Household optical storage integrated intelligent distribution method and system

Abstract

The invention discloses a household light storage integrated intelligent distribution method and system, and the method comprises the steps: building a household photovoltaic-load-energy storage energy flow model through obtaining the real-time power generation power data of a household side photovoltaic power generation system, the real-time power utilization power data of a household power utilization load, and the operation state data of a household energy storage system; and the feasible constraint condition of each energy flow direction is determined in the model. And on the basis, optimization calculation is carried out on the distribution proportion of the photovoltaic power generation power among the household load, the household energy storage system and the power grid in combination with power grid electricity price information and energy storage battery safety constraints, a target energy distribution strategy is generated, and the charging and discharging states of the energy storage system and the power interaction relation between the household and the power grid are controlled according to the target energy distribution strategy. According to the method, on the premise that safe operation of the energy storage system is guaranteed, the photovoltaic self-generation and self-use proportion is improved, the household energy consumption cost is reduced, and intelligent and refined management of household side energy consumption is achieved.

Description

Technical Field

[0001] This specification relates to the field of energy management, and more specifically, this application relates to a smart distribution method and system for integrated home photovoltaic and energy storage. Background Technology

[0002] With the popularization of distributed photovoltaic (PV) and residential energy storage technologies, residential energy systems have gradually formed, integrating PV power generation, energy storage batteries, and various types of electrical loads. Compared to the traditional residential energy consumption model that only draws power from the grid, integrated PV and energy storage systems have significant advantages in improving the utilization rate of renewable energy, reducing electricity costs, and alleviating peak-valley pressure on the grid. However, due to the obvious randomness and intermittency of residential PV power generation, the uncertainty of changes in residential electrical load in terms of time and power, and the multiple constraints of energy storage batteries such as state of charge, temperature, and lifespan degradation, the rational allocation of residential PV power among residential loads, energy storage systems, and the grid becomes a complex multi-constraint, multi-objective problem.

[0003] In existing technologies, energy management methods for residential photovoltaic (PV) and energy storage systems are mostly based on simple priority rules or static threshold control strategies. For example, they prioritize meeting household loads, using surplus power for energy storage charging or grid connection, or simply triggering energy storage discharge during peak electricity prices to reduce purchased power. These methods typically focus only on instantaneous power balance or short-term economics, lacking characterization of the differentiated characteristics of household loads and failing to distinguish between rigid and adjustable loads in terms of energy demand. Furthermore, they fail to systematically incorporate the operating status and lifespan of energy storage batteries into the decision-making process, easily leading to frequent charge-discharge cycles or even deep cycling, thus accelerating battery aging. In addition, existing solutions generally do not consider energy budget and responsibility constraints for household energy consumption over a longer timescale. When PV output fluctuates or load changes occur, problems such as frequent switching of dispatch strategies, long-term suppression of local loads, or repeated fluctuations in grid dependence can easily arise, making it difficult to achieve stable, explainable, and intelligent allocation control that conforms to users' long-term energy consumption preferences.

[0004] Therefore, there is an urgent need for a smart allocation method for integrated home photovoltaic and energy storage that can establish a unified energy flow model among home photovoltaics, loads, energy storage and the grid, and optimize the allocation of photovoltaic power generation for multiple objectives while meeting the requirements of energy storage safety and grid constraints, so as to achieve refined management and long-term stable operation of energy consumption on the home side. Summary of the Invention

[0005] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0006] Firstly, this application proposes a smart allocation method integrating home photovoltaic and energy storage, including: Acquire real-time power generation data of the household photovoltaic power generation system, real-time power consumption data of the household electrical load, and operating status data of the household energy storage system. The operating status data includes the state of charge (SOC) and temperature parameters of the energy storage battery. Based on the above photovoltaic power generation data, the above electricity load data, and the above operating status data, an energy flow model of household photovoltaic-load-energy storage is constructed, and feasible constraints for each energy flow direction are determined in the above energy flow model. Based on the above energy flow model, combined with grid electricity price information and energy storage battery safety constraints, the distribution ratio of household photovoltaic power generation among household load, household energy storage system and grid is optimized and calculated to generate target energy distribution strategy. Based on the aforementioned target energy distribution strategy, the charging and discharging status of the aforementioned home energy storage system and the power interaction relationship between the home and the power grid are controlled to achieve intelligent distribution of energy consumption on the home side.

[0007] In one feasible implementation, based on the photovoltaic power generation data, the electricity load data, and the operating status data, a household photovoltaic-load-energy storage energy flow model is constructed, and feasible constraints for each energy flow direction are determined in the energy flow model, including: Based on the real-time power consumption data of the household electrical load, the load type of the household electrical load is identified, and the household electrical load is divided into multiple load partitions that include at least basic load and adjustable load. A rigid power supply constraint is set for the aforementioned basic load, and an adjustable power supply constraint is set for the aforementioned adjustable load. The adjustable power supply constraint includes the allowable power reduction range and the allowable power supply delay time range. Based on the state of charge (SOC) and temperature parameters of the energy storage battery, the available discharge capacity and available charge capacity of the energy storage battery in the current control cycle are determined. The available discharge capacity and the available charge capacity are introduced into the energy flow model as constraint factors affecting the energy supply capacity of the adjustable load partition, so that the energy supply capacity of the adjustable load partition changes dynamically with the operating state of the energy storage battery. Under the premise of satisfying the rigid power supply constraints of the above-mentioned basic load, the energy flow relationship between photovoltaic power generation and the above-mentioned basic load, the above-mentioned adjustable load, the above-mentioned home energy storage system and the above-mentioned power grid is established, thereby forming the above-mentioned energy flow model that is dynamically coupled with the operating status of the above-mentioned energy storage battery.

[0008] In one feasible implementation, based on the aforementioned energy flow model, and in conjunction with grid electricity price information and energy storage battery safety constraints, the distribution ratio of household photovoltaic power generation among household loads, household energy storage systems, and the grid is optimized to generate a target energy allocation strategy, including: Based on the above power grid price information, a power grid price cost item is constructed that reflects the cost of purchasing electricity and the revenue from grid connection in different time periods; Based on the charging and discharging power, state of charge variation, and temperature parameters of the energy storage battery, the equivalent aging cost of the energy storage battery in the current control cycle is calculated, and the equivalent aging cost item of the energy storage battery is formed. The above-mentioned grid electricity price cost item and the above-mentioned energy storage battery equivalent aging cost item are combined to construct a joint optimization objective function; Under the conditions of satisfying the above energy flow model and the above energy storage battery safety constraints, the above joint optimization objective function is used as the optimization objective to solve the distribution ratio of the above household photovoltaic power generation among the above household load, the above household energy storage system and the above power grid, so as to obtain the solution results; Based on the above solution results, the target energy allocation strategy is generated for subsequent control execution.

[0009] In one feasible implementation, the above-mentioned grid electricity price cost item, which reflects the electricity purchase cost and grid connection revenue at different time periods, is constructed based on the aforementioned grid electricity price information, including: Obtain the grid time-of-use electricity price information corresponding to the current control cycle. The grid time-of-use electricity price information includes at least the peak electricity price, the normal electricity price, and the valley electricity price. Based on the above-mentioned time-of-use electricity price information of the power grid, the electricity purchase price parameters corresponding to the household's purchase of electricity from the above-mentioned power grid, and the grid connection revenue parameters corresponding to the household's connection to the above-mentioned power grid are determined respectively. Based on the above-mentioned electricity purchase price parameters, the above-mentioned grid connection revenue price parameters, and the above-mentioned power interaction direction between households and the power grid, a grid electricity price cost calculation rule corresponding to the above-mentioned control cycle is constructed. Based on the above-mentioned power grid price cost calculation rules, the power purchased and connected to the grid by the above-mentioned households during the above-mentioned control period are quantitatively evaluated, and the above-mentioned power grid price cost item is generated for joint optimization objective function calculation.

[0010] In one feasible implementation, based on the charging and discharging power, state of charge variation, and temperature parameters of the energy storage battery, the equivalent aging cost of the energy storage battery within the current control cycle is calculated, and an equivalent aging cost item for the energy storage battery is formed, including: Within the aforementioned current control cycle, the actual charge and discharge power change curves and the corresponding state of charge (SOC) change curves of the aforementioned energy storage battery are obtained. Based on the above charging and discharging power change curves and the above state of charge (SOC) change curves, the magnitude of the state of charge change within the above control cycle is calculated to characterize the depth of charge and discharge of the above energy storage battery. Obtain the temperature parameters of the above-mentioned energy storage battery within the above-mentioned control cycle, and determine the correction coefficient for the effect of temperature on the aging of the energy storage battery based on the above-mentioned temperature parameters. Based on the above charging and discharging power, the above state of charge change range, the above temperature parameters, and the above correction coefficient, the equivalent aging cost of the above energy storage battery during the above control period is calculated. The equivalent aging cost mentioned above is used as a cost term characterizing the degree of lifespan loss of the energy storage battery. It is introduced into the above joint optimization objective function to form the above equivalent aging cost term of the energy storage battery.

[0011] In one feasible implementation, the above-mentioned control of the charging and discharging state of the home energy storage system and the power interaction relationship between the home and the power grid, based on the aforementioned target energy distribution strategy, to achieve intelligent distribution of energy consumption on the home side, includes: Based on the above target energy distribution strategy, charging control commands or discharging control commands for the above home energy storage system are generated, and power interaction control commands for controlling the power purchased or fed into the grid between the home and the above grid are generated. The above-mentioned charging control command or the above-mentioned discharging control command is sent to the above-mentioned home energy storage system to adjust the actual charging and discharging power of the above-mentioned home energy storage system; The aforementioned power interaction control command is sent to the grid connection control device to control the power purchased or connected to the grid between the household and the grid, so that the power distribution on the household side meets the aforementioned target energy distribution strategy. During the execution of the above-mentioned charging and discharging control commands and the above-mentioned power interaction control commands, the operating status data of the above-mentioned home energy storage system and the actual power interaction data between the home and the above-mentioned power grid are continuously collected. When the above-mentioned operating status data or the above-mentioned actual power interaction data are detected to deviate from the expected range corresponding to the above-mentioned target energy allocation strategy, the above-mentioned target energy allocation strategy is corrected based on the above-mentioned deviation, and the above-mentioned charging control command, the above-mentioned discharging control command, or the above-mentioned power interaction control command is updated.

[0012] In one feasible implementation, prior to performing the optimized allocation calculation for household photovoltaic power generation, the method further includes: Based on the historical electricity consumption behavior of household electricity load, household energy demand is divided into multiple energy responsibility types, including at least immediate energy demand, deferred energy demand, and energy storage compensation energy demand. A corresponding energy responsibility quota is allocated to each of the above-mentioned energy consumption responsibility types. The energy responsibility quota is used to limit the proportion of photovoltaic energy, grid energy or energy storage energy that can be consumed by the energy consumption responsibility type within a preset control period. The aforementioned energy responsibility quota is introduced as a constraint into the energy flow model, so that the allocation ratio of the aforementioned household photovoltaic power generation is subject to the aforementioned energy responsibility quota.

[0013] In one feasible implementation, the above-mentioned calculation of the allocation ratio optimization for household photovoltaic power generation also includes: Based on the above energy responsibility quotas, calculate the actual energy consumption value of each of the above energy responsibility types in the current control cycle; The actual energy consumption value is compared with the corresponding energy responsibility quota to obtain the energy responsibility deviation. The aforementioned energy responsibility deviation is incorporated into the joint optimization objective function as a constraint or penalty term to suppress the deviation of the energy allocation result from the aforementioned energy responsibility quota. The aforementioned target energy allocation strategy is generated based on the joint optimization objective function that includes the aforementioned energy responsibility deviation.

[0014] In one feasible implementation, when controlling the charging and discharging state of the home energy storage system and the power interaction relationship between the home and the power grid according to the aforementioned target energy distribution strategy, the method further includes: Based on the above energy responsibility quota and the above target energy allocation strategy, determine the power execution priority corresponding to different energy consumption responsibility types in the current control cycle; When the aforementioned household photovoltaic power generation or the aforementioned household electricity load fluctuates, priority will be given to adjusting the interaction behavior of charging and discharging power or grid power corresponding to the lower priority energy consumption responsibility type. Provided that the aforementioned energy responsibility quotas are met, dynamic compensation is permitted for power allocation corresponding to high-priority energy consumption responsibility types.

[0015] Secondly, this invention also proposes a home-based integrated light and energy storage intelligent distribution method system, comprising: The acquisition unit is used to acquire real-time power generation data of the household photovoltaic power generation system, real-time power consumption data of the household electrical load, and operating status data of the household energy storage system. The operating status data includes the state of charge (SOC) and temperature parameters of the energy storage battery. The construction unit is used to construct an energy flow model of household photovoltaic-load-energy storage based on the above photovoltaic power generation data, the above electricity load data and the above operating status data, and to determine the feasible constraints of each energy flow direction in the above energy flow model. The calculation unit is used to optimize the distribution ratio of household photovoltaic power generation among household load, household energy storage system and grid based on the above energy flow model, combined with grid electricity price information and energy storage battery safety constraints, and generate target energy distribution strategy. The control unit is used to control the charging and discharging status of the aforementioned home energy storage system and the power interaction relationship between the home and the power grid according to the aforementioned target energy distribution strategy, so as to realize intelligent distribution of energy consumption on the home side.

[0016] In summary, this invention proposes an integrated intelligent energy allocation method for household photovoltaic (PV) and energy storage systems. By constructing a unified energy flow model of household PV-load-energy storage, and comprehensively incorporating PV power generation, household load power, energy storage battery operating status, grid electricity price information, and safety constraints, this method optimizes the allocation ratio of household PV power among household load, household energy storage system, and the grid. This achieves intelligent scheduling of household energy consumption at the system level. Compared to existing technologies, this invention no longer relies solely on simple rules or fixed priorities for power allocation. Instead, by clearly defining the energy flow direction and its feasible constraints, the allocation decision possesses clear physical meaning and computability, effectively improving the stability and interpretability of the scheduling strategy. Furthermore, this invention combines grid electricity price information and energy storage battery safety constraints during the optimization calculation process. This ensures that the allocation of PV energy not only considers the immediate energy needs of the household but also dynamically balances the cost of electricity purchase, grid connection revenue, and energy storage usage strategies during different electricity price periods, thereby increasing the self-consumption ratio of PV power generation and reducing the overall energy cost of the household. Meanwhile, by incorporating the state of charge and temperature parameters of the energy storage battery into the operational status data, blind charging and discharging when the battery condition is unfavorable is avoided, effectively reducing the safety risks and lifespan loss of the energy storage system and improving the long-term reliability of the home energy storage system. Furthermore, this invention directly controls the charging and discharging state of the home energy storage system and the power interaction between the home and the grid after the target energy allocation strategy is generated, enabling the optimization results to be accurately and in real-time executed to the physical layer devices, avoiding the problem of "strategy and execution disconnect" in traditional solutions. When the output of the home photovoltaic system or the load changes, this method can recalculate and update the allocation strategy based on the latest collected data, thereby achieving adaptive adjustment of energy consumption on the home side and ensuring that the system can maintain power balance and safe operation under dynamic conditions. In summary, this invention, by introducing energy flow modeling and multi-constraint optimization allocation mechanisms on the home side, achieves coordinated scheduling between photovoltaics, loads, energy storage, and the grid. Compared with the schemes in the background technology that only focus on a single target or instantaneous power control, it can better balance economy, safety, and operational stability, and has stronger engineering applicability and promotional value.

[0017] Other advantages, objectives and features of this application will be partly apparent from the description below, and partly understood by those skilled in the art through study and practice of this application. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1A flowchart illustrating a home-based integrated photovoltaic and energy storage intelligent distribution method provided in this application embodiment: Figure 2 This is a schematic diagram of the system structure of a home-based integrated photovoltaic and energy storage intelligent distribution method provided in an embodiment of this application. Detailed Implementation

[0019] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0020] Please see Figure 1 This is a flowchart illustrating a home-based integrated photovoltaic and energy storage intelligent allocation method provided in an embodiment of this application, which may specifically include: S110. Obtain real-time power generation data of the household photovoltaic power generation system, real-time power consumption data of the household electrical load, and operating status data of the household energy storage system. The operating status data includes the state of charge (SOC) and temperature parameters of the energy storage battery. S120. Based on the above photovoltaic power generation data, the above electricity load data and the above operating status data, construct a household photovoltaic-load-energy storage energy flow model, and determine the feasible constraints for each energy flow direction in the above energy flow model. S130. Based on the above energy flow model, combined with grid electricity price information and energy storage battery safety constraints, optimize the distribution ratio of household photovoltaic power generation among household load, household energy storage system and grid, and generate target energy distribution strategy. S140. Based on the above-mentioned target energy distribution strategy, control the charging and discharging state of the above-mentioned home energy storage system and the power interaction relationship between the home and the power grid to realize intelligent distribution of energy consumption on the home side.

[0021] For example, in step S110, the system first collects and synchronizes multi-source data from the home side, including real-time power generation data output from the photovoltaic inverter, real-time power consumption data of each circuit collected by the home distribution box or branch metering device, and operating status data reported by the energy storage system BMS / PCS. The operating status data includes at least the state of charge (SOC) and temperature parameters of the energy storage battery. The SOC is used to characterize the current available power and sufficiency of the energy storage, and the temperature parameter is used to characterize the battery's thermal safety boundary and the allowable charge / discharge rate range. Together, they determine the power at which the energy storage system can safely charge or discharge at the current moment. At the same time, the system can further collect auxiliary information such as grid connection point power, inverter power limiting status, power outage alarm, and charge / discharge enable to improve the completeness and robustness of subsequent decisions.

[0022] In step S120, the system constructs a household photovoltaic-load-energy storage energy flow model based on the photovoltaic power generation data, electricity load data, and operating status data obtained in step S110. This model is used to characterize the relationship between each energy flow direction and power balance. That is, within the same control cycle, the photovoltaic output power can be allocated to power the household load, charge the energy storage, or be connected to the grid for output. Energy storage can also discharge to compensate the household load when photovoltaic power is insufficient or electricity prices are unfavorable, or purchase electricity from the grid to charge the load when electricity prices are low. To ensure that the model is feasible in engineering, the system simultaneously determines the feasible constraints for each energy flow direction in the energy flow model, such as... The upper limit of photovoltaic power is determined by real-time irradiance and inverter capacity; the power demand of household loads is determined by metering data and must meet the requirements of power supply continuity; the charging and discharging power of energy storage is constrained by the upper and lower limits of SOC, temperature threshold, maximum charging and discharging current, PCS rated power, and grid connection point power limit, respectively; the grid interaction power is constrained by grid connection protocol, reverse power transmission limit, power ramp rate, and household main switch capacity; at the same time, the model can also introduce power change rate constraints to avoid relay impact or high-frequency charging and discharging of batteries caused by frequent switching, thereby forming an energy flow feasible region that meets the constraints of power conservation, safety boundary, and equipment capacity.

[0023] In step S130, the system further incorporates grid electricity price information and energy storage battery safety constraints within the feasible region of the energy flow model, performs optimized calculations of the allocation ratio, and generates a target energy allocation strategy. The grid electricity price information can be time-of-use pricing, real-time pricing, or bidirectional pricing parameters including grid connection settlement prices, used to quantify the "cost of purchasing electricity from the grid" and the "benefits of connecting to the grid." The energy storage battery safety constraints at least include a state of charge (SOC) safety boundary, a temperature safety boundary, and a temperature-related allowable charge / discharge rate range. The system constructs an optimization objective and constraint set based on this, and solves the allocation ratio of energy paths such as "PV → load", "PV → energy storage", "PV → grid", "grid → energy storage", and "energy storage → load" within a rolling control cycle. Typically, it can prioritize meeting basic electricity demand, minimize electricity purchases during peak electricity price periods and allow energy storage to discharge and smooth out peaks, moderately purchase electricity from the grid to supplement energy storage during off-peak electricity price periods, prioritize charging when PV is surplus and energy storage is not full to increase the self-consumption ratio, and then connect the remaining PV to the grid to obtain revenue when energy storage is close to the SOC limit or grid connection allows reverse power feeding. At the same time, to avoid the risk of battery failure in high temperature or high SOC range, the optimization calculation will automatically reduce the charging power or switch to grid output, so that the generated target energy allocation strategy takes into account economic efficiency, PV absorption efficiency and battery safety.

[0024] In step S140, the system executes closed-loop control based on the target energy allocation strategy generated in step S130. On one hand, it sends charging control commands or discharging control commands to the energy storage converter or energy storage system to adjust the actual charging and discharging power of the energy storage system, so that it can provide compensation power or absorb surplus photovoltaic power when needed according to the strategy. On the other hand, it sends power interaction control commands to the grid connection control device or inverter power control interface to adjust the power purchased by the household and the grid or the power fed into the grid, so that the power at the grid connection point meets the strategy planning and complies with the grid connection restrictions. During the execution process, the system continuously reads back the operating status data such as the SOC and temperature parameters of the energy storage battery and the actual power at the grid connection point. When it finds that the actual operation deviates from the strategy expectation or triggers the safety boundary (e.g., the temperature approaches the threshold, the SOC reaches the upper / lower limit, the grid connection restriction changes, or the load increases suddenly), the system can immediately correct the charging and discharging power and the power interaction with the grid. If necessary, it can reduce the derated operation or temporarily switch to the safety mode. Then, in the next control cycle, it re-updates the energy flow model based on the latest data and recalculates the allocation ratio, thus forming a closed-loop scheduling mechanism of "strategy-execution-feedback-re-optimization". Through the above process, this invention can achieve priority self-use and reasonable storage of photovoltaic energy in actual scenarios of fluctuating household load and photovoltaic output, reduce the purchase of electricity during high-price periods, improve the energy replenishment efficiency during low-price periods, and achieve safe operation and lifespan-friendly use of energy storage under the premise of constraining battery SOC and temperature boundaries throughout the process, ultimately realizing intelligent allocation of household energy consumption and overall economic improvement.

[0025] This invention proposes a smart energy allocation method integrating household photovoltaic (PV) power generation and energy storage. By constructing a unified energy flow model of household PV-load-energy storage, and comprehensively incorporating PV power generation, household load power, energy storage battery operating status, grid electricity price information, and safety constraints, the method optimizes the allocation ratio of household PV power generation among household load, household energy storage system, and the grid. This achieves intelligent scheduling of household energy consumption at the system level. Compared with existing technologies, this invention no longer relies solely on simple rules or fixed priorities for power allocation. Instead, by clearly defining the energy flow direction and its feasible constraints, the allocation decision has clear physical meaning and computability, effectively improving the stability and interpretability of the scheduling strategy. Furthermore, this invention combines grid electricity price information and energy storage battery safety constraints during the optimization calculation process. This ensures that the allocation of PV energy not only considers the immediate energy needs of the household but also dynamically balances the cost of electricity purchase, grid connection revenue, and energy storage usage strategies during different electricity price periods, thereby increasing the self-consumption ratio of PV power generation and reducing the overall energy cost of the household. Meanwhile, by incorporating the state of charge and temperature parameters of the energy storage battery into the operational status data, blind charging and discharging when the battery condition is unfavorable is avoided, effectively reducing the safety risks and lifespan loss of the energy storage system and improving the long-term reliability of the home energy storage system. Furthermore, this invention directly controls the charging and discharging state of the home energy storage system and the power interaction between the home and the grid after the target energy allocation strategy is generated, enabling the optimization results to be accurately and in real-time executed to the physical layer devices, avoiding the problem of "strategy and execution disconnect" in traditional solutions. When the output of the home photovoltaic system or the load changes, this method can recalculate and update the allocation strategy based on the latest collected data, thereby achieving adaptive adjustment of energy consumption on the home side and ensuring that the system can maintain power balance and safe operation under dynamic conditions. In summary, this invention, by introducing energy flow modeling and multi-constraint optimization allocation mechanisms on the home side, achieves coordinated scheduling between photovoltaics, loads, energy storage, and the grid. Compared with the schemes in the background technology that only focus on a single target or instantaneous power control, it can better balance economy, safety, and operational stability, and has stronger engineering applicability and promotional value.

[0026] In one feasible implementation, based on the photovoltaic power generation data, the electricity load data, and the operating status data, a household photovoltaic-load-energy storage energy flow model is constructed, and feasible constraints for each energy flow direction are determined in the energy flow model, including: Based on the real-time power consumption data of the household electrical load, the load type of the household electrical load is identified, and the household electrical load is divided into multiple load partitions that include at least basic load and adjustable load. A rigid power supply constraint is set for the aforementioned basic load, and an adjustable power supply constraint is set for the aforementioned adjustable load. The adjustable power supply constraint includes the allowable power reduction range and the allowable power supply delay time range. Based on the state of charge (SOC) and temperature parameters of the energy storage battery, the available discharge capacity and available charge capacity of the energy storage battery in the current control cycle are determined. The available discharge capacity and the available charge capacity are introduced into the energy flow model as constraint factors affecting the energy supply capacity of the adjustable load partition, so that the energy supply capacity of the adjustable load partition changes dynamically with the operating state of the energy storage battery. Under the premise of satisfying the rigid power supply constraints of the above-mentioned basic load, the energy flow relationship between photovoltaic power generation and the above-mentioned basic load, the above-mentioned adjustable load, the above-mentioned home energy storage system and the above-mentioned power grid is established, thereby forming the above-mentioned energy flow model that is dynamically coupled with the operating status of the above-mentioned energy storage battery.

[0027] For example, in the control cycle Within a timeframe (e.g., 1 minute or 5 minutes), the household electrical load is first divided into a basic load zone and an adjustable load zone based on the rigidity and adjustability of the power supply, and the basic load power requirement is defined as follows: Adjustable load power requirement is The actual power supply to the adjustable load is .in, This indicates the load power that must be guaranteed (e.g., refrigerators, lighting, network equipment, etc.). This indicates the expected power demand of an adjustable load without reduction or delay (e.g., air conditioners, electric water heaters, washing machines, etc.), while This refers to the power actually allocated to the adjustable load after scheduling. To characterize the range of permissible power reduction and the range of permissible power delay, the feasible power supply interval for the adjustable load can be represented as: in Usually not exceeding ,and It can be 0 or a certain comfort level lower limit power; if the adjustable load is a task-oriented load (such as a washing machine or dishwasher), its delay feasibility can also be described by task energy constraints, for example, within the allowable delay window. Domestic demand fulfills energy requirements : in For task-oriented workloads in cycles Power supply capacity The duration of a single control cycle (in hours).

[0028] At the same time, defining photovoltaics in a cycle The available output power is The energy storage battery charging power is The energy storage battery discharge power is The power purchased by the power grid is The grid-connected power is To facilitate uniform constraints, all the above power values ​​can be defined as non-negative, and directional variables can be used to distinguish the direction of energy flow. The power balance relationship on the household side can be written as: The left side represents the available power supply for households (photovoltaic output, energy storage discharge, and grid purchase), while the right side represents the consumption destinations for households (base load, adjustable load, energy storage charging, and grid connection).

[0029] Based on the aforementioned power balance, it is necessary to incorporate the operating state (SOC and temperature) of the energy storage battery into feasible constraints. Let the state of charge of the energy storage battery be... Its update can be approximated using coulomb or energy measurement as follows: in Rated energy capacity of the battery (kWh). and Charging efficiency and discharging efficiency, respectively To control cycle duration (in hours). To ensure safe operation, the SOC must meet boundary constraints: in and These are the minimum and maximum allowed state of charge thresholds, respectively.

[0030] Temperature parameters can be expressed as This directly affects the upper limit of permissible charge and discharge power. Charge and discharge capacity constraints can be constructed using a temperature derating function. in and This represents the maximum permissible charging power and maximum permissible discharging power as a function of State of Charge (SOC) and temperature. In engineering practice, this function can be defined using piecewise linear methods or lookup tables, for example, when the temperature is within a safe range. The rated power is allowed at the specified temperature, but is derated proportionally when the temperature approaches the limit: in The rated charge and discharge power of the energy storage converter, Temperature derating factor SOC deduction factor It is used to prevent high-power charging at high SOC or high-power discharging at low SOC.

[0031] To reflect that available discharge and charge capabilities are constraints affecting the power supply capacity of adjustable loads, a coupling relationship can be established between the power supply capacity and energy storage capacity of adjustable loads. For example, the adjustable load can be specified in terms of cycle time. The upper limit of available power supply is jointly determined by the surplus of photovoltaic power, the discharge capacity of energy storage, and the grid's allowable power purchase, while its potential for reduction is constrained by the state of energy storage. A feasible coupled expression is: in and This represents the amount of power correction required to reduce the adjustable load due to the SOC or temperature entering an unfavorable range. The lower the SOC or the higher the temperature, the larger the correction. This allows the adjustable load to automatically reduce its allowable power supply range when the battery is not suitable for discharging or frequent charging and discharging. Alternatively, it can be used to define the proportional coupling between the adjustable load's energy supply capacity and the energy storage's available charging and discharging capacity, for example: in It is a monotonic function used to map the available discharge capacity of energy storage to the proportion of adjustable load that can be enabled, so that the more sufficient the energy storage capacity, the higher the proportion of adjustable load that can be satisfied.

[0032] Furthermore, grid interaction must also satisfy grid connection constraints, which can be written as: in The maximum allowable power purchase for a household (subject to the capacity of the household or the contracted capacity). To allow the maximum grid-connected power (limited by inverter or reverse power feeding policy); if there is a requirement to prohibit reverse power feeding, then it can be used. or Therefore, the set of feasible constraints for the energy flow model is essentially composed of power balance constraints, load zoning constraints, energy storage SOC / temperature constraints, and grid interaction constraints.

[0033] Based on the above mathematical description, the system can then allocate variables in the next optimization calculation with objectives such as minimizing electricity purchase cost, maximizing photovoltaic self-consumption, or taking into account energy storage lifespan. The solution process is performed by introducing load partitioning and SOC / temperature derating coupling in the energy flow model stage. This ensures that the feasible region for optimization naturally satisfies the battery safety boundary and reflects the rigidity and flexibility differences of household loads. This avoids problems such as unstable power supply to the basic load, discontinuous adjustment of adjustable loads, and risks of battery overheating or over-charging / over-discharging, which can occur when scheduling is based solely on electricity price or power thresholds. Through the above continuous modeling and constraint construction, this embodiment forms a household photovoltaic-load-energy storage energy flow model dynamically coupled with the operating state of the energy storage battery. This provides a clear and executable mathematical foundation for generating target energy allocation strategies under electricity price and safety constraints.

[0034] In one feasible implementation, based on the aforementioned energy flow model, and in conjunction with grid electricity price information and energy storage battery safety constraints, the distribution ratio of household photovoltaic power generation among household loads, household energy storage systems, and the grid is optimized to generate a target energy allocation strategy, including: Based on the above power grid price information, a power grid price cost item is constructed that reflects the cost of purchasing electricity and the revenue from grid connection in different time periods; Based on the charging and discharging power, state of charge variation, and temperature parameters of the energy storage battery, the equivalent aging cost of the energy storage battery in the current control cycle is calculated, and the equivalent aging cost item of the energy storage battery is formed. The above-mentioned grid electricity price cost item and the above-mentioned energy storage battery equivalent aging cost item are combined to construct a joint optimization objective function; Under the conditions of satisfying the above energy flow model and the above energy storage battery safety constraints, the above joint optimization objective function is used as the optimization objective to solve the distribution ratio of the above household photovoltaic power generation among the above household load, the above household energy storage system and the above power grid, so as to obtain the solution results; Based on the above solution results, the target energy allocation strategy is generated for subsequent control execution.

[0035] In one feasible implementation, the above-mentioned grid electricity price cost item, which reflects the electricity purchase cost and grid connection revenue at different time periods, is constructed based on the aforementioned grid electricity price information, including: Obtain the grid time-of-use electricity price information corresponding to the current control cycle. The grid time-of-use electricity price information includes at least the peak electricity price, the normal electricity price, and the valley electricity price. Based on the above-mentioned time-of-use electricity price information of the power grid, the electricity purchase price parameters corresponding to the household's purchase of electricity from the above-mentioned power grid, and the grid connection revenue parameters corresponding to the household's connection to the above-mentioned power grid are determined respectively. Based on the above-mentioned electricity purchase price parameters, the above-mentioned grid connection revenue price parameters, and the above-mentioned power interaction direction between households and the power grid, a grid electricity price cost calculation rule corresponding to the above-mentioned control cycle is constructed. Based on the above-mentioned power grid price cost calculation rules, the power purchased and connected to the grid by the above-mentioned households during the above-mentioned control period are quantitatively evaluated, and the above-mentioned power grid price cost item is generated for joint optimization objective function calculation.

[0036] In one feasible implementation, based on the charging and discharging power, state of charge variation, and temperature parameters of the energy storage battery, the equivalent aging cost of the energy storage battery within the current control cycle is calculated, and an equivalent aging cost item for the energy storage battery is formed, including: Within the aforementioned current control cycle, the actual charge and discharge power change curves and the corresponding state of charge (SOC) change curves of the aforementioned energy storage battery are obtained. Based on the above charging and discharging power change curves and the above state of charge (SOC) change curves, the magnitude of the state of charge change within the above control cycle is calculated to characterize the depth of charge and discharge of the above energy storage battery. Obtain the temperature parameters of the above-mentioned energy storage battery within the above-mentioned control cycle, and determine the correction coefficient for the effect of temperature on the aging of the energy storage battery based on the above-mentioned temperature parameters. Based on the above charging and discharging power, the above state of charge change range, the above temperature parameters, and the above correction coefficient, the equivalent aging cost of the above energy storage battery during the above control period is calculated. The equivalent aging cost mentioned above is used as a cost term characterizing the degree of lifespan loss of the energy storage battery. It is introduced into the above joint optimization objective function to form the above equivalent aging cost term of the energy storage battery.

[0037] For example, in constructing the electricity price cost item, the aforementioned grid electricity price information is usually a time-of-use price or a real-time price. In this embodiment, a time-of-use price is used as an example, defined in the period... The unit price of electricity is The unit price for internet access revenue is Both are derived from peak-hour electricity prices, normal-hour electricity prices, off-peak electricity prices, and grid connection settlement rules. Grid electricity price costs can be quantified based on net costs within the control period, for example: in Indicates period The electricity price cost within the grid (positive indicates expenditure, negative indicates net income). This indicates the electricity purchase price per unit (yuan / kWh) for this period. This represents the unit price parameter of the grid connection revenue for this period (yuan / kWh). and These represent the purchased power and the grid-connected power (kW), respectively. The period duration is in hours (h). This construction method allows the system to directly unify electricity purchase costs and grid connection revenue into a single cost item, which is then dynamically updated according to electricity price changes over different time periods, thus reflecting the economic differences between purchasing and connecting to the grid at different times.

[0038] Regarding the construction of the equivalent aging cost term for energy storage batteries, the aforementioned equivalent aging cost term is used to quantify the degree of lifespan loss caused by charging and discharging behavior of energy storage batteries. It can be considered as a cost factor of equal importance to electricity price costs in optimization. To this end, the system acquires the actual charging and discharging power variation curve and the state of charge (SOC) variation curve of the energy storage battery within the current control cycle, and calculates the magnitude of the SOC change. Used to characterize the depth of charge and discharge, for example: in Indicates period The electricity price cost within the grid (positive indicates expenditure, negative indicates net income). This indicates the electricity purchase price per unit (yuan / kWh) for this period. This represents the unit price parameter of the grid connection revenue for this period (yuan / kWh). and These represent the purchased power and the grid-connected power (kW), respectively. The period duration is in hours (h). This construction method allows the system to directly unify electricity purchase costs and grid connection revenue into a single cost item, which is then dynamically updated according to electricity price changes over different time periods, thus reflecting the economic differences between purchasing and connecting to the grid at different times.

[0039] Regarding the construction of the equivalent aging cost term for energy storage batteries, the aforementioned equivalent aging cost term is used to quantify the degree of lifespan loss caused by charging and discharging behavior of energy storage batteries. It can be considered as a cost factor of equal importance to electricity price costs in optimization. To this end, the system acquires the actual charging and discharging power variation curve and the state of charge (SOC) variation curve of the energy storage battery within the current control cycle, and calculates the magnitude of the SOC change. Used to characterize the depth of charge and discharge, for example: Meanwhile, based on temperature parameters Determine the temperature correction factor This is used to characterize the effect of temperature on the aging rate. In engineering, piecewise functions or exponential functions can be used, for example: in and These represent the lower and upper limits of the battery's safe or recommended operating temperature range, respectively. and This represents the temperature deviation penalty factor. Furthermore, the equivalent aging cost can be expressed as a combined function of charge / discharge power intensity, depth of charge / discharge, and temperature correction, for example... in Indicates period Equivalent aging cost within, The power intensity aging weighting coefficient, This is the weighting coefficient for deep aging. This represents the temperature correction factor. The above form means that within the same cycle, the higher the charge / discharge power, the greater the SOC variation, or the further the temperature deviates from the suitable range, the higher the battery life loss cost. This thus suppresses frequent high-power charge / discharge cycles in optimization calculations. For more refined modeling, the equivalent aging cost can also be defined using the equivalent full cycle count (EFC) as an intermediate quantity, for example: in Indicates period The equivalent number of iterations calculated internally. This is the cyclic aging factor, used to map the number of cycles to the cost of lifespan loss.

[0040] After constructing the grid electricity price cost item and the equivalent aging cost item of the energy storage battery, the system combines the two to form a joint optimization objective function. For example, a weighted summation form can be used: in To control the cycle The joint optimization objective function value, and These are the weighting parameters for electricity cost and aging cost, respectively, used to characterize the trade-off between household economic goals and battery life protection goals; when a greater emphasis is placed on reducing electricity costs, these parameters can be increased. When a greater emphasis is placed on extending battery life or suppressing charge and discharge in high-temperature seasons, it can be improved. Under the conditions of satisfying power balance constraints, energy storage safety constraints, and grid connection constraints, the above optimization calculation can be expressed as: s. t. Power balance constraints, SOC update constraints, SOC boundary constraints, temperature / power boundary constraints, and grid-connected power constraints, among which For period The set of decision variables (adjustable load power variables from the load partition can also be added if necessary). Solving this optimization problem yields the solution for the allocation ratio, which can be expressed as the proportion of photovoltaic power in the two destinations: in Representing the period The power components of the internal photovoltaic power are distributed to the load, energy storage charging, and grid connection, and satisfy the following requirements. (exist (In the case of...). Finally, based on the solution results, the system generates a target energy allocation strategy for subsequent control execution. This target energy allocation strategy can be specifically transformed into an interaction setpoint between the energy storage charging / discharging power setpoint and the grid power setpoint, i.e.... and This is converted into charging or discharging control commands for the energy storage system. and The power interaction control command is converted into the grid-connected power purchase capacity or grid-connected power capacity. In the next control cycle, the data is reacquired, the cost items are reconstructed, and the strategy is updated on a rolling basis. This achieves unified scheduling of electricity price-driven economic optimization and lifespan-friendly battery protection under the same joint optimization objective function. Through the above continuous cost item construction and joint optimization solution, this embodiment can dynamically generate energy allocation strategies under different electricity price periods, different photovoltaic outputs, and different battery states. This enables households to achieve lower electricity purchase costs, higher photovoltaic self-consumption rates, and reduce battery life loss caused by high temperatures or deep cycling under safety constraints throughout the process.

[0041] In one feasible implementation, the above-mentioned control of the charging and discharging state of the home energy storage system and the power interaction relationship between the home and the power grid, based on the aforementioned target energy distribution strategy, to achieve intelligent distribution of energy consumption on the home side, includes: Based on the above target energy distribution strategy, charging control commands or discharging control commands for the above home energy storage system are generated, and power interaction control commands for controlling the power purchased or fed into the grid between the home and the above grid are generated. The above-mentioned charging control command or the above-mentioned discharging control command is sent to the above-mentioned home energy storage system to adjust the actual charging and discharging power of the above-mentioned home energy storage system; The aforementioned power interaction control command is sent to the grid connection control device to control the power purchased or connected to the grid between the household and the grid, so that the power distribution on the household side meets the aforementioned target energy distribution strategy. During the execution of the above-mentioned charging and discharging control commands and the above-mentioned power interaction control commands, the operating status data of the above-mentioned home energy storage system and the actual power interaction data between the home and the above-mentioned power grid are continuously collected. When the above-mentioned operating status data or the above-mentioned actual power interaction data are detected to deviate from the expected range corresponding to the above-mentioned target energy allocation strategy, the above-mentioned target energy allocation strategy is corrected based on the above-mentioned deviation, and the above-mentioned charging control command, the above-mentioned discharging control command, or the above-mentioned power interaction control command is updated.

[0042] For example, after generating the target energy allocation strategy, the system first performs instruction parsing on the strategy, extracting the target values ​​for energy storage charging and discharging power settings and grid power interaction settings, and generating charging control commands or discharging control commands for the home energy storage system. Simultaneously, it generates power interaction control commands to control the power purchased or fed into the grid between the home and the grid. The charging control commands typically include at least the target charging power setting, power ramp-up rate limit, allowable SOC range, and necessary charging enable flags. The discharging control commands typically include at least the target discharging power setting, power ramp-up rate limit, minimum SOC protection threshold, and discharging enable flags. The power interaction control commands typically include at least the target active power setting at the grid connection point, power purchase / grid connection direction identifier, and reverse power transmission restriction or power limiting strategy parameters, enabling subsequent controlled objects to receive and execute strategy requirements in a unified format.

[0043] The system sends the charging control command or the discharging control command to the energy storage inverter or battery management system of the home energy storage system to adjust the actual charging and discharging power of the home energy storage system, so that the energy storage can absorb surplus photovoltaic power during the time period required by the strategy or provide compensating power to the home load during the time period required by the strategy. For example, when the target energy allocation strategy requires increasing photovoltaic self-consumption and reserving power for nighttime electricity use, the system will issue a charging control command and set the target charging power to a positive value, so that the energy storage charges at a controlled power. When the target energy allocation strategy requires peak shaving or reducing electricity purchase costs, the system will issue a discharging control command and set the target discharging power to a positive value, so that the energy storage can discharge to the load side while meeting the minimum SOC protection. Simultaneously, the system sends the power interaction control command to the grid-connected control device, such as the grid-connected inverter control unit or the home energy gateway, to control the power purchased or fed into the grid between the home and the grid, ensuring that the grid-connected power meets the strategy planning and complies with the grid connection protocol requirements. For example, when the strategy determines that purchasing electricity during off-peak hours is more economical and the energy storage has charging margin, the system sets the power interaction control command to the power purchase direction and limits the upper limit of the power purchase. When the strategy determines that photovoltaic power is surplus and the energy storage is close to full charge or not suitable for continued charging, the system sets the power interaction control command to the grid connection direction and outputs the grid-connected power within the allowed reverse power supply range. If there is a restriction prohibiting reverse power supply, the power interaction control command sets the grid-connected power to zero and constrains the grid-connected power within the allowed range through power limiting or absorption strategies.

[0044] During the execution of the aforementioned control commands, to avoid power distribution distortion caused by discrepancies between the calculated strategy values ​​and the actual execution values ​​of the equipment, the system continuously collects operational status data of the home energy storage system and actual power interaction data between the home and the power grid to form a closed-loop feedback. The operational status data includes at least the State of Charge (SOC) of the energy storage battery, temperature parameters, actual charging and discharging power, charging and discharging enable status, and alarm status. The actual power interaction data includes at least the actual purchased power at the grid connection point, the actual power fed into the grid, and the direction status. Based on this real-time feedback, the system can compare the target strategy value with the actual execution value in each control cycle to determine whether a deviation has occurred and its source. For example, the actual charging power may be lower than the target value due to temperature protection derating of the energy storage system, or a sudden increase in home load may cause the power at the grid connection point to exceed the strategy setting value, or changes in grid connection restrictions may prevent the power fed into the grid from reaching the target value.

[0045] When the system detects that the operating status data or the actual power interaction data deviates from the expected range corresponding to the target energy allocation strategy, the system will correct the target energy allocation strategy based on the deviation and simultaneously update the charging control command, discharging control command or power interaction control command to achieve rapid adaptive correction.

[0046] In one feasible implementation, prior to performing the optimized allocation calculation for household photovoltaic power generation, the method further includes: Based on the historical electricity consumption behavior of household electricity load, household energy demand is divided into multiple energy responsibility types, including at least immediate energy demand, deferred energy demand, and energy storage compensation energy demand. A corresponding energy responsibility quota is allocated to each of the above-mentioned energy consumption responsibility types. The energy responsibility quota is used to limit the proportion of photovoltaic energy, grid energy or energy storage energy that can be consumed by the energy consumption responsibility type within a preset control period. The aforementioned energy responsibility quota is introduced as a constraint into the energy flow model, so that the allocation ratio of the aforementioned household photovoltaic power generation is subject to the aforementioned energy responsibility quota.

[0047] For example, firstly, household energy demand is categorized based on historical electricity consumption behavior. This historical electricity consumption behavior can be categorized by smart meters, branch metering, smart sockets, or home energy gateways. Statistics are collected within the last 7 or 30 days (e.g., the start and stop times, durations, daily recurrence patterns, and energy demands of each type of load). Based on these characteristics, the system categorizes household energy demand into multiple energy responsibility types, including at least immediate energy demand, deferred energy demand, and energy storage compensation energy demand, and these are denoted as responsibility type sets. Where imm represents immediate energy demand responsibility, def represents delayed energy demand responsibility, and bat represents energy storage compensation energy demand responsibility.

[0048] After completing the responsibility type classification, the system allocates a corresponding energy responsibility quota to each of the above-mentioned energy consumption responsibility types. To facilitate standardized measurement, this implementation introduces a preset control period (also known as a quota window) H (e.g., 1 hour, 4 hours, or 24 hours), and discretizes it into control step sizes. Multiple sub-cycles ,in For each type of liability Define its total energy responsibility quota within the quota window as (Unit: kWh), and further define the upper limit or target proportion of consumption from different energy sources within the quota window for this type of responsibility, respectively. Where pv represents photovoltaic energy source, grid represents grid energy source, and es represents energy storage energy source, and meets the following requirements: This allows us to derive energy responsibility quotas allocated by source: in Indicates the type of responsibility The amount of photovoltaic energy allowed or expected to be consumed within the quota window. This indicates the permitted or expected amount of grid energy consumption. This indicates the permitted or expected energy storage capacity. The engineering implications of these parameters are that within the same quota window, different responsibility types can have different energy source preferences and constraint strengths; for example, a higher limit can be set for immediate energy demand. To ensure uninterrupted availability, higher settings can be applied to demand that can be delayed. To promote the consumption of surplus photovoltaic power during peak periods, higher energy storage compensation requirements can be set. To clarify the peak shaving compensation responsibility of energy storage, but also to... The upper limit limits excessive looping.

[0049] In order to incorporate the aforementioned energy responsibility quota as a constraint into the energy flow model, the system in each control sub-cycle Define the responsibility type—the power allocation variable of the energy source, denoted as , respectively representing the sub-period Internally, photovoltaic, grid, and energy storage are allocated to responsibility types. The power component (unit: kW). Based on the integral relationship between power and energy, the cumulative energy consumption within the quota window can be obtained: in These represent the types of liability. The cumulative energy consumed (in kWh) from photovoltaic, grid, and energy storage sources within the quota window. The duration of a single sub-cycle (in hours). The core constraint of incorporating quotas into the energy flow model can be written as: This limits the cumulative consumption of each responsibility type from different energy sources to no more than its energy responsibility quota at the quota window level, thus making the allocation ratio of household photovoltaic power generation subject to the responsibility budget when solving the problem.

[0050] Simultaneously, to ensure consistency with the overall energy flow model after responsibility decomposition, the system sets aggregation consistency constraints to link the power components assigned to each responsibility type with the total system power variable. For example, it can be set that... Indicates sub-period The available output power of photovoltaics, Indicates sub-period The power purchased by the power grid Indicates sub-period The energy storage and discharge power can then be: And define the actual power supply for the responsibility type as This makes the total household load power satisfy Through the above aggregation constraints, the introduction of responsibility type quotas will not violate power conservation and system executability, but will instead embed the "responsibility budget" into the energy flow model in a computable manner.

[0051] In actual operation, the above-mentioned energy responsibility quota plays a role as follows: when a certain responsibility type is within the current quota window... It is close In subsequent allocation ratio optimization calculations, the system will reduce the priority of this responsibility type continuing to consume photovoltaic energy, thereby prompting photovoltaic power stations to switch to other responsibility types, to energy storage charging, or to grid connection; when a certain responsibility type It is close When this occurs, the system will inhibit the purchase and supply of electricity for that type of responsibility during periods of higher electricity prices, instead guiding it to be postponed or compensated by energy storage within permissible limits. When a certain type of responsibility... It is close At this time, the system will reduce the use of energy storage discharge to avoid excessive cycling of the battery within the quota window, thereby achieving a balance between lifespan friendliness and responsibility at the strategy level.

[0052] In one feasible implementation, the above-mentioned calculation of the allocation ratio optimization for household photovoltaic power generation also includes: Based on the above energy responsibility quotas, calculate the actual energy consumption value of each of the above energy responsibility types in the current control cycle; The actual energy consumption value is compared with the corresponding energy responsibility quota to obtain the energy responsibility deviation. The aforementioned energy responsibility deviation is incorporated into the joint optimization objective function as a constraint or penalty term to suppress the deviation of the energy allocation result from the aforementioned energy responsibility quota. The aforementioned target energy allocation strategy is generated based on the joint optimization objective function that includes the aforementioned energy responsibility deviation.

[0053] For example, in order to optimize the allocation ratio of household photovoltaic power generation, not only to achieve the best electricity price and energy storage aging costs, but also to continuously meet the energy responsibility quotas of each type of energy use during operation, the above method introduces an online calculation and suppression mechanism for energy responsibility deviation in the optimization solution stage. That is, in each current control cycle, the actual energy consumption of each type of energy use is first calculated, and then compared with the corresponding energy responsibility quota to obtain the degree of deviation. This degree of deviation is then injected into the joint optimization objective function as a constraint or penalty term, so that the optimized target energy allocation strategy has stability and interpretability of "responsibility fulfillment" on the basis of economy and safety.

[0054] Within the current control cycle, based on candidate solutions to the target energy allocation strategy or the execution results of the previous cycle, the system statistically analyzes the actual energy supplied from photovoltaic, grid, and energy storage sources for each energy responsibility type during this cycle, including immediate energy demand, deferred energy demand, and energy storage compensation demand. This data forms the actual energy consumption value for each responsibility type. Based on this, the system compares the actual energy consumption value with the corresponding energy responsibility quota to determine whether each responsibility type has exceeded its quota or failed to meet its obligations within the preset control cycle. This difference is then categorized as the energy responsibility deviation, used to characterize the degree of deviation of the current scheduling result from the predetermined responsibility budget.

[0055] The system incorporates the aforementioned energy responsibility deviation into the joint optimization objective function: When using a constraint term, the system forces the scheduling results to meet the quota boundary by limiting the deviation to no more than a preset tolerance, thereby preventing a certain responsibility type from long-term crowding out of photovoltaic energy or excessively occupying energy storage energy; when using a penalty term, the system imposes a cost on the deviation in the joint optimization objective function, making the optimization cost higher for larger deviations, thus automatically favoring energy allocation schemes closer to the quota budget during the solution process, and still allowing limited strategy offsets within the safety boundary under extreme conditions to ensure basic power supply. Based on the aforementioned joint optimization objective function including energy responsibility deviation, the system recalculates the distribution ratio of photovoltaic power generation among household loads, household energy storage systems, and the grid, under the premise of satisfying the energy flow model, grid connection constraints, and energy storage battery safety constraints, thereby generating a target energy allocation strategy that takes into account electricity price economy, energy storage lifetime friendliness, and responsibility quota compliance, and uses this strategy for subsequent control execution and rolling updates in the next control cycle.

[0056] Through the above process, this embodiment can suppress the cumulative deviation of energy allocation from the energy responsibility quota during long-term operation, and avoid problems such as the long-term suppression of deferred energy use, the forced purchase of large amounts of electricity during high-price periods for immediate energy use, or the excessive use of energy storage compensation responsibility leading to frequent deep cycling of batteries. This makes the integrated scheduling of home photovoltaic and energy storage more stable, interpretable, and more in line with users' energy preferences and budget constraints.

[0057] In one feasible implementation, when controlling the charging and discharging state of the home energy storage system and the power interaction relationship between the home and the power grid according to the aforementioned target energy distribution strategy, the method further includes: Based on the above energy responsibility quota and the above target energy allocation strategy, determine the power execution priority corresponding to different energy consumption responsibility types in the current control cycle; When the aforementioned household photovoltaic power generation or the aforementioned household electricity load fluctuates, priority will be given to adjusting the interaction behavior of charging and discharging power or grid power corresponding to the lower priority energy consumption responsibility type. Provided that the aforementioned energy responsibility quotas are met, dynamic compensation is permitted for power allocation corresponding to high-priority energy consumption responsibility types.

[0058] For example, firstly, based on energy responsibility quotas and target energy allocation strategies, the corresponding power execution priority within the current control cycle is determined for different energy consumption responsibility types. This maps responsibility types such as immediate energy demand, deferred energy demand, and energy storage compensation demand to different execution levels. The priority determination considers not only the rigidity of the responsibility type itself but also its quota fulfillment status within the preset control cycle. For instance, when immediate energy demand is close to its minimum guarantee threshold or its quota has not yet been met, it is set to high priority to ensure power supply continuity. When deferred energy demand is still within the deferred window and its quota fulfillment is sufficient, it is set to medium to low priority so that it can be preferentially reduced or postponed when disturbances occur. When energy storage compensation demand is not necessary in the current period and the energy storage quota is close to the upper limit or the battery temperature is high, it is set to low priority to suppress unnecessary charging and discharging calls.

[0059] After the priorities are determined, when household photovoltaic (PV) power generation or household electricity load fluctuates, the system does not directly overturn the overall target energy allocation strategy. Instead, it prioritizes adjusting the charging and discharging power or grid power interaction behavior corresponding to lower-priority energy consumption responsibilities. This allows for rapid restoration of power balance and grid connection constraint satisfaction while minimizing the impact on higher-priority responsibilities. For example, when PV output suddenly drops, leading to insufficient supply, the system can first reduce or suspend the supply power for lower-priority, deferable energy demands, or postpone their execution to subsequent periods of PV surplus / low electricity prices. If this is still insufficient, unnecessary discharge plans in lower-priority energy storage compensation activities can be reduced, or grid power purchases can be adjusted to make up the difference. When PV output suddenly increases or load drops sharply, leading to an increase in surplus power, the system can prioritize using the surplus power for the early execution of lower-priority tasks or for energy storage charging within the quota allowable range, thereby reducing losses caused by curtailment or restricted grid connection. When grid connection restrictions change (e.g., prohibition of reverse power transmission), the system also prioritizes absorbing surplus power by reducing lower-priority energy consumption or adjusting energy storage charging power to ensure that the grid connection point power meets the restrictions. By employing this "first-hand low-priority" execution strategy, the system can minimize the impact of disturbances and prevent high-priority energy usage from being frequently interrupted or experiencing a decline in user experience.

[0060] Furthermore, provided that the energy responsibility quota is met, the system allows for dynamic compensation of power allocation corresponding to high-priority energy consumption responsibility types to achieve reliable protection of critical energy consumption responsibilities and closed-loop fulfillment of the responsibility budget. Dynamic compensation refers to the system's compensation of high-priority responsibilities in subsequent control cycles when the system temporarily reduces energy supply for certain responsibility types to cope with disturbances, or when equipment limitations prevent certain responsibility types from obtaining their due energy share in the first few cycles. This compensation is based on quota margins and feasible constraints. For example, it increases the energy supply guarantee level for immediate energy demand during periods of low electricity prices or photovoltaic surplus, or appropriately increases energy storage discharge to compensate for immediate energy demand during peak hours when the energy storage SOC and temperature are within safe ranges and the energy storage quota still has a margin, ensuring that the cumulative energy consumption of critical responsibility types reaches their quota targets and is not subject to long-term undersupply. At the same time, the compensation process is also subject to energy responsibility quotas: when the quota for a high-priority responsibility is met or close to the limit, the system will stop compensating it and redirect resources to other responsibility types that have not yet met their quotas or to the grid connection revenue path, so that the compensation behavior serves the continuity of critical energy use without undermining the fairness of the long-term budget.

[0061] Therefore, this embodiment can quickly restore power balance and meet safety boundaries when photovoltaic fluctuations, load fluctuations and grid connection restrictions change. On the other hand, it can maintain the consistency of energy responsibility quota performance within a preset control cycle, avoiding long-term dispatch bias towards a certain type of energy responsibility or excessive consumption of a certain type of energy source, thereby achieving more stable, more explainable and more user-friendly intelligent allocation of household energy consumption.

[0062] Secondly, such as Figure 2 As shown, this invention also proposes a home-based integrated intelligent distribution method system for light and energy storage, comprising: The acquisition unit 21 is used to acquire real-time power generation data of the household photovoltaic power generation system, real-time power consumption data of the household electrical load, and operating status data of the household energy storage system. The operating status data includes the state of charge (SOC) and temperature parameters of the energy storage battery. The construction unit 22 is used to construct an energy flow model of household photovoltaic-load-energy storage based on the photovoltaic power generation data, the electricity load data and the operating status data, and to determine the feasible constraints of each energy flow direction in the energy flow model. The calculation unit 23 is used to optimize the distribution ratio of household photovoltaic power generation among household load, household energy storage system and grid based on the above energy flow model, combined with grid electricity price information and energy storage battery safety constraints, and generate target energy distribution strategy. The control unit 24 is used to control the charging and discharging state of the above-mentioned home energy storage system and the power interaction relationship between the home and the power grid according to the above-mentioned target energy distribution strategy, so as to realize the intelligent distribution of energy consumption on the home side.

[0063] In one feasible implementation, the home photovoltaic and energy storage integrated intelligent allocation method system proposed in this invention can also execute the home photovoltaic and energy storage integrated intelligent allocation method described in any of the first aspects.

[0064] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A home light storage integrated intelligent distribution method, characterized in that, include: The system acquires real-time power generation data of the household photovoltaic power generation system, real-time power consumption data of the household electrical load, and operating status data of the household energy storage system. The operating status data includes the state of charge (SOC) and temperature parameters of the energy storage battery. Based on the photovoltaic power generation data, the electricity load data, and the operating status data, an energy flow model of household photovoltaic-load-energy storage is constructed, and feasible constraints for each energy flow direction are determined in the energy flow model. Based on the energy flow model, combined with grid electricity price information and energy storage battery safety constraints, the distribution ratio of household photovoltaic power generation among household load, household energy storage system and grid is optimized and calculated to generate target energy distribution strategy. Based on the target energy distribution strategy, the charging and discharging state of the home energy storage system and the power interaction relationship between the home and the power grid are controlled to achieve intelligent distribution of energy consumption on the home side. 2.The home integrated photovoltaic and energy storage smart distribution method according to claim 1, wherein, The energy flow model of household photovoltaic-load-energy storage is constructed based on the photovoltaic power generation data, the electricity load data, and the operating status data. Feasible constraints for each energy flow direction are determined in the energy flow model, including: Based on the real-time power consumption data of the household electrical load, the load type of the household electrical load is identified, and the household electrical load is divided into multiple load partitions, including at least a basic load and an adjustable load. A rigid power supply constraint is set for the basic load, and an adjustable power supply constraint is set for the adjustable load. The adjustable power supply constraint includes an allowable power reduction range and an allowable power supply delay time range. Based on the state of charge (SOC) and temperature parameters of the energy storage battery, determine the available discharge capacity and available charge capacity of the energy storage battery in the current control cycle. The available discharge capacity and the available charge capacity are introduced into the energy flow model as constraint factors affecting the energy supply capacity of the adjustable load partition, so that the energy supply capacity of the adjustable load partition changes dynamically with the operating state of the energy storage battery. Under the premise of satisfying the rigid power supply constraint of the basic load, the energy flow relationship between photovoltaic power generation and the basic load, the adjustable load, the home energy storage system and the power grid is established, thereby forming the energy flow model dynamically coupled with the operating state of the energy storage battery.

3. The integrated smart distribution method for home photovoltaic and energy storage according to claim 1, characterized in that, Based on the energy flow model, combined with grid electricity price information and energy storage battery safety constraints, the distribution ratio of household photovoltaic power generation among household loads, household energy storage systems, and the grid is optimized to generate a target energy allocation strategy, including: Based on the aforementioned grid electricity price information, a grid electricity price cost item is constructed that reflects the electricity purchase cost and grid connection revenue in different time periods; Based on the charging and discharging power, state of charge change range, and temperature parameters of the energy storage battery, the equivalent aging cost of the energy storage battery in the current control cycle is calculated, and the equivalent aging cost item of the energy storage battery is formed. A joint optimization objective function is constructed by combining the grid electricity price cost item and the energy storage battery equivalent aging cost item. Under the conditions of satisfying the energy flow model and the safety constraints of the energy storage battery, the distribution ratio of the household photovoltaic power generation among the household load, the household energy storage system and the power grid is solved with the joint optimization objective function as the optimization objective to obtain the solution result; The target energy allocation strategy is generated based on the solution results for subsequent control execution.

4. The integrated intelligent distribution method for home photovoltaic and energy storage according to claim 3, characterized in that, The construction of a grid electricity price cost item, reflecting the electricity purchase cost and grid connection revenue at different time periods, based on the grid electricity price information, includes: Obtain the time-of-use electricity price information of the power grid corresponding to the current control cycle. The time-of-use electricity price information of the power grid includes at least the peak electricity price, the normal electricity price, and the valley electricity price. Based on the time-of-use electricity price information of the power grid, the electricity purchase price parameter corresponding to the household when purchasing electricity from the power grid, and the grid connection revenue parameter corresponding to the household when connecting to the power grid are determined respectively; Based on the electricity purchase price parameter, the grid connection revenue price parameter, and the power interaction direction between the household and the power grid, a grid electricity price cost calculation rule corresponding to the control cycle is constructed. Based on the grid electricity price cost calculation rules, the household's purchased electricity and grid-connected electricity during the control period are quantitatively evaluated to generate the grid electricity price cost item for joint optimization objective function calculation.

5. The integrated smart distribution method for home photovoltaic and energy storage according to claim 3, characterized in that, Based on the charging and discharging power, state of charge variation, and temperature parameters of the energy storage battery, the equivalent aging cost of the energy storage battery within the current control cycle is calculated, and an equivalent aging cost item for the energy storage battery is formed, including: Within the current control cycle, the actual charge and discharge power change curve of the energy storage battery and the corresponding state of charge (SOC) change curve are obtained. Based on the charge and discharge power change curve and the state of charge (SOC) change curve, the magnitude of the state of charge change within the control cycle is calculated to characterize the depth of charge and discharge of the energy storage battery. The temperature parameters of the energy storage battery within the control cycle are obtained, and the correction coefficient for the effect of temperature on the aging of the energy storage battery is determined based on the temperature parameters. Based on the charging and discharging power, the change in state of charge, the temperature parameter, and the correction coefficient, the equivalent aging cost of the energy storage battery during the control cycle is calculated. The equivalent aging cost is used as a cost term characterizing the degree of lifespan loss of the energy storage battery and is introduced into the joint optimization objective function to form the equivalent aging cost term of the energy storage battery.

6. The integrated intelligent distribution method for home photovoltaic and energy storage according to claim 1, characterized in that, The method of controlling the charging and discharging state of the home energy storage system and the power interaction between the home and the power grid, based on the target energy distribution strategy, to achieve intelligent energy distribution on the home side, includes: Based on the target energy distribution strategy, charging control commands or discharging control commands are generated for the home energy storage system, and power interaction control commands are generated for controlling the power purchased or fed into the grid between the home and the grid. The charging control command or the discharging control command is sent to the home energy storage system to adjust the actual charging and discharging power of the home energy storage system; The power interaction control command is sent to the grid connection control device to control the power purchased or connected to the grid between the household and the grid, so that the power distribution on the household side meets the target energy distribution strategy. During the execution of the charging and discharging control commands and the power interaction control commands, the operating status data of the home energy storage system and the actual power interaction data between the home and the power grid are continuously collected. When the operating status data or the actual power interaction data is detected to deviate from the expected range corresponding to the target energy allocation strategy, the target energy allocation strategy is corrected based on the deviation, and the charging control command, the discharging control command, or the power interaction control command is updated.

7. The integrated intelligent distribution method for home photovoltaic and energy storage according to claim 1, characterized in that, Before performing optimization calculations on the allocation ratio of household photovoltaic power generation, the following steps are also included: Based on the historical electricity consumption behavior of household electricity load, household energy demand is divided into multiple energy responsibility types, including at least immediate energy demand, deferred energy demand, and energy storage compensation energy demand. A corresponding energy responsibility quota is assigned to each of the aforementioned energy consumption responsibility types. The energy responsibility quota is used to limit the proportion of photovoltaic energy, grid energy, or energy storage energy that can be consumed by the energy consumption responsibility type within a preset control period. The energy responsibility quota is introduced as a constraint into the energy flow model, so that the allocation ratio of household photovoltaic power generation is constrained by the energy responsibility quota.

8. The integrated smart distribution method for home photovoltaic and energy storage according to claim 7, characterized in that, The calculation of the allocation ratio for household photovoltaic power generation also includes: Based on the energy responsibility quota, calculate the actual energy consumption value of each of the energy responsibility types in the current control cycle; The actual energy consumption value is compared with the corresponding energy responsibility quota to obtain the energy responsibility deviation. The energy responsibility deviation is incorporated into the joint optimization objective function as a constraint or penalty term to suppress the deviation of the energy allocation result from the energy responsibility quota. The target energy allocation strategy is generated based on the joint optimization objective function that includes the energy responsibility deviation.

9. The integrated intelligent distribution method for home photovoltaic and energy storage according to claim 6, characterized in that, When controlling the charging and discharging state of the home energy storage system and the power interaction between the home and the power grid according to the target energy distribution strategy, the method further includes: Based on the energy responsibility quota and the target energy allocation strategy, determine the power execution priority corresponding to different energy consumption responsibility types in the current control cycle; When the household photovoltaic power generation or the household electricity load fluctuates, the charging and discharging power or grid power interaction behavior corresponding to the low priority energy consumption responsibility type is adjusted first. Provided that the energy responsibility quota is met, dynamic compensation is allowed for the power allocation corresponding to high priority energy responsibility types.

10. A home integrated photovoltaic and energy storage intelligent distribution system, used to execute the home integrated photovoltaic and energy storage intelligent distribution method according to any one of claims 1 to 9, characterized in that, include: The acquisition unit is used to acquire real-time power generation data of the household photovoltaic power generation system, real-time power consumption data of the household electrical load, and operating status data of the household energy storage system. The operating status data includes the state of charge (SOC) and temperature parameters of the energy storage battery. The construction unit is used to construct an energy flow model of household photovoltaic-load-energy storage based on the photovoltaic power generation data, the electricity load data and the operating status data, and to determine the feasible constraints of each energy flow direction in the energy flow model. The calculation unit is used to optimize the distribution ratio of household photovoltaic power generation among household load, household energy storage system and grid based on the energy flow model, combined with grid electricity price information and energy storage battery safety constraints, and generate target energy distribution strategy. The control unit is used to control the charging and discharging state of the home energy storage system and the power interaction relationship between the home and the power grid according to the target energy distribution strategy, so as to realize intelligent distribution of energy consumption on the home side.

Images