Multi-target cooperative control method and device for community photovoltaic energy storage fresh air system

Abstract

The invention discloses a multi-target cooperative control method and device for a community photovoltaic energy storage fresh air system. According to the method, community power sharing between any two user nodes is carried out through a preset P2P transaction contract based on a block chain microgrid; monitoring an external environment and an indoor environment of each user node in real time to obtain external environment parameters and indoor environment parameters of each user node; determining a fresh air power supply strategy corresponding to the user node so as to solve an optimal power distribution instruction through a preset multi-constraint optimization model; based on the optimal power distribution instruction, each power supply source in the fresh air power supply strategy is controlled to provide corresponding electric energy; and after a preset duration, repeating the above process to perform multi-target cooperative control on the plurality of user nodes in the same community. By combining community power supply, photovoltaic power supply and energy storage functions, an optimal fresh air power supply strategy can be matched for each user node.

Description

Technical Field

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

[0002] Fresh air systems are currently widely used in building energy conservation, but traditional solutions often rely on grid power, resulting in high operating costs and limited economic viability. With the increasing adoption of photovoltaic technology, some fresh air systems are beginning to incorporate photovoltaic power generation to reduce their dependence on utility power. However, these "direct photovoltaic drive" solutions lack effective energy storage buffers, resulting in inadequate utilization of excess photovoltaic power, high curtailment rates, and low overall energy efficiency.

[0003] Since then, fresh air systems featuring a "fixed-price electricity purchase + energy storage" have emerged on the market. These solutions utilize energy storage devices to shift peak demand and fill valleys, which can reduce electricity costs to a certain extent. However, since the electricity price is set at a fixed value and cannot be flexibly adjusted based on real-time price fluctuations, it is difficult to achieve better economic efficiency during peak electricity price periods. Furthermore, existing systems generally use a simple threshold trigger mechanism for IAQ (indoor air quality) control. For example, when the CO2 concentration exceeds 1000ppm, the system will operate at full speed. This rigid, "black-or-white" control method can easily cause sharp fluctuations in electricity demand in a short period of time, creating new load spikes, which in turn exacerbate system operational instability and energy consumption pressures.

[0004] In recent years, the application of blockchain technology in distributed energy trading has matured, providing a new technical path for community-level energy sharing and optimization. In theory, it can achieve flexible electricity allocation and cost sharing through peer-to-peer (P2P) trading mechanisms. However, existing technologies have yet to effectively integrate blockchain, photovoltaic power generation, energy storage management, and the operation and control of fresh air systems, resulting in a comprehensive optimization control strategy that simultaneously balances economic efficiency, 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 and fresh air systems to systematically solve the multiple defects of traditional solutions in terms of grid dependence, photovoltaic utilization, energy storage management, electricity price response and IAQ control. Summary of the Invention

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

[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 and fresh air system.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical solutions: According to a first aspect of an embodiment of the present invention, a multi-objective coordinated control method for a community photovoltaic energy storage and fresh air system is provided, comprising the following steps: Based on the blockchain microgrid, multiple user nodes in the same community are pre-connected via the Ethernet protocol, allowing any two user nodes to share community electricity through a preset P2P transaction contract. Each user node corresponds to a fresh air system, and the community transaction electricity price is lower than the current grid price. Monitor the external environment in real time to obtain external environmental parameters; For any of the user nodes, monitor the indoor environment of the user node in real time to obtain indoor environment parameters; Based on the external environmental parameters and the indoor environmental parameters, determining a fresh air power supply strategy corresponding to the user node; wherein the fresh air power supply strategy includes at least one or more combinations of community power supply, photovoltaic power supply, and energy storage power supply; According to the fresh air power supply strategy corresponding to the user node, the optimal power allocation instruction is solved through the preset multi-constraint optimization model; Based on the optimal power allocation instruction, each power supply source in the fresh air power supply strategy is controlled to provide corresponding electric energy; After a preset period of time, the external environment and the indoor environment of each user node are re-monitored and the above process is repeated, thereby performing multi-objective collaborative control of multiple user nodes in the same community.

[0009] Preferably, the fresh air power supply strategy is determined by the following steps: When the outdoor PM2.5 level is greater than the first threshold, the fresh air system shuts down the external circulation and starts electrostatic dust removal, primarily using energy storage with a fast response speed for power supply, and supplementing the difference through community power supply. 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 runs at full speed in the 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 operates at the lowest speed, mainly powered by photovoltaic power, and supplemented by 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 a low speed, mainly powered by photovoltaic power, and supplemented by 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 to perform dynamic time-sharing control based on the current electricity price period; The dynamic time-sharing control includes the following steps: During low-price periods, if the SOC of the energy storage battery is less than 30%, it will be charged first and powered by photovoltaic power, community power, or the grid. If the SOC of the energy storage battery is greater than 50%, the energy storage power supply will be used to run the fresh air at full speed to perform pre-ventilation through internal circulation. When the current electricity price period is a high-price period, if the energy storage SOC of the energy storage battery is greater than 40%, fresh air will be operated according to the preset air volume, and the power supply of photovoltaic power supply, energy storage supplement and community power supply will be allocated according to the preset ratio; if the energy storage SOC of the energy storage battery is ≤30%, a graded response will be made according to the SOC range: when the SOC is in the range of 20% to 30%, the speed will be reduced in steps; when the SOC is in the range of 10% to 20%, it will switch to pulse mode; when the SOC is lower than 10%, it will be forced to switch to the natural ventilation damper.

[0010] Preferably, the method of solving the optimal power allocation instruction according to the fresh air power supply strategy corresponding to the user node through a preset multi-constraint optimization model specifically includes: According to the preset multi-constraint optimization model, calculate the minimum value of the model output ; in, represents the power purchased by the power grid during period t; represents the real-time electricity price during period t; Indicates the duration of IAQ exceeding the standard; Indicates optimizing the time domain length and using 5 minutes as the time granularity; represents the battery degradation penalty term at time t; α represents the economic weight coefficient, which is positively correlated with the real-time electricity price; β represents the battery life weight coefficient, which is positively correlated with the battery health status; γ represents the IAQ weight coefficient, which is positively correlated with the degree of pollution exceeding the standard; ; in, represents the penalty coefficient for charge and discharge power, and k represents the penalty coefficient for SOC deviation from the median; Get 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 wind volume, the power allocation amounts for community power supply, photovoltaic power supply, and energy storage power supply are determined to form the optimal power allocation instruction.

[0011] Preferably, the economic weight coefficient α, the battery life weight coefficient β, and the IAQ weight coefficient γ are calculated by the following steps: ; in, Indicates the basic electricity price; Indicates the peak mark, 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, Indicates battery health, and when SOH < 80%, the β weight increases by 40%; Indicates the SOC change rate; Indicates the preset attenuation coefficient; ; in, Indicates the preset emergency factor.

[0012] Preferably, the minimum required air volume Q of the fresh air system is req Calculated by the following formula: Among them, V represents the volume of indoor space; 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 particle removal efficiency coefficient; IPM2.5 represents the current indoor PM2.5 concentration, and the target concentration limit of PM2.5 is 35.

[0013] The best case scenario is when indoor PM2.5 is ≤80μg / m 3 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, thereby calculating the minimum value of the multi-constraint optimization model; and the constraint condition of the energy storage battery is: the battery SOC is between 20% and 80%; When 80 μg / m 3 <Indoor PM2.5<150μg / m 3 When , the IAQ weight coefficient γ is increased; When indoor PM2.5 ≥ 150 μg / m 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 to: the battery SOC is between 15% and 100%.

[0014] Preferably, 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 economical low-priced electricity in the community, the fresh air system will run at full speed; if there is no economical low-priced electricity in the community, the fresh air system will start running at the preset wind speed and increase the air volume in steps at preset time intervals until the fresh air system reaches the maximum wind speed.

[0015] Preferably, when any of the user nodes loses power due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the basic operation of the fresh air of the user node.

[0016] According to a second aspect of an embodiment of the present invention, a multi-objective coordinated control device for a community photovoltaic energy storage and fresh air system is provided, comprising: The blockchain microgrid layer consists of multiple user nodes, smart meters, and P2P transaction contracts. Multiple user nodes in the same community are interconnected through the Ethernet protocol, so that any two user nodes can share community electricity through a preset P2P transaction contract. The IAQ adaptive execution layer includes multiple environmental sensors and execution units. The multiple environmental sensors are used to monitor the external environment and the indoor environment of each user node in real time, thereby obtaining external environmental parameters and indoor environmental parameters of each user node; The dynamic time-sharing 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-sharing electricity price table and compare it with the community electricity price; the rolling optimizer is connected to the multiple environmental sensors and is used 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 instruction through a preset multi-constraint optimization model based on the fresh air power supply strategy corresponding to the user node; The energy storage management unit is connected to the execution unit and is used to send the optimal power allocation instruction to the execution unit, so that the execution unit controls each power supply source in the fresh air power supply strategy to provide corresponding electric energy.

[0017] According to a third aspect of an embodiment of the present invention, a multi-objective collaborative control device for a community-type photovoltaic energy storage and fresh air system is provided, comprising a processor and a memory, wherein the processor reads a computer program in the memory to implement the above-mentioned multi-objective collaborative control method.

[0018] Compared with the prior art, the present invention has the following technical effects: (1) Through the blockchain microgrid, community power sharing is carried out to effectively reduce the cost of purchasing electricity from the power grid. In addition, 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.

[0019] (2) The SOC of the energy storage battery is constrained, which greatly improves the battery cycle life; and, in emergency conditions, the lower limit of the SOC of the energy storage battery can be relaxed to ensure the reliability of fresh air operation.

[0020] (3) At preset intervals, rolling optimization is performed based on the community electricity price, battery energy storage results, and photovoltaic power supply results to ensure that the fresh air system continues to operate stably at a low cost.

[0021] (4) Provide emergency protection measures for fresh air supply. When any user node loses power due to extreme weather, the blockchain microgrid will forcibly allocate shared electricity to ensure the basic operation of fresh air for that user node. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the overall process of a multi-objective collaborative control method for a community photovoltaic energy storage and fresh air system provided by the first embodiment of the present invention; Figure 2 This is a schematic diagram of community power sharing in the first embodiment of the present invention; Figure 3 This is a schematic diagram of an operation case of the multi-objective collaborative control method in the first embodiment of the present invention; Figure 4 A structural diagram of a multi-objective coordinated control device for a community-based photovoltaic energy storage and fresh air system provided by the second embodiment of the present invention; Figure 5 A flowchart of a multi-objective collaborative control device for a community-based photovoltaic energy storage and fresh air system provided by the second embodiment of the present invention; Figure 6 This is a flowchart of the IAQ adaptive control layer in the second embodiment of the present invention; Figure 7 This is a flow chart of dynamically optimizing the optimal power allocation instruction in the second embodiment of the present invention; Figure 8 This is a structural diagram of a multi-objective collaborative control device for a community-based photovoltaic energy storage and fresh air system provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical content of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] The embodiment of the present invention provides a multi-objective collaborative control method and device for a community photovoltaic energy storage fresh air system, which is mainly applicable to fresh air system control scenarios in community photovoltaic microgrid environments, and is particularly suitable for residential or commercial buildings that need to dynamically balance energy economy, battery life and indoor air quality. The fresh air system has photovoltaic panels for photovoltaic power supply and energy storage batteries for storing or releasing electrical energy. By adjusting the dynamic weights of multiple objectives, the emphasis is placed on economy during peak electricity prices and on IAQ (indoor air quality) during peak pollution. In addition, a battery attenuation penalty term is added to the optimization objectives to avoid frequent deep charging and discharging of the battery. In addition, based on different environmental monitoring data, a combination of community power supply, photovoltaic power supply and energy storage power supply is used to solve the multi-objective contradiction of optimizing energy consumption, air quality and equipment life.

[0025] First embodiment 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 and fresh air system, which specifically includes the following steps: S10: Community power sharing.

[0026] In this embodiment, based on a blockchain microgrid, multiple user nodes within the same community are pre-connected via the Ethernet protocol, enabling any two user nodes to share community electricity through a pre-set P2P transaction contract. It is understood that each user node corresponds to a fresh air system, and the community transaction price is lower than the current grid price.

[0027] like Figure 2 As shown in the example, user A posts a request to sell 1.5kW of electricity. The smart P2P trading contract matches user B's 1.2kW request based on min(grid price × 80%, neighbor's quote). B's energy storage unit receives the electricity. Transaction data is uploaded to the blockchain, ensuring transparency and auditability.

[0028] S20: Monitor the external environment in real time to obtain external environment parameters.

[0029] Specifically, a temperature and humidity sensor is provided to monitor the external temperature and humidity, and a laser light scattering sensor is provided to monitor the outdoor PM2.5 value. In this embodiment, the outdoor temperature and humidity and PM2.5 are primarily collected as external environmental parameters. In other embodiments, other sensors may be used to monitor more environmental parameters as needed, which are not specifically limited here.

[0030] S30: Monitor the indoor environment of each user node in real time.

[0031] Specifically, for any user node, indoor environmental parameters are collected on a household basis to obtain the indoor environmental parameters of each user node and finally aggregate them on the blockchain platform. Among them, the indoor environmental parameters include at least: indoor CO2 concentration, PM2.5 and indoor VOC (Volatile Organic Compounds) concentration.

[0032] S40: Determine the fresh air power supply strategy corresponding to each user node.

[0033] After the external environment parameters and the indoor environment parameters of each user node are acquired 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 environment parameters of the user node with the external environment parameters.

[0034] 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 understandable that in this embodiment, each user node is an independent decision-making entity, and thus corresponds to an independent fresh air power supply strategy.

[0035] Specifically, in this embodiment, the fresh air power supply strategies include the following: (1) Energy storage power supply + community supplement

[0036] Specifically, when the outdoor PM2.5 is greater than the first threshold (for example, outdoor PM2.5>75μg / m 3 ), it means that the outdoor PM2.5 exceeds the standard. At this time, the fresh air system needs instant high-precision filtration, so the fresh air system needs to shut down the external circulation and start electrostatic dust removal.

[0037] When electrostatic precipitation is enabled, a stable high-voltage power supply is required. However, the volatility of photovoltaic power supply can reduce dust removal efficiency. Furthermore, there is a risk of harmonic interference between the photovoltaic DC power and the electrostatic precipitation module. Therefore, it is necessary to disable photovoltaic direct drive and prioritize energy storage (which has a faster power response) for power supply, with community power supply as a secondary option to make up the difference.

[0038] (2) Community power supply + energy storage supplement.

[0039] Specifically, when the outdoor PM2.5 is less than the first threshold (for example, outdoor PM2.5 < 75 μg / m 3 ), it means that the outdoor PM2.5 is within the standard and external circulation can be turned on. At this time, it is necessary to determine whether the indoor CO2 concentration exceeds the standard. If the indoor CO2 concentration exceeds the standard (for example: indoor CO2 concentration > 1000ppm), rapid and large air volume ventilation is required. Therefore, the fresh air system needs to run the external circulation at full speed.

[0040] When the fresh air system is running at full speed in the external circulation, the photovoltaic power supply may be insufficient (for example, only 0.3 to 0.8 kW on a cloudy day). Therefore, the highly economical community power supply is mainly used (the average community electricity price is lower than the grid price), and the difference is supplemented by energy storage power supply to ensure higher reliability.

[0041] It should be understood that if there is low-cost electricity in the community with high economic efficiency, the fresh air system will run at full speed; if there is no low-cost electricity in the community with high economic efficiency, the fresh air system will start running at the preset wind speed (energy storage power supply is used at this time) and increase the air volume in steps at preset time intervals (for example: the air volume increases by 20% every 10 minutes) until the fresh air system reaches the maximum wind speed.

[0042] (3) Photovoltaic power supply + energy storage supplement.

[0043] Specifically, when outdoor PM2.5 levels and indoor CO2 concentrations are within standards, there's no demand for fresh air. At this point, the fresh air system operates at its lowest speed to ensure basic ventilation. Therefore, to maximize economic efficiency, photovoltaic power generation is the primary source of power, with energy storage supplementing the difference.

[0044] Furthermore, when both outdoor PM2.5 and indoor CO2 concentrations are within standards, but indoor VOC concentrations exceed standards (for example, indoor VOC > 500 ppb), an appropriate amount of fresh air is needed to reduce indoor VOC concentrations. In this case, the fresh air system operates at a low speed and can perform activated carbon filtration. Rapid air flow is not required, so photovoltaic power generation is the primary source (fluctuations are permitted), with energy storage providing the difference.

[0045] It is understood that in this embodiment, when the fresh air system does not have an urgent need for fresh air ventilation, it can operate at a low or medium speed, thereby providing power through photovoltaic direct drive to ensure maximum economic efficiency. When photovoltaic direct drive cannot meet the power demand, energy storage power supply is used as an emergency measure to ensure the reliability of the fresh air system operation.

[0046] (4) Dynamic time-sharing control + community supplement.

[0047] Specifically, when both outdoor PM2.5 and indoor CO2 concentrations exceed the standard (for example, outdoor PM2.5 > 75 μg / m 3 and the indoor CO2 concentration is greater than 1000ppm), the fresh air system needs to alternately perform external circulation and internal circulation according to the preset time (for example: 5 minutes) to reduce the indoor CO2 concentration through external circulation and reduce the indoor PM2.5 concentration through internal circulation.

[0048] In this embodiment, the dynamic time-sharing control strategy specifically includes: 1) Operation logic during low price period ① SOC < 30%: Priority charging: Grid off-peak charging, community P2P charging with electricity at a price lower than the grid price, and storage of excess photovoltaic power. Maintain a minimum fresh air flow (0.3 m / s), with direct power supply from photovoltaics, community services, and the grid (not relying on energy storage).

[0049] ②SOC ≥ 50%, energy storage power supply runs at full speed (internal circulation pre-ventilation).

[0050] ③ Extreme scenario (no electricity in the community during charging): PV real-time power generation directly drives the minimum wind speed. If PV = 0, grid power supply is enabled (still low-priced electricity), and operation is not interrupted, but the power is limited to 50% of the rated value.

[0051] 2) Operation logic during high price period: ① SOC ≤ 30%: Graded response: SOC range 20%-30%, step-down (reducing air volume by 20% every 10 minutes), PV + 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 switching to natural ventilation damper.

[0052] ② When SOC > 40%, the system operates at the preset wind speed and uses the following power supply combinations: "PV direct supply": 60%, "storage supplement": 30%, and "community power": 10%.

[0053] In addition, in this embodiment, when indoor PM2.5≤80μg / m 3 When the fresh air system is in normal mode, the operating constraints for the energy storage battery are: battery SOC is between 20% and 80%. When the indoor PM2.5 is ≥150μg / m 3 When the fresh air system is in emergency mode, the operating constraints on the energy storage battery are relaxed to 15% to 100%. Understandably, in the high-price period operation logic of dynamic time-sharing control, a step-by-step response should normally be implemented within the SOC ≥ 20%. If the SOC falls below 20%, an alarm is triggered and a more conservative mode is switched (for example, pulse mode or forced switching to natural ventilation dampers).

[0054] The grid serves as a tertiary backup power source, taking precedence over photovoltaic power, community P2P power, and energy storage (for emergency discharge). Its main functions include: 1) directly driving the fresh air system when photovoltaic power, community P2P power, and energy storage are unable to meet demand (e.g., extreme weather combined with a community power outage and depleted energy storage); 2) charging the energy storage battery under specific conditions (only during off-peak hours and requiring manual user authorization); and 3) providing power for equipment maintenance. Grid intervention to directly drive the fresh air system requires the following conditions to be met: ① insufficient photovoltaic power generation; ② no available community P2P power; and ③ energy storage SOC < 20%.

[0055] Furthermore, in this embodiment, the energy storage battery follows the principle of "immediate consumption priority": preferentially charging with excess photovoltaic power. The charging priority is: photovoltaic (when in excess) > community P2P (when in excess) > grid (only during off-peak hours, when energy storage is insufficient, and with user manual authorization). Discharge conditions are: 1) when photovoltaic and community P2P power supply is insufficient to meet demand, and direct grid power supply is unsuitable or impossible; 2) when an IAQ emergency requires a rapid response.

[0056] S50: Solving the optimal power allocation instruction through a preset multi-constraint optimization model.

[0057] After the fresh air power supply strategy corresponding to each user node is determined based on the above step S40, it is necessary to solve the optimal power allocation instruction through a preset multi-constraint optimization model to determine the power required to be provided by each power supply mode.

[0058] Specifically, the following steps are included: S51: Calculate the minimum value of the model output according to the preset multi-constraint optimization model.

[0059] In this embodiment, the calculation formula of the multi-constraint optimization model is as follows: in, represents the power purchased by the power grid during period t; represents the real-time electricity price during period t; Indicates the duration of IAQ exceeding the standard; represents the total length of the optimization horizon (e.g., 6 hours), where 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; represents the battery degradation penalty term at time t; α represents the economic weight coefficient, which is positively correlated with the real-time electricity price; β represents the battery life weight coefficient, which is positively correlated with the battery health status; γ represents the IAQ weight coefficient, which is positively correlated with the degree of pollution exceeding the standard; ; in, represents the penalty coefficient for charge and discharge power, and k represents the penalty coefficient for SOC deviation from the median.

[0060] Furthermore, the economic weight coefficient α, battery life weight coefficient β, and IAQ weight coefficient γ are calculated using the following formula: ; in, Indicates the basic electricity price; Indicates the peak mark, which is 1 when the electricity price is at the peak and 0 when the electricity price is at the trough; k1 and k2 represent the preset adjustment coefficients. In addition, it should be understood that in this embodiment, the peak mark Only the peak or valley of electricity price is discussed as an example, without adding multiple ladder electricity price cases. In other embodiments, additional linear transition functions can be added as needed. For example: in the case of linear electricity price, 1.1 yuan corresponds to The corresponding value is 0.75 or 0.9 yuan. The value is 0.25. When the electricity price is nonlinear, the parameters can be set manually.

[0061] ; in, Indicates battery health, and when SOH < 80%, the β weight increases by 40%; Indicates the SOC change rate; Indicates the preset attenuation coefficient.

[0062] ; in, Indicates the preset emergency factor.

[0063] In addition, 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 calculate the minimum value of the multi-constraint optimization model; and the constraint condition of the energy storage battery is: the battery SOC is between 20% and 80%.

[0064] When 80 μg / m 3 <Indoor PM2.5<150μg / m 3 , the IAQ weight coefficient γ is increased by 40% (i.e., from γ to 1.4γ).

[0065] 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%.

[0066] S52: Calculate the minimum required air volume of the fresh air system.

[0067] Specifically, in this embodiment, the minimum required air volume Q of the fresh air system req Calculated by the following formula: Among them, V represents the volume of indoor space; 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 particle removal efficiency coefficient; IPM2.5 represents the current indoor PM2.5 concentration, and the target concentration limit of PM2.5 is 35.

[0068] S53: Based on the minimum value of the model output and the minimum required air volume of the fresh air system, the power allocation amounts of the community power supply, photovoltaic power supply, and energy storage power supply are determined to form an optimal power allocation instruction.

[0069] Specifically, in this embodiment, power distribution is performed in the following manner: .

[0070] In addition, the power allocation priorities of the three power supply modes are as follows: First priority: direct photovoltaic supply, full utilization of real-time power generation .

[0071] Second priority, community supplement, purchase by price, demand .

[0072] The third priority is energy storage filling, which is used to make up for the difference between the sum of the power of the first two levels of power supply (photovoltaic direct supply and community supplement) and the power P_req corresponding to the minimum required wind volume Q_req.

[0073] The following is an example of dynamic allocation: 0.8kW (calculated minimum demand), The current photovoltaic power generation is 0.5kW, and the available electricity in the community is 0.4kW@0.6 yuan (50% cheaper than the grid (1.2 yuan)). The final distribution plan is: photovoltaic power supply 0.5kW, community power supply 0.3kW, and energy storage power supply is not enabled.

[0074] S60: Control each power supply source in the fresh air power supply strategy to provide corresponding electric energy.

[0075] Specifically, after the power required to be output for each power supply mode is determined based on the above step S50, each power supply source in the fresh air power supply strategy is controlled to provide corresponding electric energy.

[0076] After the preset time (for example, 10 minutes), the process returns to step S20 and repeats steps S20 to S60, thereby achieving multi-objective collaborative control of multiple user nodes in the same community.

[0077] In addition, in this embodiment, preferably, when any user node loses power due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the basic operation of the fresh air of the user node.

[0078] The following combination Figure 3 As shown, the working process of the multi-objective collaborative control method of an embodiment of the present invention is explained with an actual case: Assume the environment is a sandstorm + the current peak electricity price (peak electricity price 1.2 yuan / kWh), the initial state SOC = 65%, indoor CO2 = 700ppm, and the outdoor environment emergency: sandstorm weather (outdoor PM2.5 from 35→200μg / m 3 ).

[0079] ① In the first stage (t=0), due to the sudden sandstorm, the outdoor PM2.5 increased to 200μg / m 3 , a red alert is issued (PM2.5 seriously exceeds the standard). At this time, the current optimization is interrupted, marked as emergency mode, and the constraints are forced to be modified: γ = 1.8, α = 0, and the battery SOC lower limit is adjusted to 15%.

[0080] ② The system shuts off external circulation, operates at full power, and activates electrostatic dust removal (e.g., requiring 1.8 kW). At this point, the PV direct drive is disconnected, and a strategy of energy storage power supply plus community supplementary fresh air power supply is adopted.

[0081] ③ Determine whether the energy storage SOC is greater than 30%. If so, output the base power (e.g., 1.5 kW) through energy storage power supply. Furthermore, calculate the power difference based on the required power: 1.8 - 1.5 = 0.3 kW.

[0082] ④ As the energy storage power supply continues to be consumed, the community is checked every 2 minutes to see if there is available power, and a power support request (for example: 0.3KW) is sent to the blockchain microgrid. If neighbor A has surplus power, it provides a quote lower than the grid price (0.9 yuan / kwh). After the blockchain microgrid responds to the quote, it updates the energy input in the fresh air power supply strategy.

[0083] ⑤ Based on the updated energy input, the minimum value of the preset multi-objective function is recalculated to switch the power supply source, 0.3KW community power + 1.5KW energy storage, to control the operation of the fresh air system.

[0084] ⑥ As the fresh air system continues to circulate internally, when the indoor PM2.5 is detected to be qualified (for example: PM2.5 = 85μg / m 3 ), the red alarm is lifted, normal mode is restored, and the battery SOC constraint is adjusted to 20% to 80%.

[0085] Second embodiment like Figure 4 and Figure 5 As shown, based on the above-mentioned first embodiment, the second embodiment of the present invention provides a multi-objective collaborative control device for a community-type photovoltaic energy storage and fresh air system, including a blockchain microgrid layer 1, an IAQ adaptive execution layer 2, and a dynamic time-sharing control layer 3.

[0086] Specifically, the blockchain microgrid layer 1 consists of multiple user nodes, smart meters and P2P transaction contracts. Multiple user nodes in the same community are interconnected through the Ethernet protocol, so that any two user nodes can share community electricity through a preset P2P transaction contract.

[0087] The IAQ adaptive execution layer 2 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 environment parameters and the indoor environment parameters of each user node. Figure 6 As shown, after the indoor and outdoor environmental parameters are acquired through a variety of environmental sensors, the execution unit is used to execute the optimal power allocation instruction issued by the dynamic time-sharing control layer 3.

[0088] The dynamic time-sharing control layer 3 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 1 to call the grid API to obtain the time-sharing electricity price table and compare 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 according to 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 instruction through the preset multi-constraint optimization model according to the fresh air power supply strategy corresponding to the user node. And, referring to Figure 4 As shown, the energy storage management unit is also connected to the execution unit for sending the optimal power allocation instruction to the execution unit, thereby controlling each power supply source in the fresh air power supply strategy to provide corresponding electric energy through the execution unit.

[0089] It can be understood that the functions and connection relationships of each module unit in this embodiment are only a specific implementation method for realizing the multi-objective collaborative control method in the first embodiment mentioned above. In other embodiments, the functions and connection relationships of each module unit can be adaptively adjusted as needed, and no specific limitation is made here.

[0090] Third embodiment like Figure 8As shown, based on the above-mentioned multi-objective coordinated control method for community photovoltaic energy storage and fresh air systems, the third embodiment of the present invention further provides a multi-objective coordinated control device for community photovoltaic energy storage and fresh air systems. The multi-objective coordinated 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 programs are executed by the processor, the processor implements the multi-objective coordinated control method for community photovoltaic energy storage and fresh air systems described in the above-mentioned embodiment.

[0091] The processor is used to control the overall operation of the multi-objective coordinated control device to complete all or part of the steps of the multi-objective coordinated control method for community photovoltaic energy storage and fresh air systems. The processor can be a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a digital signal processing (DSP) chip, etc. The memory is used to store various types of data to support the operation of the multi-objective coordinated control device. This data may include, for example, instructions for any application or method operating on the multi-objective coordinated control device, as well as application-related data. The memory can be implemented by 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.

[0092] In an exemplary embodiment, the multi-objective collaborative control device can be implemented by a computer chip or entity, or by a product with a certain function, to implement the multi-objective collaborative control method for the community photovoltaic energy storage and fresh air system described above, and achieve the same technical effect as the above method. A typical embodiment is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0093] In another exemplary embodiment, the present invention further provides a computer-readable storage medium comprising program instructions, which, when executed by a processor, implement the steps of the multi-objective coordinated control method for a community photovoltaic energy storage and fresh air system described in any of the aforementioned embodiments. For example, the computer-readable storage medium may be the aforementioned memory comprising the program instructions, which may be executed by a processor of the multi-objective coordinated control device to implement the multi-objective coordinated control method for a community photovoltaic energy storage and fresh air system described above, achieving the same technical effects as the aforementioned method.

[0094] It should be noted that the above embodiments are merely examples, and the technical solutions of the various embodiments may be combined and are all within the scope of protection of the present invention.

[0095] 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 the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0096] The above describes in detail the multi-objective coordinated control method and apparatus for a community photovoltaic energy storage and fresh air system provided by the present invention. For those skilled in the art, any obvious modifications made to this invention without departing from its essence will constitute an infringement of the present invention's patent rights and will result in corresponding legal liability.

Claims

1. A multi-objective collaborative control method for community photovoltaic energy storage and fresh air system, characterized by The steps include: Based on the blockchain microgrid, multiple user nodes in the same community are pre-connected via the Ethernet protocol, allowing any two user nodes to share community electricity through a preset P2P transaction contract. Each user node corresponds to a fresh air system, and the community transaction electricity price is lower than the current grid price. Monitor the external environment in real time to obtain external environmental parameters; For any of the user nodes, monitor the indoor environment of the user node in real time to obtain indoor environment parameters; Based on the external environmental parameters and the indoor environmental parameters, determining a fresh air power supply strategy corresponding to the user node; wherein the fresh air power supply strategy includes at least one or more combinations of community power supply, photovoltaic power supply, and energy storage power supply; According to the fresh air power supply strategy corresponding to the user node, the optimal power allocation instruction is solved through the preset multi-constraint optimization model; Based on the optimal power allocation instruction, each power supply source in the fresh air power supply strategy is controlled to provide corresponding electric energy; After a preset period of time, the external environment and the indoor environment of each user node are re-monitored and the above process is repeated, thereby performing multi-objective collaborative control of multiple user nodes in the same community.

2. The multi-objective collaborative control method according to claim 1, characterized in that The fresh air power supply strategy is determined by the following steps: When the outdoor PM2.5 level is greater than the first threshold, the fresh air system shuts down the external circulation and starts electrostatic dust removal, primarily using energy storage with a fast response speed for power supply, and supplementing the difference through community power supply. 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 runs at full speed in the 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 operates at the lowest speed, mainly powered by photovoltaic power, and supplemented by 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 a low speed, mainly powered by photovoltaic power, and supplemented by 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 to perform dynamic time-sharing control based on the current electricity price period; The dynamic time-sharing control includes: During low-price periods, if the SOC of the energy storage battery is less than 30%, it will be charged first and powered by photovoltaic power, community power, or the grid. If the SOC of the energy storage battery is greater than 50%, the energy storage power supply will be used to run the fresh air at full speed to perform pre-ventilation through internal circulation. When the current electricity price period is a high-price period, if the energy storage SOC of the energy storage battery is greater than 40%, fresh air will be operated according to the preset air volume, and the power supply of photovoltaic power supply, energy storage supplement and community power supply will be allocated according to the preset ratio; if the energy storage SOC of the energy storage battery is ≤30%, a graded response will be made according to the SOC range: when the SOC is in the range of 20%-30%, the speed will be reduced in steps; when the SOC is in the range of 10%-20%, it will switch to pulse mode; when the SOC is lower than 10%, it will be forced to switch to the natural ventilation damper.

3. The multi-objective collaborative control method according to claim 2, characterized in that The method of solving the optimal power allocation instruction according to the fresh air power supply strategy corresponding to the user node through a preset multi-constraint optimization model specifically includes: According to the preset multi-constraint optimization model, calculate the minimum value of the model output ； in, represents the power purchased by the power grid during period t; represents the real-time electricity price during period t; Indicates the duration of IAQ exceeding the standard; Indicates optimizing the time domain length and using 5 minutes as the time granularity; represents the battery degradation penalty term at time t; α represents the economic weight coefficient, which is positively correlated with the real-time electricity price; β represents the battery life weight coefficient, which is positively correlated with the battery health status; γ represents the IAQ weight coefficient, which is positively correlated with the degree of pollution exceeding the standard; ； in, represents the penalty coefficient for charge and discharge power, and k represents the penalty coefficient for SOC deviation from the median; Get 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 wind volume, the power allocation amounts for community power supply, photovoltaic power supply, and energy storage power supply are determined to form the optimal power allocation instruction.

4. The multi-objective collaborative control method according to claim 3, characterized in that The economic weight coefficient α, the battery life weight coefficient β, and the IAQ weight coefficient γ are calculated using the following formula: ； in, Indicates the basic electricity price; Indicates the peak mark, 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, Indicates battery health, and when SOH < 80%, the β weight increases by 40%; Indicates the SOC change rate; Indicates the preset attenuation coefficient; ； in, Indicates the preset emergency factor.

5. The multi-objective collaborative control method according to claim 3, characterized in that The minimum required air volume Q of the fresh air system req Calculated by the following formula: Among them, V represents the volume of indoor space; 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 particle removal efficiency coefficient; IPM2.5 represents the current indoor PM2.5 concentration, and the target concentration limit of PM2.5 is 35.

6. The multi-objective coordinated control method according to claim 4, wherein: When indoor PM2.5≤80μg / m 3 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, thereby calculating the minimum value of the multi-constraint optimization model; and the constraint condition of the energy storage battery is: the battery SOC is between 20% and 80%; When 80 μg / m 3 <Indoor PM2.5<150μg / m 3 When , the IAQ weight coefficient γ is increased; When indoor PM2.5 ≥ 150 μg / m 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 to: the battery SOC is between 15% and 100%.

7. The multi-objective coordinated control method according to claim 2, wherein: 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 economical and low-priced electricity in the community, the fresh air system will run at full speed; if there is no economical and low-priced electricity in the community, the fresh air system will start running at the preset wind speed and increase the air volume in steps at preset time intervals until the fresh air system reaches the maximum wind speed.

8. The multi-objective coordinated control method according to claim 1, wherein: When any of the user nodes loses power due to extreme weather, the blockchain microgrid will forcibly allocate shared power to ensure the basic operation of the fresh air of the user node.

9. A multi-objective collaborative control device for a community-type photovoltaic energy storage and fresh air system, used to implement the multi-objective collaborative control method according to any one of claims 1 to 8, characterized in that include: The blockchain microgrid layer consists of multiple user nodes, smart meters, and P2P transaction contracts. Multiple user nodes in the same community are interconnected through the Ethernet protocol, so that any two user nodes can share community electricity through a preset P2P transaction contract. The IAQ adaptive execution layer includes multiple environmental sensors and execution units. The multiple environmental sensors are used to monitor the external environment and the indoor environment of each user node in real time, thereby obtaining external environmental parameters and indoor environmental parameters of each user node; The dynamic time-sharing 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-sharing 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 instruction through a preset multi-constraint optimization model according to the fresh air power supply strategy corresponding to the user node; The energy storage management unit is connected to the execution unit and is used to send the optimal power allocation instruction to the execution unit, so that the execution unit controls each power supply source in the fresh air power supply strategy to provide corresponding electric energy.

10. A multi-objective collaborative control device for a community-based photovoltaic energy storage and fresh air system, characterized in that It comprises a processor and a memory, wherein the processor reads a computer program in the memory to implement the multi-objective collaborative control method according to any one of claims 1 to 8.

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