setpoint stepping scheme described in Example 3 achieve a better balance between energy saving, reducing grid power purchases, and improving local photovoltaic consumption. They are more suitable for demonstrating the comprehensive effect of passive energy storage-based room temperature adaptive control in improving local photovoltaic consumption and reducing grid dependence.

[0118] like Figure 4As shown, the monthly energy characteristics of the baseline operating condition and four control strategies during the cooling season are compared. The monthly energy characteristics include at least the following indicators: photovoltaic power generation, local photovoltaic consumption, grid-purchased electricity, grid connection of surplus photovoltaic electricity, and air conditioning energy consumption. The baseline operating condition is a fixed air conditioning temperature setpoint with ceiling fans not activated. Strategy I corresponds to the time-of-use pricing-based setpoint switching scheme described in Example 5; Strategy II corresponds to the tiered setpoint scheme based on photovoltaic output level described in Example 4; Strategy III corresponds to the progressive setpoint stepping scheme based on the difference between photovoltaic and load described in Example 2; and Strategy IV corresponds to the proportional setpoint stepping scheme based on the photovoltaic-to-load ratio described in Example 3.

[0119] Depend on Figure 4 It can be seen that when using Strategy III of the progressive setpoint stepping scheme described in Example 2 and Strategy IV of the proportional setpoint stepping scheme described in Example 3, the local photovoltaic consumption in each month is generally higher than the baseline operating condition, and the increase covers multiple monthly ranges. Specifically, Strategy III increases the monthly local photovoltaic consumption by 3.9 to 38.4, and Strategy IV increases it by 4.1 to 38.6, indicating that the setpoint stepping update driven by the matching relationship index can more fully absorb photovoltaic output in most months, thereby reducing the transmission of surplus electricity to the grid. In contrast, when using Strategy I of the time-of-use pricing setpoint switching scheme described in Example 5, the monthly local photovoltaic consumption is basically equivalent to the baseline operating condition, with some months showing a slight decrease compared to the baseline operating condition.

[0120] Regarding grid-purchased electricity, the monthly grid-purchased electricity volume using Strategy II (tiered setpoint scheme as described in Example 4), Strategy III (progressive setpoint step scheme as described in Example 2), and Strategy IV (proportional setpoint step scheme as described in Example 3) was significantly lower than the baseline operating condition. From May to October, the average grid-purchased electricity volume under these three schemes decreased by 39.8%, 42.0%, and 42.3%, respectively, demonstrating that adaptive setpoint reset can more effectively reduce reliance on grid-purchased electricity in months with strong photovoltaic output and high air conditioning load. In contrast, Strategy I (time-of-use pricing setpoint switching scheme as described in Example 5) showed a relatively limited reduction in grid-purchased electricity volume, with an average reduction of 11.6%, indicating that on a monthly scale, this scheme's ability to match photovoltaic output time-series changes is weaker than the setpoint step or tiered schemes based on the relationship between photovoltaic and load.

[0121] like Figures 5a to 5b As shown, the monthly photovoltaic self-consumption rate and monthly photovoltaic self-sufficiency rate under the baseline operating conditions and four control strategies during the cooling season are compared. Figure 5a This shows the monthly photovoltaic self-consumption rate. Figure 5bThe monthly photovoltaic self-sufficiency rate is displayed. The baseline operating condition is that the air conditioning temperature setpoint is fixed and the ceiling fan is not used; Strategy I corresponds to the setpoint switching scheme based on time-of-use pricing described in Example 5; Strategy II corresponds to the tiered setpoint scheme based on photovoltaic output level described in Example 4; Strategy III corresponds to the progressive setpoint stepping scheme based on the difference between photovoltaic and load described in Example 2; Strategy IV corresponds to the proportional setpoint stepping scheme based on the ratio of photovoltaic to load described in Example 3.

[0122] Compared to other schemes, Strategy III, employing the progressive setpoint stepping scheme described in Example 2, and Strategy IV, employing the proportional setpoint stepping scheme described in Example 3, achieve higher photovoltaic self-consumption and self-sufficiency rates in each month, demonstrating stronger local photovoltaic consumption capacity and higher photovoltaic power supply coverage. Specifically, the highest monthly photovoltaic self-consumption rate is 98.0%, achieved using Strategy IV of the proportional setpoint stepping scheme described in Example 3; the highest monthly photovoltaic self-sufficiency rate is 88.0%, achieved using Strategy III of the progressive setpoint stepping scheme described in Example 2.

[0123] Furthermore, both the progressive setpoint stepping scheme (Strategy III) described in Example 2 and the proportional setpoint stepping scheme (Strategy IV) described in Example 3 exhibit better stability in terms of monthly photovoltaic self-consumption rate in response to weather changes. Specifically, the differences between the maximum and minimum monthly photovoltaic self-consumption rates for the two schemes are 27.9 and 31.5, respectively, indicating that even under varying meteorological conditions causing fluctuations in photovoltaic output, the above schemes can still maintain relatively stable local photovoltaic consumption performance. In contrast, the monthly photovoltaic self-consumption rate fluctuation range under the baseline operating condition reaches 49.1, indicating that without adaptive setpoint control, changes in photovoltaic output are more easily translated into monthly fluctuations in local consumption capacity. Therefore, the progressive setpoint stepping scheme described in Example 2 and the proportional setpoint stepping scheme described in Example 3 can achieve more stable local photovoltaic consumption and self-sufficiency performance under different meteorological conditions.

[0124] like Figures 6a to 6e As shown, the power and temperature variation characteristics of the baseline operating condition and four control strategies over five consecutive working days are displayed. The horizontal axis represents time, and the vertical axis shows the changes in photovoltaic power generation, total building load, and air conditioning set temperature, respectively, to compare the matching characteristics of photovoltaic output and building load in the time dimension under different control strategies.

[0125] like Figure 6aAs shown, under the baseline operating condition, the air conditioning set temperature remains fixed, and fan linkage control is not activated. This condition reveals a significant mismatch between the photovoltaic power generation curve and the building load curve in terms of time. The photovoltaic power reaches its peak at midday, while the building load fails to synchronize with it at various times, resulting in a situation where surplus photovoltaic power is fed into the grid and transmitted externally, while grid power is purchased simultaneously.

[0126] like Figure 6b As shown, when using the time-of-use pricing-based setpoint switching scheme described in Example 5, the control module lowers the air conditioner setpoint temperature to a lower level during off-peak electricity price periods to increase the air conditioner's cooling load and enhance electricity demand; during peak electricity price periods, it raises the air conditioner setpoint temperature to a higher level to reduce the cooling load. This control method does indeed increase air conditioner energy consumption during some off-peak electricity price periods, thereby improving local photovoltaic (PV) consumption to some extent. However, since the setpoint temperature reset in this scheme only depends on the electricity price sequence and does not directly consider changes in PV output, it significantly reduces air conditioner power during peak electricity price periods when PV power reaches its peak, further exacerbating the time mismatch between PV power generation and building load.

[0127] like Figure 6c As shown, when using the tiered setting value scheme based on photovoltaic output level described in Example 4, the control module adjusts the air conditioning set temperature between multiple discrete temperature values ​​according to the real-time photovoltaic power output level. Compared to the scheme described in Example 5, this scheme has a more significant effect on improving local photovoltaic consumption. However, since the set temperature can only switch between several fixed discrete values, such as 21℃, 25℃, and 28℃, it cannot achieve small-amplitude continuous adjustment. Therefore, there is still a mismatch between photovoltaic power and building load during certain periods, such as... Figure 6c The area shown in the middle circle is as follows.

[0128] like Figure 6d As shown, when using the progressive setpoint stepping scheme based on the difference between photovoltaic power and load as described in Example 2, the air conditioning setpoint temperature can be gradually adjusted in preset steps, allowing the building load to more closely follow changes in photovoltaic power over time. Compared to the tiered setpoint scheme, this progressive adjustment method provides more available setpoint temperature levels, thereby improving the overall matching degree between photovoltaic power and building load. However, during certain periods, this scheme exhibits relatively frequent setpoint temperature oscillations. This is mainly because the air conditioning operating point may be in the critical area between high-temperature and low-temperature zones. In this area, frequent fine-tuning of the setpoint temperature is required to maintain supply and demand matching. Furthermore, the 5-minute control time step used in the simulation further amplifies the adjustment oscillations on a short time scale.

[0129] like Figure 6eAs shown, when using the proportional setpoint stepping scheme based on the photovoltaic-to-load ratio described in Example 3, the matching degree between the total building load and the photovoltaic power generation is further improved, and its overall photovoltaic local consumption level is higher than that of the scheme described in Example 2. This scheme dynamically selects a setpoint temperature adjustment range of ±0.5℃, ±1.0℃, or ±1.5℃ based on the deviation ratio between photovoltaic power and load in the previous control cycle, enabling the air conditioning load to respond more quickly to changes in photovoltaic power, thereby achieving a more accurate load tracking effect during continuous working days. Therefore, this proportional setpoint stepping scheme exhibits superior overall performance in terms of response speed, supply-demand matching accuracy, and photovoltaic local consumption capacity.

[0130] like Figure 7a and Figure 7b As shown, the comparison results of photovoltaic self-consumption rate and photovoltaic self-sufficiency rate under the baseline operating condition and four control strategies are presented under different photovoltaic installed capacity ratios. The photovoltaic installed capacity ratio is defined as the ratio of the total annual photovoltaic power generation to the total annual electricity load of the target building under the baseline operating condition, used to characterize the penetration level of the photovoltaic system in the building energy system.

[0131] like Figure 7a As shown, within the entire range of photovoltaic (PV) installed capacity ratios examined, Strategy IV, employing the proportional setpoint stepping scheme based on the PV-to-load ratio described in Example 3, consistently achieved the highest PV self-consumption rate; Strategy III, employing the progressive setpoint stepping scheme based on the PV-to-load difference described in Example 2, followed closely. As the PV installed capacity ratio gradually increased from 0.5 to 1.5, the PV self-consumption rate under Strategy IV decreased by only 10.3%, while the decrease under the baseline operating condition reached 19.3%, indicating that the proportional setpoint stepping scheme can still maintain a high level of local consumption even under high PV penetration conditions. In contrast, Strategy I, employing the setpoint switching scheme based on time-of-use pricing described in Example 5, exhibited a lower PV self-consumption rate across all installed capacity ratios, with a more significant decrease occurring at higher PV installed capacity ratios. Strategy II, which adopts the photovoltaic output level-based tiered setting scheme described in Example 4, has certain advantages over Strategy I under the condition of low photovoltaic installed capacity ratio. However, as the photovoltaic installed capacity ratio increases, its photovoltaic self-consumption rate decreases more significantly. The main reason is that the setting temperature of this scheme only switches between finite discrete values, making it difficult to finely match continuously changing photovoltaic output.

[0132] like Figure 7bAs shown, under different photovoltaic (PV) installed capacity ratios, the PV self-sufficiency rates of Strategy III (using the progressive setpoint stepping scheme described in Example 2) and Strategy IV (using the proportional setpoint stepping scheme described in Example 3) are significantly higher than those of Strategy I (using the scheme described in Example 5) and Strategy II (using the scheme described in Example 4). When the PV installed capacity ratio increases to 1.5, the PV self-sufficiency rates of both Strategy III and Strategy IV are close to 75%, indicating that under higher PV penetration conditions, the above two setpoint stepping schemes based on the relationship between PV and load can effectively improve the overall energy self-sufficiency level of buildings. Furthermore, as the PV installed capacity ratio increases from 0.5 to 1.5, the PV self-sufficiency rate under Strategy IV increases by 44.0%, while the increase under the baseline condition is only 36.5%. This indicates that the proportional setpoint stepping scheme not only helps to improve the instantaneous on-site PV absorption capacity but also enhances the building's utilization rate of its own PV power generation on an annual scale, thereby reducing dependence on grid power supply.

[0133] comprehensive Figure 7a and Figure 7b The results show that the progressive setpoint stepping scheme described in Example 2 and the proportional setpoint stepping scheme described in Example 3 both exhibit better adaptability and stability under different photovoltaic installed capacity ratios. They are especially suitable for application scenarios with a high photovoltaic installed capacity ratio, and can fully utilize the passive energy storage regulation role of building envelope and / or indoor components while improving the local consumption and self-sufficiency of photovoltaics.

[0134] Since the photovoltaic-driven air conditioning system is equipped with additional photovoltaic modules, inverters and controllers on the basis of the traditional air conditioning system, its initial investment cost mainly consists of the following parts, as shown in Table 5.

[0135] Table 5. Initial Investment Cost Structure of Photovoltaic-Driven Air Conditioning System

[0136] In one specific implementation, the annual operating costs and investment payback periods of the baseline operating condition and four control strategies are compared under three electricity pricing schemes, and the results are as follows: Figure 8 As shown.

[0137] Electricity pricing schemes A and B are based on commercial electricity prices for voltage levels below 1 kV and 35 kV and above, respectively. Scheme C, based on scheme A, further increases the peak-hour electricity price by 25%. For all three schemes, the grid-connected price for surplus photovoltaic power is based on the benchmark on-grid price of coal-fired power in Guangdong Province, which is 0.453 yuan / kWh. To compare and analyze the economic impact of the grid-connected price for surplus photovoltaic power, the grid-connected price of surplus photovoltaic power in Xinjiang Uygur Autonomous Region, at 0.253 yuan / kWh, is also introduced as scheme D.

[0138] The baseline operating condition is that the air conditioner is set to a fixed temperature and the fan is not activated; control strategy I corresponds to the time-of-use electricity price-based setpoint switching scheme described in Example 5; control strategy II corresponds to the photovoltaic output level-based tiered setpoint scheme described in Example 4; control strategy III corresponds to the photovoltaic-load difference-based progressive setpoint stepping scheme described in Example 2; and control strategy IV corresponds to the photovoltaic-load ratio-based proportional setpoint stepping scheme described in Example 3.

[0139] Depend on Figure 8 It can be seen that the baseline operating condition exhibits the highest annual operating cost under all three electricity price schemes. Control strategy I, using the scheme described in Example 5, reduces operating costs by approximately 21.7 to 27.9% through load shifting; however, its cost-saving effect is somewhat limited due to increased cooling demand during off-peak hours. In contrast, control strategies II, III, and IV, employing the adaptive control strategies described in Examples 4, 2, and 3, all achieve operating cost reductions of approximately 28.8 to 30.7% under different electricity price schemes, demonstrating the effectiveness of adaptively adjusting the air conditioning set temperature based on photovoltaic output or the relationship between photovoltaic power and load in reducing operating costs.

[0140] Regarding the payback period, control strategy I exhibits a longer payback period under all three electricity price schemes. Control strategies II, III, and IV, employing the adaptive control strategies described in Examples 4, 2, and 3, show a payback period shortened by approximately 1.5 to 3.6 compared to the baseline operating condition. Taking control strategy IV as an example, its payback period increases by approximately 8.0 under electricity price scheme B compared to scheme A, while under electricity price scheme C, due to increased self-consumption during peak hours, its payback period decreases by approximately 16.9. This indicates that the adaptive control strategy demonstrates superior economic performance when there is a significant difference between peak and off-peak electricity prices.

[0141] like Figure 9The diagram illustrates a comparison of the payback period between the baseline operating condition and various control strategies under different photovoltaic (PV) installed capacity ratios and different feed-in tariffs for surplus PV power. The PV installed capacity ratio is defined as the ratio of total annual PV power generation to the building's total annual electricity load under the baseline operating condition. Control strategy I corresponds to the time-of-use pricing-based setpoint switching scheme described in Example 5; control strategy II corresponds to the tiered setpoint scheme based on PV output levels described in Example 4; control strategy III corresponds to the progressive setpoint stepping scheme based on the difference between PV and load described in Example 2; and control strategy IV corresponds to the proportional setpoint stepping scheme based on the PV-to-load ratio described in Example 3.

[0142] Depend on Figure 9 It can be seen that when the photovoltaic installed capacity ratio is 0.5 and 0.75, the difference in the investment payback period of each control strategy is small, and the payback period values ​​are highly similar. This is because when the photovoltaic installed capacity ratio is low, the photovoltaic power generated during operation can basically be consumed locally by the building load, and the proportion of surplus photovoltaic power transmitted to the grid is relatively small. Different control strategies have limited room for suppressing surplus power, so it is difficult to significantly widen the difference in investment payback period. As the photovoltaic installed capacity ratio increases from 1.0 to 1.5, the gap in investment payback period between control strategy I using the scheme described in Example 5 and control strategies II, III, and IV using the adaptive control strategies described in Examples 4, 2, and 3 gradually widens. This indicates that under high photovoltaic penetration conditions, the adaptive control strategy, by improving local consumption and reducing grid-connected transmission and electricity purchase demand, is more likely to translate into a payback period advantage.

[0143] Furthermore, when the feed-in tariff for surplus photovoltaic power decreases by 44.8% from tariff scheme A to tariff scheme C, the difference in payback period between control strategy I and the adaptive control strategy further widens. Taking the photovoltaic installed capacity ratio of 1.5 as an example, the payback period of control strategy IV using the proportional setpoint step scheme described in Example 3 and the baseline operating condition increases by approximately 16.8% and 21.0%, respectively. This indicates that under conditions of declining feed-in tariffs or even grid connection restrictions, the path of obtaining revenue through grid-connected transmission is limited, while the path of obtaining economic benefits by improving local consumption and reducing grid dependence is more critical. Therefore, in future application scenarios where the feed-in tariff for surplus photovoltaic power continues to decline or distributed photovoltaic grid connection is restricted, the adaptive control strategies described in Examples 4, 2, and 3 are more likely to demonstrate a more significant comprehensive economic advantage compared to the traditional operation mode with a fixed set temperature.

[0144] like Figure 10The diagram illustrates the distribution of the predicted average thermal perception index (PMV) under a baseline operating condition and four control strategies during the cooling season. The baseline operating condition is a fixed air conditioning setpoint with fan linkage disabled. Control strategy I corresponds to the time-of-use pricing-based setpoint switching scheme described in Example 5; control strategy II corresponds to the tiered setpoint scheme based on photovoltaic output level described in Example 4; control strategy III corresponds to the progressive setpoint stepping scheme based on the difference between photovoltaic output and load described in Example 2; and control strategy IV corresponds to the proportional setpoint stepping scheme based on the photovoltaic output and load ratio described in Example 3.

[0145] Depend on Figure 10 It can be seen that under the baseline operating conditions, the PMV value is mainly concentrated around 0.5, indicating that the overall indoor thermal sensation is slightly warm, but the fluctuation range is small, and the thermal environment is relatively stable. Compared with the baseline operating conditions, the PMV distribution range of the control strategy I described in Example 5 is significantly increased, indicating that the degree of fluctuation in indoor thermal comfort is increased, with both slightly cold and slightly warm thermal sensations appearing. This is mainly because the scheme lowers the air conditioner setting temperature to a lower level during off-peak electricity price periods, which can easily produce a slightly cold thermal sensation in a short period of time; while after the setting temperature is adjusted back to a higher level, due to the thermal mass of the building envelope and / or interior components, the indoor temperature shows a slow upward process, and the fan maintains a high speed, which may further amplify the changes in the thermal sensation of being slightly cold or slightly warm. Nevertheless, the overall PMV value under this strategy is still within the acceptable thermal comfort range.

[0146] Furthermore, compared to control strategy I, control strategies II, III, and IV, which employ the adaptive control strategies described in Examples 4, 2, and 3, exhibit a more concentrated PMV distribution, a significant reduction in extreme values, and superior overall thermal comfort performance. Specifically, the median PMV values ​​for control strategies II, III, and IV are approximately 0.37, 0.25, and 0.17, respectively, indicating that adaptive adjustment based on photovoltaic output or the relationship between photovoltaic output and load helps maintain a more thermally neutral indoor environment during different operating phases.

[0147] Based on the comprehensive PMV distribution characteristics, economic analysis results, and energy consumption performance, it can be seen that the control strategy III with the progressive setpoint stepping scheme described in Example 2 and the control strategy IV with the proportional setpoint stepping scheme described in Example 3 achieve a more reasonable balance between energy consumption performance, operating cost savings, and indoor thermal comfort. They can leverage the advantages of passive energy storage regulation while also taking into account the comfort needs of living or using the space.

[0148] Example 7 This invention provides a photovoltaic air conditioning room temperature adaptive control system based on passive energy storage. The system includes at least a photovoltaic power generation device, an air conditioner, a power monitoring sensor, and a controller. The controller is connected to the photovoltaic power generation device, the air conditioner, and the power monitoring sensor, and is configured to execute the photovoltaic air conditioning room temperature adaptive control method based on passive energy storage described in the foregoing embodiments.

[0149] Specifically, photovoltaic (PV) power generation equipment (such as rooftop solar panels) generates solar electricity to power the air conditioning system. The air conditioning system regulates indoor temperature, maintaining a comfortable environment through cooling or heating. Power monitoring sensors monitor the power output of both the PV system and the air conditioning system in real time, ensuring the controller adjusts the temperature settings based on current power generation and load demands. The controller connects the PV equipment, air conditioning system, and sensors, implementing a passive energy storage-based control method to dynamically adjust the air conditioning temperature and optimize energy efficiency.

[0150] The photovoltaic air conditioning room temperature adaptive control system based on passive energy storage of the present invention may further include a fan, which works in conjunction with the air conditioner to regulate the indoor temperature. Increasing airflow helps regulate the indoor temperature. The fan can improve the cooling effect of the air conditioner, especially at higher temperature settings, thus enhancing comfort.

[0151] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0152] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0157] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage, characterized in that, The method includes: Obtain the photovoltaic power generation of the target building in the current control cycle and the real-time power load of the target building; Based on the photovoltaic power generation and the real-time electricity load of the target building, obtain the matching relationship index between photovoltaic and load; Based on temperature control criteria and preset adaptive temperature setting strategy, the air conditioning set temperature for the current control cycle is determined, wherein the temperature control criteria include at least one of photovoltaic power generation, the matching relationship index, and time-of-use electricity price information of the target building's area; Based on the air conditioner's set temperature, set the temperature of the air conditioner in the target building to regulate the room temperature; Wherein, the air conditioner is set to a temperature within a preset temperature range, the preset temperature range includes at least a first temperature level and a second temperature level, and the first temperature level is lower than the second temperature level; When the air conditioner's set temperature is less than or equal to the first temperature level, the target building absorbs cold energy to form cold energy storage; when the air conditioner's set temperature is greater than or equal to the second temperature level, the target building releases cold energy.

2. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to claim 1, characterized in that, When the temperature control criterion includes the matching relationship index, the matching relationship index is determined based on the difference between the photovoltaic power generation and the real-time power load. Determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: Based on the air conditioning set temperature of the previous control cycle, obtain the initial set temperature of the current control cycle; Based on the matching relationship index and the preset adjustment step size, the adjustment direction and adjustment range of the initial set temperature are obtained; The initial set temperature is adjusted according to the adjustment direction and the adjustment range to obtain the air conditioning set temperature for the current control cycle.

3. The photovoltaic air conditioning room temperature adaptive control method based on passive energy storage according to claim 2, characterized in that, The step of obtaining the adjustment direction and adjustment range of the initial set temperature based on the matching relationship index and the preset adjustment step size includes: When the difference between the photovoltaic power generation and the real-time power load is greater than the upper limit of the first preset balance range, the initial set temperature is reduced by the preset adjustment step size. When the difference between the photovoltaic power generation and the real-time power load is less than the lower limit of the first preset balance range, the initial set temperature is increased by the preset adjustment step size. When the difference between the photovoltaic power generation and the real-time power load is within the first preset balance range, the initial set temperature is kept constant. The upper and lower limits of the first preset balance interval are located on both sides of the first preset balance threshold.

4. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to claim 1, characterized in that, When the temperature control criterion includes the matching relationship index, the matching relationship index is determined based on the ratio of the photovoltaic power generation to the real-time power load. The step of determining the air conditioning set temperature for the current control cycle according to the preset temperature control criterion and the preset adaptive temperature setting strategy includes: Based on the air conditioning set temperature of the previous control cycle, obtain the initial set temperature of the current control cycle; Based on the preset ratio range to which the matching relationship index belongs, determine the adjustment direction and adjustment range of the initial set temperature; The initial set temperature is adjusted according to the adjustment direction and the adjustment range to obtain the air conditioning set temperature for the current control cycle.

5. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to claim 4, characterized in that, The step of determining the adjustment direction and adjustment range of the initial set temperature based on the preset ratio range to which the matching relationship index belongs includes: The range of values ​​for the matching relationship index is divided into at least one second preset balance interval and several preset ratio intervals, with different adjustment directions and / or adjustment ranges for different preset ratio intervals. When the matching relationship index is within the second preset balance range, the initial set temperature remains unchanged; When the matching relationship index is within any of the preset ratio ranges, the adjustment direction and adjustment range of the initial set temperature are determined according to the preset ratio range to which the matching relationship index belongs; The upper and lower limits of the second preset balance range are located on both sides of the second preset balance threshold, and the adjustment range is positively correlated with the degree of deviation between the preset ratio range and the second preset balance threshold.

6. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to claim 1, characterized in that, When the temperature control criterion includes the photovoltaic power generation, determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: The photovoltaic power ratio is obtained based on the ratio of the photovoltaic power generation to the reference photovoltaic power under standard operating conditions. Based on the photovoltaic power ratio, a target temperature level corresponding to the photovoltaic power ratio is selected from multiple preset temperature levels and used as the air conditioning set temperature for the current control cycle.

7. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to claim 1, characterized in that, When the temperature control criterion includes the time-of-use electricity price, determining the air conditioning set temperature for the current control cycle based on the preset temperature control criterion and the preset adaptive temperature setting strategy includes: Based on the time-of-use electricity price information, determine whether the current control period belongs to the preset peak period; If the current control cycle is during the preset peak period, the air conditioner set temperature will be reset to the first preset temperature value; If the current control cycle is not in the preset peak period, the air conditioner set temperature will be reset to the second preset temperature value; The first preset temperature value is higher than the second preset temperature value.

8. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to any one of claims 1-7, characterized in that, After setting the temperature of the air conditioner in the target building according to the air conditioner's set temperature to regulate the room temperature, the method further includes: The fan speed level is obtained based on the air conditioner set temperature, wherein the fan speed level and the air conditioner set temperature are positively correlated. The fans inside the target building are controlled to rotate according to the fan speed level.

9. The method for adaptive temperature control of photovoltaic air conditioning based on passive energy storage according to any one of claims 1-7, characterized in that, The preset temperature range is determined based on the preset acceptable range of the predicted average thermal sensation index.

10. A photovoltaic air conditioning room temperature adaptive control system based on passive energy storage, characterized in that, The system includes at least a photovoltaic power generation device, an air conditioner, a power monitoring sensor, and a controller, wherein the controller is connected to the photovoltaic power generation device, the air conditioner, and the power monitoring sensor respectively, and the controller is configured to execute the photovoltaic air conditioner room temperature adaptive control method based on passive energy storage as described in any one of claims 1-9.

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