Photovoltaic energy storage and fresh air humidifying system based on supply and demand time sequence matching

Abstract

The invention discloses a photovoltaic energy storage and fresh air humidifying system based on supply and demand time sequence matching. The photovoltaic energy storage and fresh air humidifying system comprises a supply and demand curve dynamic alignment unit, a communication and prediction unit and a dynamic humidity control strategy and scheduling expansion unit. The communication and prediction unit comprises system interconnection, photovoltaic output prediction and humidity demand prediction; according to the system interconnection, a light storage EMS and a fresh air humidity adjusting controller are connected through an RS485-MQTT protocol, and a two-way data interaction channel (the transmission period is smaller than or equal to 1 s) is established. The overlapping time period of the photovoltaic output and the humidity adjusting load is prolonged from 23% to 61% through predictive control, the service life of equipment is protected, the number of times of starting and stopping of a compressor is reduced by 54% (the daily average number of times is reduced from 28 to 13), and the battery circulation depth is reduced to 50%.

Description

Technical Field

[0001] The present invention relates to a fresh air humidity control system, and in particular to a photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching. Background Art

[0002] With the upgrading of healthy building standards, intelligent fresh air systems with integrated dehumidification and humidification functions are rapidly popularized in high-humidity areas (average annual RH>75%), dry areas (winter RH<30%) and residential areas with frequent return of south wind. However, existing technologies face the dual challenges of low energy synergy efficiency and equipment life loss in practical applications. The specific technical contradictions are as follows: humidity module causes energy consumption to jump, control mode and perception characteristics mismatch, photovoltaic-load timing mismatch, energy storage control and load characteristics conflict, nonlinear load power supply problems, insufficient prediction and control accuracy, using a single meteorological station data to predict photovoltaic output, the model error rate reaches 15%, and it cannot adapt to short-term weather mutations (such as sudden rainfall causing photovoltaic output to drop by 80%), resulting in: o The probability of humidity deviation exceeding ±8%RH increases by 41%; the number of deep cycles of the energy storage system fluctuates by more than ±3 times per day (standard deviation σ=1.8).

[0003] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0004] In response to the problems in the related technology, the present invention proposes a photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] To this end, the specific technical solution adopted by the present invention is as follows: A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching, including a supply and demand curve dynamic alignment unit, a communication and prediction unit, a dynamic humidity control strategy and a scheduling expansion unit; The communication and prediction unit includes system interconnection, photovoltaic output prediction and humidity demand prediction; The system interconnection: connect the solar storage EMS and the fresh air humidity control controller through the RS485-MQTT protocol to establish a two-way data interaction channel; The photovoltaic output forecast: integrates the sunshine intensity and temperature data of the meteorological station and the group string current monitoring, and uses the ARIMA model to roll-up the output forecast for the next 15 minutes; The humidity demand prediction: training the LSTM model based on historical data to predict the humidity change trend in the next 2 hours; The dynamic humidity control strategy includes a photovoltaic direct supply stage and an energy storage power supply stage.

[0006] Preferably, the photovoltaic direct supply stage includes a control target and an execution strategy.

[0007] Preferably, the control target is to quickly adjust the indoor humidity to a preset limit value during the photovoltaic output peak period.

[0008] Preferably, the execution strategy is to operate the compressor and the humidifier at full power, give priority to using photovoltaic direct power, and store the remaining power in the energy storage system.

[0009] Preferably, during the energy storage power supply stage, the humidity is maintained near the lower limit of the tolerance range, the power demand is reduced, the humidity control module operates at a reduced frequency, the energy storage system discharge power is limited to below 0.5C, and the humidity set value is adjusted dynamically.

[0010] Preferably, the scheduling extension unit includes prediction of sunshine-free days and energy efficiency optimization.

[0011] Preferably, the sunshine-free day prediction is as follows: when the EMS receives a rainy weather warning for the next day, it automatically switches to the grid interaction mode: Off-peak charging: 00:00-06:00 to fully charge the energy storage at 0.3 yuan / kWh; Peak power avoidance: Rely entirely on energy storage for power supply during the day to avoid purchasing electricity during high-price periods.

[0012] The beneficial effects of the present invention are: through predictive control, the overlapping period of photovoltaic output and humidity control load is increased from 23% to 61%, protecting the life of the equipment: the number of compressor starts and stops is reduced by 54%, and the battery cycle depth is reduced to DOD50%. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a structural schematic diagram of a photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to an embodiment of the present invention.

[0015] In the figure: 1. Dynamic alignment unit of supply and demand curves; 2. Communication and prediction unit; 3. Dynamic humidity control strategy; 4. Scheduling expansion unit. DETAILED DESCRIPTION

[0016] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0017] According to an embodiment of the present invention, a photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching is provided.

[0018] Embodiment 1: like Figure 1 As shown, the photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to an embodiment of the present invention includes a supply and demand curve dynamic alignment unit 1, a communication and prediction unit 2, a dynamic humidity control strategy 3 and a scheduling expansion unit 4; The communication and prediction unit 2 includes system interconnection, photovoltaic output prediction and humidity demand prediction; The system interconnection: the solar storage EMS and the fresh air humidity control controller are connected through the RS485-MQTT protocol to establish a two-way data interaction channel, in which the transmission cycle is ≤1s; The photovoltaic output forecast: integrates the sunshine intensity and temperature data of the meteorological station and the group string current monitoring, and uses the ARIMA model to roll-up the output forecast for the next 15 minutes, thereby achieving an error rate of <5%; The humidity demand forecast: Based on the historical data, the LSTM model is trained to predict the humidity change trend in the next 2 hours, with a prediction accuracy of about 92.4%; The dynamic humidity control strategy 3 includes a photovoltaic direct supply stage and an energy storage power supply stage.

[0019] Embodiment 2: like Figure 1 As shown, the photovoltaic direct supply stage includes control objectives and execution strategies.

[0020] The control target is to quickly adjust the indoor humidity to a preset limit value, specifically 45±3%RH or 55±3%RH, during the photovoltaic output peak period.

[0021] The execution strategy runs the compressor and humidifier at full power, specifically 1.8kW and 0.6kW respectively, giving priority to the use of photovoltaic direct power supply, with the specific photovoltaic direct power supply accounting for ≥85%. The remaining electricity is stored in the energy storage system, with the charge state increased to more than 90%.

[0022] During the energy storage power supply stage, the humidity is maintained near the lower limit of the tolerance range, which is 45-55%RH. The power demand is reduced, and the humidity control module operates at a reduced frequency, wherein the compressor is ≤40Hz and the power is ≤0.8kW. The discharge power of the energy storage system is limited to below 0.5C, which effectively extends the battery life and dynamically adjusts the humidity set value.

[0023] The scheduling extension unit 4 includes prediction of sunshine-free days and energy efficiency optimization.

[0024] The prediction of no sunshine day: When EMS receives a rainy weather warning for the next day, the specific standard is that the cloud coverage rate is >80%, and it automatically switches to the grid interaction mode: Valley charging: 00:00-06:00, the energy storage is fully charged at 0.3 yuan / kWh, which is reflected as 100% state of charge; Peak power avoidance: Rely entirely on energy storage for power supply during the day to avoid purchasing electricity during high-price periods.

[0025] In summary, with the help of the above technical solution of the present invention, the overlapping period of photovoltaic output and humidity control load is increased from 23% to 61% through predictive control, thereby protecting the life of the equipment: the number of compressor starts and stops is reduced by 54%, and the specific average daily number of compressor starts and stops is reduced from 28 times to 13 times, and the battery cycle depth is reduced to DOD50%.

[0026] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching, characterized in that: It includes a supply and demand curve dynamic alignment unit (1), a communication and prediction unit (2), a dynamic humidity control strategy (3) and a scheduling expansion unit (4); The communication and prediction unit (2) includes system interconnection, photovoltaic output prediction and humidity demand prediction; The system interconnection: connect the solar storage EMS and the fresh air humidity control controller through the RS485-MQTT protocol to establish a two-way data interaction channel; The photovoltaic output forecast: integrates the sunshine intensity and temperature data of the meteorological station and the group string current monitoring, and uses the ARIMA model to roll-up the output forecast for the next 15 minutes; The humidity demand prediction: training the LSTM model based on historical data to predict the humidity change trend in the next 2 hours; The dynamic humidity control strategy (3) includes a photovoltaic direct supply stage and an energy storage power supply stage.

2. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to claim 1, characterized in that: The photovoltaic direct supply stage includes control objectives and execution strategies.

3. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to claim 2, characterized in that: The control target is to quickly adjust the indoor humidity to a preset limit value during the photovoltaic output peak period.

4. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to claim 3, characterized in that: The execution strategy is to operate the compressor and the humidifier at full power to give priority to the use of photovoltaic direct power, and store the remaining power in the energy storage system.

5. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to claim 1, characterized in that: The energy storage power supply stage is to maintain the humidity near the lower limit of the tolerance range, reduce power demand, reduce the frequency of the humidity control module, limit the discharge power of the energy storage system to below 0.5C, and dynamically adjust the humidity set value.

6. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to claim 1, characterized in that: The scheduling extension unit (4) includes prediction of sunshine-free days and energy efficiency optimization.

7. A photovoltaic energy storage and fresh air humidity control system based on supply and demand timing matching according to claim 6, characterized in that: The prediction of no sunshine day: When the EMS receives a rainy weather warning for the next day, it automatically switches to the grid interaction mode: Off-peak charging: 00:00-06:00 to fully charge the energy storage at 0.3 yuan / kWh; Peak power avoidance: Rely entirely on energy storage for power supply during the day to avoid purchasing electricity during high-price periods.

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