Multi-objective collaborative control method and device for community photovoltaic energy storage fresh air system

Abstract

This invention discloses a multi-objective collaborative control method and device for community photovoltaic energy storage fresh air systems. The method is based on a blockchain microgrid, enabling community power sharing between any two user nodes through a pre-set P2P transaction contract; it monitors the external environment and the indoor environment of each user node in real time to obtain external environmental parameters and indoor environmental parameters for each user node; it determines the fresh air power supply strategy corresponding to each user node, and solves for the optimal power allocation command through a pre-set multi-constraint optimization model; based on the optimal power allocation command, it controls each power source in the fresh air power supply strategy to provide corresponding electrical energy; after a preset duration, the above process is repeated to perform multi-objective collaborative control of multiple user nodes within the same community. This invention combines community power supply, photovoltaic power supply, and energy storage functions to match the optimal fresh air power supply strategy for each user node.

Description

Technical Field

[0001] This invention relates to a multi-objective collaborative control method for a community photovoltaic energy storage fresh air system, and also to a corresponding multi-objective collaborative control device, belonging to the field of community power supply technology. Background Technology

[0002] Currently, fresh air systems are widely used in building energy conservation, but traditional solutions mostly rely on grid power, resulting in high operating costs and insufficient economic efficiency. With the gradual popularization of photovoltaic technology, some fresh air systems have begun to incorporate photovoltaic power generation in an attempt to reduce dependence on grid power. However, these "photovoltaic direct-drive" solutions lack effective energy storage buffers, leading to unused photovoltaic power when output is surplus, high curtailment rates, and overall low energy efficiency.

[0003] Following this, new ventilation systems based on "fixed-price electricity purchase + energy storage" emerged in the market. These solutions use energy storage devices to smooth out peak and off-peak demand, reducing electricity costs to some extent. However, because the electricity price is set at a fixed value, it cannot be flexibly adjusted according to real-time price fluctuations, making it difficult to achieve better economic performance during peak electricity price periods. Furthermore, existing systems generally employ simple threshold triggering mechanisms for IAQ (Indoor Air Quality) control, such as operating at full speed when the CO2 concentration exceeds 1000 ppm. This rigid, "black and white" control method can easily cause drastic fluctuations in electricity demand in a short period, creating new load spikes and exacerbating system instability and energy consumption pressure.

[0004] In recent years, the application of blockchain technology in distributed energy trading has gradually matured, providing a new technological path for community-level energy sharing and optimization. Theoretically, it can achieve flexible power allocation and cost sharing through peer-to-peer (P2P) trading mechanisms. However, existing technologies have not yet effectively integrated blockchain, photovoltaic power generation, energy storage management, and the operation and control of fresh air systems, and have not yet formed a comprehensive optimization control strategy that can simultaneously consider economy, energy efficiency, battery life, and indoor air quality.

[0005] Therefore, there is an urgent need to propose a multi-objective collaborative control method for community photovoltaic energy storage fresh air systems, in order to systematically solve the multiple defects of traditional solutions in terms of grid dependence, photovoltaic utilization rate, energy storage management, electricity price response and IAQ control. Summary of the Invention

[0006] The primary technical problem to be solved by this invention is to provide a multi-objective collaborative control method for community photovoltaic energy storage fresh air systems.

[0007] Another technical problem to be solved by the present invention is to provide a multi-objective collaborative control device for a community-type photovoltaic energy storage fresh air system.

[0008] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0009] According to a first aspect of the present invention, a multi-objective collaborative control method for a community photovoltaic energy storage fresh air system is provided, comprising the following steps:

[0010] Based on a blockchain microgrid, multiple user nodes within the same community are pre-interconnected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-set P2P transaction contract; each user node corresponds to a fresh air system, and the community transaction electricity price is lower than the grid price at the current time.

[0011] Real-time monitoring of the external environment to obtain external environmental parameters;

[0012] For any one of the user nodes, the indoor environment of that user node is monitored in real time to obtain indoor environmental parameters;

[0013] Based on the external environmental parameters and the indoor environmental parameters, a fresh air power supply strategy corresponding to the user node is determined; wherein, the fresh air power supply strategy includes at least one or a combination of community power supply, photovoltaic power supply and energy storage power supply.

[0014] Based on the fresh air power supply strategy corresponding to the user node, the optimal power allocation command is solved through a preset multi-constraint optimization model.

[0015] Based on the optimal power allocation command, each power source in the fresh air power supply strategy is controlled to provide corresponding electrical energy.

[0016] After a preset duration, the external environment and the indoor environment of each user node are re-monitored, and the above process is repeated to achieve multi-objective collaborative control of multiple user nodes in the same community.

[0017] Preferably, the fresh air power supply strategy is determined through the following steps:

[0018] When the outdoor PM2.5 exceeds the first threshold, the fresh air system shuts off the external circulation and starts electrostatic dust removal, primarily using fast-response energy storage power supply, supplemented by the community power supply to compensate for the difference.

[0019] When the outdoor PM2.5 is less than the first threshold and the indoor CO2 concentration is greater than the second threshold, the fresh air system will run at full speed in external circulation, mainly using the economical community power supply, and supplementing the difference through energy storage power supply.

[0020] When the outdoor PM2.5 is less than the first threshold and the indoor CO2 concentration is less than the second threshold, the fresh air system will operate at the lowest speed, mainly using photovoltaic power supply, and supplementing the difference through energy storage power supply.

[0021] When the indoor CO2 concentration is less than the second threshold and the indoor VOC concentration is greater than the third threshold, the fresh air system will operate at high speed, mainly using photovoltaic power supply, and supplementing the difference through energy storage power supply.

[0022] When the outdoor PM2.5 concentration is greater than the first threshold and the indoor CO2 concentration is greater than the second threshold, the fresh air system alternates between external circulation and internal circulation according to the preset duration, so as to perform dynamic time-sharing control based on the current electricity price period.

[0023] The dynamic time-sharing control includes the following steps:

[0024] When the current electricity price period is a low-price period, if the energy storage battery's SOC is less than 30%, it will be charged first and powered by photovoltaic, community or grid; if the energy storage battery's SOC is greater than 50%, the fresh air will be operated at full speed by energy storage power supply to pre-exchange air through internal circulation.

[0025] When the current electricity price period is a high-price period, if the energy storage battery's SOC is greater than 40%, the fresh air will be operated according to the preset air volume, and the power supply of photovoltaic power, energy storage supplement and community power supply will be allocated according to the preset ratio; if the energy storage battery's SOC is ≤30%, the response will be graded according to the SOC range: when the SOC is in the range of 20% to 30%, the speed will be reduced in stages; when the SOC is in the range of 10% to 20%, it will switch to pulse mode; when the SOC is below 10%, it will be forcibly switched to natural ventilation.

[0026] Preferably, the step of solving for the optimal power allocation command based on the fresh air power supply strategy corresponding to the user node through a preset multi-constraint optimization model specifically includes:

[0027] Based on the pre-defined multi-constraint optimization model, calculate the minimum value output by the model.

[0028] ;

[0029] in, This represents the power purchased by the power grid during time period t; This represents the real-time electricity price during time period t; Indicates the duration of IAQ exceeding the limit; This indicates an optimized time domain length, with a time granularity of 5 minutes. α represents the battery degradation penalty term at time t; α represents the economic weighting coefficient, which is positively correlated with the real-time electricity price; β represents the battery life weighting coefficient, which is positively correlated with the battery health status; γ represents the IAQ weighting coefficient, which is positively correlated with the degree of pollution exceeding the standard.

[0030] ;

[0031] in, This represents the charge / discharge power penalty coefficient, and k represents the SOC deviation from the median penalty coefficient;

[0032] Obtain the minimum required air volume for the fresh air system of this user node;

[0033] Based on the minimum output value of the model and the minimum required air volume, the power allocation of community power supply, photovoltaic power supply and energy storage power supply is determined to form the optimal power allocation instruction.

[0034] Preferably, the economic efficiency weighting coefficient α, the battery life weighting coefficient β, and the IAQ weighting coefficient γ are calculated through the following steps:

[0035] ;

[0036] in, Indicates the base electricity price; The peak value is indicated by k1, which is 1 when the electricity price is at its peak and 0 when the electricity price is at its trough; k1 and k2 represent the preset adjustment coefficients.

[0037] ;

[0038] in, This indicates battery health, and the β weight increases by 40% when SOH < 80%. Indicates the rate of change of SOC; This indicates the preset attenuation coefficient;

[0039] ;

[0040] in, This indicates the preset urgency level.

[0041] Preferably, the minimum required air volume Q of the fresh air system is... req Calculated using the following formula:

[0042]

[0043] Where V represents the volume of the indoor space; Δt represents the current indoor CO2 concentration; Ctarget represents the target CO2 concentration; Δt represents the time to reach the target concentration; k represents the CO2 generation rate constant; λ represents the particulate matter removal efficiency coefficient; IPM2.5 represents the current indoor PM2.5 concentration, and the target concentration limit for PM2.5 is 35.

[0044] Among these, the optimal condition is when indoor PM2.5 ≤ 80 μg / m³. 3 When the fresh air system is in normal mode, the economic weight coefficient α, battery life weight coefficient β, and IAQ weight coefficient γ are calculated respectively to determine the minimum value of the multi-constraint optimization model; and the constraint condition for the energy storage battery is: the battery SOC is between 20% and 80%;

[0045] When 80μg / m 3 Indoor PM2.5 < 150 μg / m³ 3 When this happens, the IAQ weighting coefficient γ is increased;

[0046] When indoor PM2.5 ≥ 150 μg / m³ 3 When the fresh air system is in emergency mode, α = 0 and γ = 1.8 are directly set to calculate the minimum value of the multi-constraint optimization model; and the constraint condition of the energy storage battery changes as follows: the battery SOC is between 15% and 100%.

[0047] In a preferred embodiment, when the outdoor PM2.5 is less than the first threshold and the indoor CO2 concentration is greater than the second threshold, if there is affordable electricity available in the community, the fresh air system will operate at full speed; if there is no affordable electricity available in the community, the fresh air system will start operating at a preset wind speed and gradually increase the air volume at preset intervals until the fresh air system reaches its maximum wind speed.

[0048] Preferably, when any of the user nodes experiences a power outage due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the operation of the user node's ventilation infrastructure.

[0049] According to a second aspect of the present invention, a multi-objective collaborative control device for a community photovoltaic energy storage fresh air system is provided, comprising:

[0050] The blockchain microgrid layer consists of multiple user nodes, smart meters, and P2P transaction contracts. Multiple user nodes within the same community are interconnected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-set P2P transaction contract.

[0051] The IAQ adaptive execution layer includes a variety of environmental sensors and execution units. The various environmental sensors are used to monitor the external environment and the indoor environment of each user node in real time, thereby obtaining the external environmental parameters and the indoor environmental parameters of each user node.

[0052] The dynamic time-of-use control layer includes an electricity price prediction module, a rolling optimizer, and an energy storage management unit. The electricity price prediction module is connected to the blockchain microgrid layer to call the grid API to obtain the time-of-use electricity price table and compare it with the community electricity price. The rolling optimizer is connected to the various environmental sensors and is used to determine the fresh air supply strategy corresponding to the user node based on the external environmental parameters and the indoor environmental parameters. The energy storage management unit is connected to the rolling optimizer and is used to solve the optimal power allocation command through a preset multi-constraint optimization model based on the fresh air supply strategy corresponding to the user node.

[0053] The energy storage management unit is connected to the execution unit and is used to send the optimal power allocation command to the execution unit, thereby controlling each power source in the fresh air power supply strategy to provide corresponding electrical energy through the execution unit.

[0054] According to a third aspect of the present invention, a multi-objective collaborative control device for a community-based photovoltaic energy storage fresh air system is provided, comprising a processor and a memory, wherein the processor reads a computer program in the memory for implementing the above-described multi-objective collaborative control method.

[0055] Compared with the prior art, the present invention has the following technical effects:

[0056] (1) Community power sharing through blockchain microgrids effectively reduces the cost of purchasing electricity from the grid. Furthermore, by combining community power supply, photovoltaic power supply and energy storage functions, the optimal fresh air power supply strategy can be matched for each user node based on the internal and external environmental parameters of each user node.

[0057] (2) The SOC of the energy storage battery is constrained, which greatly improves the battery cycle life; and in emergency situations, the lower limit of the SOC of the energy storage battery can be relaxed to ensure the reliability of the fresh air operation.

[0058] (3) Every preset time interval, rolling optimization is carried out in combination with community electricity price, battery energy storage results and photovoltaic power supply results to ensure that the fresh air system continues to operate stably at low cost.

[0059] (4) Provide emergency measures to ensure fresh air supply. When any user node loses power due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the operation of the fresh air infrastructure of that user node. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall process of a multi-objective collaborative control method for a community photovoltaic energy storage fresh air system provided in the first embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of community power sharing in the first embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram illustrating the operation of the multi-objective cooperative control method in the first embodiment of the present invention;

[0063] Figure 4 This is a structural diagram of a multi-objective collaborative control device for a community-type photovoltaic energy storage fresh air system provided in the second embodiment of the present invention;

[0064] Figure 5 A flowchart illustrating the multi-objective collaborative control device for a community-based photovoltaic energy storage fresh air system, provided in the second embodiment of the present invention;

[0065] Figure 6 This is a flowchart of the IAQ adaptive control layer in the second embodiment of the present invention;

[0066] Figure 7 This is a flowchart of dynamically optimizing the optimal power allocation command in the second embodiment of the present invention;

[0067] Figure 8 This is a structural diagram of a multi-objective collaborative control device for a community-type photovoltaic energy storage fresh air system provided in the third embodiment of the present invention. Detailed Implementation

[0068] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0069] This invention provides a multi-objective collaborative control method and device for community photovoltaic energy storage fresh air systems. It is primarily applicable to fresh air system control scenarios in community photovoltaic microgrid environments, and is particularly suitable for residential or commercial buildings requiring dynamic balancing of energy economy, battery life, and indoor air quality. The fresh air system includes photovoltaic panels for power generation and energy storage batteries for storing or releasing electrical energy. By adjusting the dynamic weights of multiple objectives, energy economy is prioritized during peak electricity price periods, while IAQ (Indoor Air Quality) is prioritized during peak pollution periods. Furthermore, a battery degradation penalty term is incorporated into the optimization objectives to prevent frequent deep charging and discharging of the battery. In addition, based on different environmental monitoring data, a combined power supply is implemented using community power, photovoltaic power, and energy storage power to resolve the multi-objective conflict between optimizing energy consumption, air quality, and equipment lifespan.

[0070] First embodiment

[0071] like Figure 1 As shown, the first embodiment of the present invention provides a multi-objective collaborative control method for a community photovoltaic energy storage fresh air system, specifically including the following steps:

[0072] S10: Community power sharing.

[0073] In this embodiment, based on a blockchain microgrid, multiple user nodes within the same community are pre-interconnected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-defined P2P transaction contract. It is understood that each user node corresponds to a fresh air system, and the community transaction electricity price is lower than the current grid price.

[0074] like Figure 2 As shown, user A posts a request to sell 1.5kW of electricity. The smart P2P trading contract matches user B's 1.2kW demand with a minimum price (grid price × 80%, neighbor's price); B's energy storage unit receives the electricity. Transaction data is recorded on the blockchain to ensure transparency and auditability.

[0075] S20: Monitors the external environment in real time to obtain external environmental parameters.

[0076] Specifically, a temperature and humidity sensor is used to monitor the external temperature and humidity, and a laser light scattering sensor is used to monitor the outdoor PM2.5 value. In this embodiment, outdoor temperature, humidity, and PM2.5 are mainly collected as external environmental parameters. In other embodiments, other sensors can be used to monitor more environmental parameters as needed, which is not specifically limited here.

[0077] S30: Real-time monitoring of the indoor environment of each user node.

[0078] Specifically, for any given user node, indoor environmental parameters are collected separately for each user node, and then aggregated on the blockchain platform. These indoor environmental parameters include at least: indoor CO2 concentration, PM2.5, and indoor VOC (Volatile Organic Compounds) concentration.

[0079] S40: Determine the fresh air power supply strategy for each user node.

[0080] After obtaining the external environmental parameters and the indoor environmental parameters of each user node based on the above steps S20 to S30, a corresponding fresh air power supply strategy is formed for each user node by combining the indoor environmental parameters of the user node with the external environmental parameters.

[0081] In this embodiment, the fresh air power supply strategy includes at least one or more combinations of community power supply, photovoltaic power supply, energy storage power supply, and grid power supply. It is understood that in this embodiment, each user node is an independent decision-making entity, thus corresponding to an independent fresh air power supply strategy.

[0082] Specifically, in this embodiment, the fresh air power supply strategy includes the following:

[0083] (1) Energy storage power supply + community supplement.

[0084] Specifically, when outdoor PM2.5 exceeds the first threshold (e.g., outdoor PM2.5 > 75 μg / m³), 3 If the outdoor PM2.5 level exceeds the standard, it indicates that the outdoor PM2.5 level is too high. In this case, the fresh air system needs to perform instantaneous high-precision filtration, so the fresh air system needs to shut down the external circulation and start electrostatic dust removal.

[0085] When electrostatic precipitators are activated, a stable high-voltage power supply is required. However, photovoltaic (PV) power supply is volatile, which can lead to a decrease in dust removal efficiency. Furthermore, there is a risk of harmonic interference between the PV DC power supply and the electrostatic precipitator module. Therefore, direct PV drive should be disabled, and energy storage should be prioritized for power supply (due to its high power response speed), with the community power supply used as a secondary option to supplement the power difference.

[0086] (2) Community power supply + energy storage supplement.

[0087] Specifically, when outdoor PM2.5 is less than the first threshold (e.g., outdoor PM2.5 < 75 μg / m³), 3 If the outdoor PM2.5 level is within acceptable limits, the external air circulation system can be turned on. At this point, it is necessary to determine whether the indoor CO2 concentration exceeds the standard. If the indoor CO2 concentration exceeds the standard (e.g., indoor CO2 concentration > 1000 ppm), then rapid and high-volume ventilation is required. Therefore, the fresh air system needs to run at full speed in external air circulation mode.

[0088] When the fresh air system is running at full speed in external circulation, the photovoltaic power supply may be insufficient (for example, only 0.3 to 0.8 kW on cloudy days). Therefore, the community power supply (community electricity price is lower than grid price on average) is used as the main source of power to make up the difference and to ensure high reliability.

[0089] It is important to understand that if there is affordable electricity in the community, the fresh air system will run at full speed; if there is no affordable electricity in the community, the fresh air system will start running at a preset wind speed (at which time energy storage power is used), and the air volume will be increased in steps at preset intervals (for example, the air volume will be increased by 20% every 10 minutes) until the fresh air system reaches the maximum wind speed.

[0090] (3) Photovoltaic power supply + energy storage supplement.

[0091] Specifically, when both outdoor PM2.5 and indoor CO2 concentrations are within acceptable limits, there is no immediate need for fresh air. In this case, the fresh air system operates at its lowest speed to ensure basic ventilation. Therefore, to maximize economic efficiency, photovoltaic power is the primary source, supplemented by energy storage to compensate for the difference.

[0092] Furthermore, when both outdoor PM2.5 and indoor CO2 concentrations are within acceptable limits, but indoor VOC concentrations exceed limits (e.g., indoor VOC > 500 ppb), it indicates a need to provide adequate fresh air to reduce indoor VOC concentrations. In this case, the fresh air system operates at high speed and can perform activated carbon filtration. At this point, rapid airflow is not required, so photovoltaic power is the primary source (fluctuations are permissible), supplemented by energy storage power to compensate for the difference.

[0093] Understandably, in this embodiment, when the fresh air system does not have an urgent need for fresh air exchange, it can operate at low or medium speed, thereby being powered by direct photovoltaic power generation to ensure maximum economic efficiency. When direct photovoltaic power generation cannot meet the power demand, energy storage power generation is used as an emergency measure to ensure the reliability of the fresh air system's operation.

[0094] (4) Dynamic time-sharing control + community supplementation.

[0095] Specifically, when both outdoor PM2.5 and indoor CO2 concentrations exceed the standard (e.g., outdoor PM2.5 > 75 μg / m³), 3 If the indoor CO2 concentration is greater than 1000 ppm, the fresh air system needs to alternate between external circulation and internal circulation for a preset time (e.g., 5 minutes) to reduce the indoor CO2 concentration through external circulation and reduce the indoor PM2.5 concentration through internal circulation.

[0096] In this embodiment, the dynamic time-sharing control strategy specifically includes:

[0097] 1) Operating Logic During Low-Price Periods

[0098] ①SOC < 30%: Priority charging, charging during off-peak hours on the grid, community P2P electricity charging at prices lower than the grid price, and storage of surplus electricity generated by photovoltaic power generation. Fresh air is maintained at a minimum airflow (0.3m³ / s) and is directly powered by photovoltaic / community / grid (without relying on energy storage).

[0099] ② When SOC ≥ 50%, the energy storage power supply operates at full speed (internal circulation pre-air exchange).

[0100] ③Extreme scenario (no electricity in the community while charging): Real-time photovoltaic power generation directly drives the minimum wind volume. If photovoltaic power is 0, grid power supply is activated (still low-priced electricity) without interruption of operation, but the power is limited to 50% of the rated value.

[0101] 2) Operating logic during high-price periods:

[0102] ①SOC ≤ 30%: Tiered response; SOC range 20%-30%, step-by-step speed reduction (20% reduction in air volume every 10 minutes), solar + community emergency support; SOC range 10%-20%, pulse mode (run for 2 minutes / stop for 5 minutes), community highest priority power; SOC range <10%, forced switch to natural ventilation damper.

[0103] ② When SOC > 40%, operate according to the preset air volume, combined power supply: "Direct Photovoltaic Supply": 60%, "Energy Storage Supplement": 30%, "Community Power": 10%.

[0104] Furthermore, in this embodiment, when the indoor PM2.5 ≤ 80 μg / m³ 3 When the fresh air system is in normal mode, the operating constraints for the energy storage battery are: battery SOC between 20% and 80%. This applies when indoor PM2.5 ≥ 150 μg / m³. 3 When the fresh air system is in emergency mode, the operating constraints on the energy storage battery are relaxed to 15%–100%. Understandably, in the high-price period operation logic of dynamic time-sharing control, a stepped response should be implemented within a range of SOC ≥ 20% under normal conditions. If the SOC drops below 20%, an alarm is triggered and a more conservative mode is switched (e.g., pulse mode or forced switch to natural ventilation damper).

[0105] The power grid serves as a level 3 backup power source, with a lower priority than photovoltaic (PV), community P2P electricity, and energy storage (emergency discharge). Its main functions include: 1) directly driving the fresh air system when PV, community P2P, and energy storage cannot meet demand (e.g., extreme weather combined with community power outages and energy storage depletion); 2) charging energy storage batteries under specific conditions (only during off-peak hours and requiring manual user authorization); and 3) providing power for equipment maintenance. For the grid to directly drive the fresh air system, the following conditions must be met simultaneously: ① Insufficient PV power generation; ② No available P2P electricity in the community; ③ Energy storage SOC < 20%.

[0106] Furthermore, in this embodiment, the energy storage battery follows the principle of "prioritizing immediate consumption": it prioritizes charging using surplus photovoltaic power. The charging priority is: photovoltaic (when there is surplus) > community P2P (when there is surplus) > grid (only during off-peak hours, when energy storage is insufficient and the user manually authorizes it). The discharge conditions are: 1) when photovoltaic and community P2P power supply is insufficient to meet the demand and it is not suitable or impossible to use direct grid power supply; 2) when an IAQ emergency requires rapid response.

[0107] S50: Solve for the optimal power allocation command using a preset multi-constraint optimization model.

[0108] After determining the fresh air power supply strategy for each user node based on the above step S40, it is necessary to solve the optimal power allocation command through a preset multi-constraint optimization model, thereby determining the electrical energy required for each power supply method.

[0109] Specifically, it includes the following steps:

[0110] S51: Calculate the minimum value output by the model based on the preset multi-constraint optimization model.

[0111] In this embodiment, the calculation formula for the multi-constraint optimization model is as follows:

[0112]

[0113] in, This represents the power purchased by the power grid during time period t; This represents the real-time electricity price during time period t; Indicates the duration of IAQ exceeding the limit; This represents the total length of the optimization time domain (e.g., 6 hours). The optimization is performed at discrete time points t (t = 1, 2, ..., N), with a time step Δt of 5 minutes, and N = T / Δt. α represents the battery degradation penalty term at time t; α represents the economic weighting coefficient, which is positively correlated with the real-time electricity price; β represents the battery life weighting coefficient, which is positively correlated with the battery health status; γ represents the IAQ weighting coefficient, which is positively correlated with the degree of pollution exceeding the standard.

[0114] ;

[0115] in, represents the charge / discharge power penalty coefficient, and k represents the SOC deviation from the median penalty coefficient.

[0116] Furthermore, the aforementioned economic efficiency weighting coefficient α, battery life weighting coefficient β, and IAQ weighting coefficient γ are calculated using the following formula:

[0117] ;

[0118] in, Indicates the base electricity price; The peak value indicator is 1 when the electricity price is at its peak and 0 when the electricity price is at its trough; k1 and k2 represent preset adjustment coefficients. Furthermore, it should be understood that in this embodiment, the peak value indicator... This discussion only considers electricity prices as peaks or troughs, without including cases with multiple tiered pricing. In other embodiments, additional linear transition functions can be added as needed. For example, in linear pricing, 1.1 yuan corresponds to... When the corresponding price is 0.75 or 0.9 yuan The value is 0.25; for non-linear electricity pricing, the parameter can be set manually.

[0119] ;

[0120] in, This indicates battery health, and the β weight increases by 40% when SOH < 80%. Indicates the rate of change of SOC; This indicates the preset attenuation coefficient.

[0121] ;

[0122] in, This indicates the preset urgency level.

[0123] In addition, when the fresh air system is in normal mode, the economic weight coefficient α, the battery life weight coefficient β, and the IAQ weight coefficient γ are calculated respectively to calculate the minimum value of the multi-constraint optimization model; and the constraint condition for the energy storage battery is: the battery SOC is between 20% and 80%.

[0124] When 80μg / m 3 Indoor PM2.5 < 150 μg / m³ 3 If so, the IAQ weighting coefficient γ is increased by 40% (i.e., from γ to 1.4γ).

[0125] When the fresh air system is in emergency mode, α = 0 and γ = 1.8 are directly set to calculate the minimum value of the multi-constraint optimization model; and the constraint condition of the energy storage battery changes to: the battery SOC is between 15% and 100%.

[0126] S52: Calculate the minimum required air volume for the fresh air system.

[0127] Specifically, in this embodiment, the minimum required air volume Q of the fresh air system req Calculated using the following formula:

[0128]

[0129] Where V represents the volume of the indoor space; Δt represents the current indoor CO2 concentration; Ctarget represents the target CO2 concentration; Δt represents the time to reach the target concentration; k represents the CO2 generation rate constant; λ represents the particulate matter removal efficiency coefficient; IPM2.5 represents the current indoor PM2.5 concentration, and the target concentration limit for PM2.5 is 35.

[0130] S53: Based on the minimum value of the model output and the minimum required air volume of the fresh air system, determine the power allocation of the community power supply, photovoltaic power supply and energy storage power supply respectively to form the optimal power allocation instruction.

[0131] Specifically, in this embodiment, power is allocated in the following manner:

[0132] .

[0133] Furthermore, the power allocation priority for the three power supply methods is as follows:

[0134] First priority: Direct photovoltaic power supply, fully utilizing real-time power generation. .

[0135] Second priority: community replenishment, purchasing sorted by price and demand. .

[0136] The third priority is energy storage, which is used to make up for the difference between the sum of the power of the first two levels of power supply (direct photovoltaic power and community supplementation) and the power P_req corresponding to the minimum required wind volume Q_req.

[0137] The following is an example of dynamic allocation:

[0138] The minimum required power is 0.8kW. The current photovoltaic power generation is 0.5kW, and the available electricity in the community is 0.4kW at 0.6 yuan (50% cheaper than the grid (1.2 yuan)). The final allocation plan is: 0.5kW photovoltaic power supply, 0.3kW community power supply, and energy storage power supply is not activated.

[0139] S60: Controls the power supply strategy for fresh air supply to provide corresponding electrical energy from each power source.

[0140] Specifically, once the required output power for each power supply method is determined based on the above step S50, then each power source in the fresh air power supply strategy can be controlled to provide the corresponding electrical energy.

[0141] After the preset duration (e.g., 10 minutes), return to step S20 above and repeat steps S20 to S60 above, thereby realizing multi-objective collaborative control of multiple user nodes in the same community.

[0142] Furthermore, in this embodiment, preferably, when any user node experiences a power outage due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the operation of the user node's fresh air infrastructure.

[0143] The following is combined with Figure 3 As shown, the working process of the multi-objective cooperative control method of this invention is illustrated with a practical example:

[0144] Assuming an environment of sandstorm and peak electricity prices (peak price 1.2 yuan / kWh), initial SOC = 65%, indoor CO2 = 700 ppm, and an unexpected outdoor event: sandstorm (outdoor PM2.5 increases from 35 to 200 μg / m³). 3 ).

[0145] ① During the first stage (t=0), a sudden sandstorm caused outdoor PM2.5 levels to rise to 200 μg / m³. 3 A red alert is issued (PM2.5 levels are severely exceeded). At this point, the current optimization is interrupted, the system is marked as emergency mode, and the constraints are forcibly modified: γ = 1.8, α = 0, and the battery's SOC lower limit is adjusted to 15%.

[0146] ② The system shuts off external circulation and operates in full-power mode, initiating electrostatic dust removal (e.g., requiring 1.8KW). At this time, direct photovoltaic drive is disconnected, and a strategy of energy storage power supply + community-supplemented fresh air power supply is adopted.

[0147] ③ Determine if the energy storage SOC is greater than 30%. If it is, output the base power (e.g., 1.5KW) through energy storage power supply. Furthermore, calculate the difference power based on the required power: 1.8 - 1.5 = 0.3KW.

[0148] ④ As the energy storage power supply continues to be consumed, the community checks for available power every 2 minutes and sends a power support request (e.g., 0.3KW) to the blockchain microgrid. If neighbor A has surplus power, it provides a price lower than the grid price (0.9 yuan / kwh). After the blockchain microgrid responds to the price, it updates the energy input in the fresh air power supply strategy.

[0149] ⑤ Based on the updated energy input, the minimum value of the preset multi-objective function is recalculated, thereby switching the power supply to 0.3KW of community power + 1.5KW of energy storage to control the operation of the fresh air system.

[0150] ⑥ As the fresh air system continuously circulates internal air, when the indoor PM2.5 level is detected to be within acceptable limits (e.g., PM2.5 = 85 μg / m³), 3 If the red alert is lifted, the normal mode will be restored, and the battery SOC constraint will be adjusted to 20%–80%.

[0151] Second embodiment

[0152] like Figure 4 and Figure 5 As shown, based on the first embodiment described above, the second embodiment of the present invention provides a multi-objective collaborative control device for a community-type photovoltaic energy storage fresh air system, including a blockchain microgrid layer 1, an IAQ adaptive execution layer 2, and a dynamic time-sharing control layer 3.

[0153] Specifically, the blockchain microgrid layer 1 consists of multiple user nodes, smart meters, and P2P transaction contracts. Multiple user nodes within the same community are interconnected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-set P2P transaction contract.

[0154] The IAQ adaptive execution layer 2 includes various environmental sensors and execution units. These environmental sensors are used to monitor the external environment and the indoor environment of each user node in real time, thereby acquiring external environmental parameters and the indoor environmental parameters of each user node. For example... Figure 6 As shown, after obtaining indoor and outdoor environmental parameters through various environmental sensors, the execution unit executes the optimal power allocation command issued by the dynamic time-sharing control layer 3.

[0155] The dynamic time-of-use control layer 3 includes an electricity price forecasting module, a rolling optimizer, and an energy storage management unit. The electricity price forecasting module is connected to the blockchain microgrid layer 1 to retrieve the time-of-use electricity price table by calling the grid API and comparing it with the community electricity price. Figure 7 As shown, the rolling optimizer is connected to the IAQ adaptive execution layer and is used to determine the fresh air power supply strategy corresponding to the user node based on external and indoor environmental parameters. The energy storage management unit is connected to the rolling optimizer and is used to solve for the optimal power allocation command using a preset multi-constraint optimization model based on the fresh air power supply strategy corresponding to the user node. Furthermore, referring to... Figure 4 As shown, the energy storage management unit is also connected to the execution unit to send the optimal power allocation command to the execution unit, thereby controlling each power source in the fresh air power supply strategy to provide the corresponding power.

[0156] It is understood that the functions and connections of each module unit in this embodiment are only one specific implementation of the multi-objective cooperative control method in the first embodiment above. In other embodiments, the functions and connections of each module unit can be adaptively adjusted as needed, and no specific limitations are made here.

[0157] Third Embodiment

[0158] like Figure 8 As shown, based on the above-described multi-objective collaborative control method for community photovoltaic energy storage fresh air systems, the third embodiment of the present invention further provides a multi-objective collaborative control device for community photovoltaic energy storage fresh air systems. This multi-objective collaborative control device includes one or more processors and a memory. The memory is coupled to the processor and is used to store one or more programs. When the program is executed by the processor, the processor implements the multi-objective collaborative control method for community photovoltaic energy storage fresh air systems described in the above embodiment.

[0159] The processor controls the overall operation of the multi-objective collaborative control device to complete all or part of the steps of the aforementioned multi-objective collaborative control method for community photovoltaic energy storage fresh air systems. The processor can be a central processing unit (CPU), graphics processing unit (GPU), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), digital signal processing (DSP) chip, etc. The memory stores various types of data to support the operation of the multi-objective collaborative control device. This data may include, for example, instructions for any application or method operating on the multi-objective collaborative control device, as well as application-related data. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, etc.

[0160] In one exemplary embodiment, the multi-objective collaborative control device may be implemented by a computer chip or physical entity, or by a product with certain functions, to implement the aforementioned multi-objective collaborative control method for community photovoltaic energy storage fresh air systems and achieve the same technical effects as the method described above. A typical embodiment is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0161] In another exemplary embodiment, the present invention also provides a computer-readable storage medium including program instructions that, when executed by a processor, implement the steps of the multi-objective cooperative control method for a community photovoltaic energy storage fresh air system in any of the above embodiments. For example, the computer-readable storage medium may be the aforementioned memory including program instructions, which may be executed by a processor of a multi-objective cooperative control device to complete the aforementioned multi-objective cooperative control method for a community photovoltaic energy storage fresh air system and achieve the same technical effects as the aforementioned method.

[0162] It should be noted that the above embodiments are merely illustrative examples. The technical solutions of each embodiment can be combined, and all are within the protection scope of this invention.

[0163] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0164] The multi-objective collaborative control method and device for community photovoltaic energy storage fresh air systems provided by this invention have been described in detail above. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A multi-objective collaborative control method for community photovoltaic energy storage fresh air systems, characterized in that... Includes the following steps: Based on a blockchain microgrid, multiple user nodes within the same community are pre-interconnected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-set P2P transaction contract; each user node corresponds to a fresh air system, and the community transaction electricity price is lower than the grid price at the current time. Real-time monitoring of the external environment to obtain external environmental parameters; For any one of the user nodes, the indoor environment of that user node is monitored in real time to obtain indoor environmental parameters; Based on the external environmental parameters and the indoor environmental parameters, a fresh air power supply strategy corresponding to the user node is determined; wherein, the fresh air power supply strategy includes at least one or a combination of community power supply, photovoltaic power supply and energy storage power supply. Based on the fresh air power supply strategy corresponding to the user node, the optimal power allocation command is solved using a preset multi-constraint optimization model; specifically, this includes: calculating the minimum value of the model output based on the preset multi-constraint optimization model. ;in, This represents the power purchased by the power grid during time period t; This represents the real-time electricity price during time period t; Indicates the duration of IAQ exceeding the limit; This indicates an optimized time domain length, with a time granularity of 5 minutes. α represents the battery degradation penalty term at time t; α represents the economic weighting coefficient, which is positively correlated with the real-time electricity price; β represents the battery life weighting coefficient, which is positively correlated with the battery health status; γ represents the IAQ weighting coefficient, which is positively correlated with the degree of pollution exceeding the standard. , P represents the charge / discharge power penalty coefficient, κ represents the SOC deviation from the median penalty coefficient, and P represents the charge / discharge power penalty coefficient. batt (t) represents the instantaneous net output power of the energy storage battery at time t; obtain the minimum required air volume corresponding to the fresh air system of the user node; based on the minimum value output by the model and the minimum required air volume, determine the power allocation of the community power supply, photovoltaic power supply and energy storage power supply respectively, so as to form the optimal power allocation instruction; Based on the optimal power allocation command, each power source in the fresh air power supply strategy is controlled to provide corresponding electrical energy. After a preset duration, the external environment and the indoor environment of each user node are re-monitored, and the above process is repeated to achieve multi-objective collaborative control of multiple user nodes in the same community.

2. The multi-objective cooperative control method as described in claim 1, characterized in that... The fresh air power supply strategy is determined through the following steps: When the outdoor PM2.5 exceeds the first threshold, the fresh air system shuts off the external circulation and starts electrostatic dust removal, primarily using fast-response energy storage power supply, supplemented by the community power supply to compensate for the difference. When the outdoor PM2.5 is less than the first threshold and the indoor CO2 concentration is greater than the second threshold, the fresh air system will run at full speed in external circulation, mainly using the economical community power supply, and supplementing the difference through energy storage power supply. When the outdoor PM2.5 is less than the first threshold and the indoor CO2 concentration is less than the second threshold, the fresh air system will operate at the lowest speed, mainly using photovoltaic power supply, and supplementing the difference through energy storage power supply. When the indoor CO2 concentration is less than the second threshold and the indoor VOC concentration is greater than the third threshold, the fresh air system will operate at high speed, mainly using photovoltaic power supply, and supplementing the difference through energy storage power supply. When the outdoor PM2.5 concentration is greater than the first threshold and the indoor CO2 concentration is greater than the second threshold, the fresh air system alternates between external circulation and internal circulation according to the preset duration, so as to perform dynamic time-sharing control based on the current electricity price period. The dynamic time-sharing control includes: When the current electricity price period is a low-price period, if the energy storage battery's SOC is less than 30%, it will be charged first and powered by photovoltaic, community or grid; if the energy storage battery's SOC is greater than 50%, the fresh air will be operated at full speed by energy storage power supply to pre-exchange air through internal circulation. When the current electricity price period is a high-price period, if the energy storage battery's SOC is greater than 40%, the fresh air will be operated according to the preset air volume, and the power supply of photovoltaic power, energy storage supplement and community power supply will be allocated according to the preset ratio; if the energy storage battery's SOC is ≤30%, the response will be graded according to the SOC range: when the SOC is in the range of 20%-30%, the speed will be reduced in stages; when the SOC is in the range of 10%-20%, it will switch to pulse mode; when the SOC is below 10%, it will be forcibly switched to natural ventilation damper.

3. The multi-objective cooperative control method as described in claim 2, characterized in that... The economic efficiency weighting coefficient α, the battery life weighting coefficient β, and the IAQ weighting coefficient γ are calculated using the following formula: ； in, Indicates the base electricity price; The peak value is indicated by k1, which is 1 when the electricity price is at its peak and 0 when the electricity price is at its trough; k1 and k2 represent the preset adjustment coefficients. ； in, This indicates battery health, and the β weight increases by 40% when SOH < 80%. Indicates the rate of change of SOC; This indicates the preset attenuation coefficient; ； in, This indicates the preset emergency factor, and CO2(t) represents the measured value of indoor carbon dioxide concentration at time t.

4. The multi-objective cooperative control method as described in claim 2, characterized in that... The minimum required air volume Q of the fresh air system req Calculated using the following formula: Where V represents the volume of the indoor space; Δt represents the current indoor CO2 concentration; Ctarget represents the target CO2 concentration; Δt represents the time to reach the target concentration; k represents the CO2 generation rate constant; λ represents the particulate matter removal efficiency coefficient; IPM2.5 represents the current indoor PM2.5 concentration, and the target concentration limit for PM2.5 is 35.

5. The multi-objective cooperative control method as described in claim 3, characterized in that: When indoor PM2.5 ≤ 80 μg / m³ 3 When the fresh air system is in normal mode, the economic weight coefficient α, battery life weight coefficient β, and IAQ weight coefficient γ are calculated respectively to determine the minimum value of the multi-constraint optimization model; and the constraint condition for the energy storage battery is: the battery SOC is between 20% and 80%; When 80μg / m 3 Indoor PM2.5 < 150 μg / m³ 3 When this happens, the IAQ weighting coefficient γ is increased; When indoor PM2.5 ≥ 150 μg / m³ 3 When the fresh air system is in emergency mode, α = 0 and γ = 1.8 are directly set to calculate the minimum value of the multi-constraint optimization model; and the constraint condition of the energy storage battery changes as follows: the battery SOC is between 15% and 100%.

6. The multi-objective cooperative control method as described in claim 2, characterized in that: When the outdoor PM2.5 is less than the first threshold and the indoor CO2 concentration is greater than the second threshold, if there is affordable electricity available in the community, the fresh air system will run at full speed; if there is no affordable electricity available in the community, the fresh air system will start running at a preset wind speed and gradually increase the air volume at preset intervals until the fresh air system reaches its maximum wind speed.

7. The multi-objective cooperative control method as described in claim 1, characterized in that: When any of the user nodes experiences a power outage due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the operation of the user node's ventilation infrastructure.

8. A multi-objective collaborative control device for a community-based photovoltaic energy storage fresh air system, used to implement the multi-objective collaborative control method according to any one of claims 1 to 7, characterized in that... include: The blockchain microgrid layer consists of multiple user nodes, smart meters, and P2P transaction contracts. Multiple user nodes within the same community are interconnected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-set P2P transaction contract. The IAQ adaptive execution layer includes a variety of environmental sensors and execution units. The various environmental sensors are used to monitor the external environment and the indoor environment of each user node in real time, thereby obtaining the external environmental parameters and the indoor environmental parameters of each user node. The dynamic time-of-use control layer includes an electricity price prediction module, a rolling optimizer, and an energy storage management unit. The electricity price prediction module is connected to the blockchain microgrid layer to call the grid API to obtain the time-of-use electricity price table and compare it with the community electricity price. The rolling optimizer is connected to the various environmental sensors to determine the fresh air power supply strategy corresponding to the user node based on the external environmental parameters and the indoor environmental parameters. The energy storage management unit is connected to the rolling optimizer and is used to solve the optimal power allocation command according to the fresh air power supply strategy corresponding to the user node through a preset multi-constraint optimization model. The energy storage management unit is connected to the execution unit and is used to send the optimal power allocation command to the execution unit, thereby controlling each power source in the fresh air power supply strategy to provide corresponding electrical energy through the execution unit.

9. A multi-objective collaborative control device for a community-based photovoltaic energy storage fresh air system, characterized in that... It includes a processor and a memory, wherein the processor reads a computer program from the memory for implementing the multi-objective cooperative control method according to any one of claims 1 to 7.

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