Multi-mode energy scheduling method and system for balcony photovoltaic energy storage system

Abstract

The invention relates to the technical field of power electronics, and discloses a multi-mode energy scheduling method and system for a balcony photovoltaic energy storage system, and the method comprises the steps: collecting real-time multi-source data of a user family corresponding to the balcony photovoltaic energy storage system, so as to construct an operation state vector of the balcony photovoltaic energy storage system; constructing a photovoltaic power generation power analysis curve and a load power utilization analysis curve of the balcony photovoltaic energy storage system; analyzing the energy supply and demand situation and the cost effectiveness of the balcony photovoltaic energy storage system to construct a multi-stage scheduling mode of the balcony photovoltaic energy storage system, and determining a target scheduling mode of the balcony photovoltaic energy storage system to generate an energy scheduling scheme of the balcony photovoltaic energy storage system; and generating a control instruction of the balcony photovoltaic energy storage system to execute energy scheduling of the balcony photovoltaic energy storage system. According to the invention, adaptability and accuracy of multi-mode energy scheduling of the balcony photovoltaic energy storage system can be improved.

Description

Technical Field

[0001] This invention relates to a multi-mode energy dispatching method and system for a balcony photovoltaic energy storage system, belonging to the field of power electronics technology. Background Technology

[0002] Multi-mode energy dispatching for balcony photovoltaic energy storage systems refers to a control strategy. Its core is that the energy management controller dynamically divides the system operation into different working modes based on real-time data (such as photovoltaic power generation, load power consumption, energy storage unit state of charge, and grid electricity price) and forecast data (such as short-term power generation and load forecasting). For each mode, a preset optimized energy allocation scheme is executed. Through multi-mode energy dispatching, multiple factors such as electricity price, weather, user habits, and grid status are comprehensively considered. It dynamically switches between various preset optimization objectives (economic, autonomous, energy efficiency, and grid support), making optimal charging and discharging decisions in real time. This maximizes user benefits, optimizes energy utilization, and ultimately contributes to building a smarter and more stable new power system.

[0003] Traditional energy dispatching methods are based on fixed rules, single objectives, and passive response control strategies. They are implemented through simple, fixed program logic embedded in inverters or controllers. They do not require connection to the cloud or complex user configuration. However, because the rules are fixed, while weather, user electricity consumption behavior, and grid conditions change daily, the system performance is highly dependent on preset parameters, cannot adapt to dynamic environments, and often makes inappropriate decisions, resulting in low resource utilization efficiency. Summary of the Invention

[0004] This invention provides a multi-mode energy dispatching method and system for a balcony photovoltaic energy storage system, the main purpose of which is to improve the adaptability and accuracy of multi-mode energy dispatching in the balcony photovoltaic energy storage system.

[0005] To achieve the above objectives, the present invention provides a multi-mode energy dispatching method for a balcony photovoltaic energy storage system, comprising: Real-time multi-source data of the user household corresponding to the balcony photovoltaic energy storage system is collected. The real-time multi-source data includes: real-time power generation, real-time state of charge, electricity price information, real-time power consumption, real-time meteorological data and user electricity consumption preferences, so as to construct the operating state vector of the balcony photovoltaic energy storage system. Historical multi-source data of the balcony photovoltaic energy storage system are obtained to construct the photovoltaic power generation analysis curve and load power consumption analysis curve of the balcony photovoltaic energy storage system; Based on the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information, the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system are analyzed to construct a multi-level scheduling mode for the balcony photovoltaic energy storage system. The multi-level scheduling mode includes: energy surplus mode, energy deficit mode, electricity price incentive mode, and protection and standby mode. Based on the operating state vector and the multi-level scheduling mode, the target scheduling mode of the balcony photovoltaic energy storage system is determined to generate the energy scheduling scheme of the balcony photovoltaic energy storage system. Based on the energy dispatch scheme, control commands for the balcony photovoltaic energy storage system are generated to execute the energy dispatch of the balcony photovoltaic energy storage system.

[0006] Optionally, the step of analyzing the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system based on the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information includes: Based on the electricity price information, fit the electricity price information curve of the balcony photovoltaic energy storage system; Unify the time axes of the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information curve; Based on the time axis, calculate the net power of the photovoltaic power generation analysis curve and the load power consumption analysis curve; Based on the net power, the net power curve of the balcony photovoltaic energy storage system is fitted to analyze the energy supply and demand situation of the balcony photovoltaic energy storage system. Extract the purchase price and sales price of electricity from the electricity price information curve; Based on the electricity purchase price, the electricity sales price, and the energy supply and demand situation, the cost-effectiveness of the balcony photovoltaic energy storage system is analyzed.

[0007] Optionally, the step of analyzing the cost-effectiveness of the balcony photovoltaic energy storage system based on the electricity purchase price, the electricity sales price, and the energy supply and demand situation includes: Based on the electricity purchase price, the electricity sales price, and the net power corresponding to the energy supply and demand situation, calculate the daily net cost of the balcony photovoltaic energy storage system without energy storage. Based on the energy supply and demand situation, determine the purchased power and sold power of the balcony photovoltaic energy storage system; Calculate the minimum total electricity cost of the balcony photovoltaic energy storage system based on the purchased power and the sold power. The daily energy storage revenue of the balcony photovoltaic energy storage system is calculated based on the net daily cost without energy storage and the minimum total electricity cost to determine the cost-effectiveness of the balcony photovoltaic energy storage system.

[0008] Optionally, the real-time multi-source data collected from the balcony photovoltaic energy storage system corresponding to the user's household includes: Define the monitoring objectives of the balcony photovoltaic energy storage system; Based on the monitoring target, configure the balcony photovoltaic energy storage system with multiple sensors; Analyze the circuit layout of the user's home to construct a sensor distribution map of the multi-sensor system; Determine the communication protocol between the multi-sensor system and the balcony photovoltaic energy storage system; Based on the communication protocol and the sensor distribution map, a sensor network for the balcony photovoltaic energy storage system is constructed. Based on the sensor network, real-time multi-source data of the households are collected.

[0009] Optionally, the construction of the operating state vector of the balcony photovoltaic energy storage system includes: The real-time multi-source data corresponding to the balcony photovoltaic energy storage system is preprocessed to obtain preprocessed real-time multi-source data. Determine the timestamp of the preprocessed real-time multi-source data; Based on the timestamp, the preprocessed real-time multi-source data is time-aligned to obtain aligned multi-source data; Define the vector format of the balcony photovoltaic energy storage system; Extract the multi-source feature vectors from the aligned multi-source data; Based on the vector format, the multi-source feature vectors are fused to obtain the running state vector.

[0010] Optionally, the photovoltaic power generation analysis curve and load power consumption analysis curve for constructing the balcony photovoltaic energy storage system include: Extract historical meteorological data and historical power generation data corresponding to the balcony photovoltaic energy storage system from the historical multi-source data. The meteorological time-series features of the historical meteorological data and the power generation time-series features of the historical power generation data are extracted respectively. Analyze the causal relationship between the meteorological time series characteristics and the power generation time series characteristics; Based on the causal relationship, the historical meteorological data, and the historical power generation data, a photovoltaic power generation analysis curve of the balcony photovoltaic energy storage system is constructed. Extract the historical electricity consumption characteristics from the historical multi-source data, and extract the electricity preference characteristics of the users corresponding to the balcony photovoltaic energy storage system; Based on the historical electricity consumption characteristics and the electricity consumption preference characteristics, a user electricity consumption profile of the balcony photovoltaic energy storage system is constructed. Based on the user's electricity consumption profile, a load electricity consumption analysis curve for the balcony photovoltaic energy storage system is constructed.

[0011] Optionally, the multi-level scheduling mode for constructing the balcony photovoltaic energy storage system includes: Based on the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system, the mode switching variables of the balcony photovoltaic energy storage system are defined. Based on the mode switching variables, determine the mode triggering conditions and core objectives of the balcony photovoltaic energy storage system; Based on the aforementioned mode triggering conditions and core objectives, a multi-level scheduling mode for the balcony photovoltaic energy storage system is constructed.

[0012] Optionally, generating the energy dispatch scheme for the balcony photovoltaic energy storage system includes: If the target scheduling mode corresponding to the balcony photovoltaic energy storage system is the energy surplus mode, the energy storage unit of the balcony photovoltaic energy storage system is charged to obtain an energy storage charging scheme. Analyze the charging power of the energy storage charging scheme. If the net power corresponding to the target scheduling mode is greater than the charging power, sell the extra power that exceeds the charging power to obtain an energy sales scheme. If the target scheduling mode is an energy deficit mode, release the stored power of the energy storage unit to obtain an energy storage release scheme; If the net power is less than the stored power, the power deficit caused by the net power being less than the stored power is purchased to obtain an energy purchase plan. If the target scheduling mode is an electricity price incentive mode, the net power and the stored power are sold to obtain a peak-valley electricity price arbitrage scheme; If the target scheduling mode is protection and standby mode, execute the stop charging and discharging behavior of the energy storage unit and the disconnection interaction behavior of the corresponding power grid of the balcony photovoltaic energy storage system; Based on the aforementioned stop charging / discharging behavior and the aforementioned disconnection interaction behavior, a protection standby scheme for the balcony photovoltaic energy storage system is integrated. An energy dispatch scheme for the balcony photovoltaic energy storage system is integrated based on the protection standby scheme, the peak-valley electricity price arbitrage scheme, the energy purchase scheme, the energy storage release scheme, the energy sale scheme, and the energy storage charging scheme.

[0013] Optionally, the calculation of the control commands for generating the balcony photovoltaic energy storage system based on the energy dispatch scheme includes: Extract the power value and mode flag of the energy dispatch scheme; Determine the instruction format for the balcony photovoltaic energy storage system; Based on the power value, the mode flag, and the instruction format, an instruction mapping table for the energy scheduling scheme is generated; The energy scheduling scheme and the instruction mapping table are mapped to obtain control instructions.

[0014] To address the aforementioned problems, the present invention also provides a multi-mode energy dispatching system for a balcony photovoltaic energy storage system, the system comprising: The operating status determination module is used to collect real-time multi-source data of the user households corresponding to the balcony photovoltaic energy storage system. The real-time multi-source data includes: real-time power generation, real-time state of charge, electricity price information, real-time power consumption, real-time meteorological data, and user electricity consumption preferences, so as to construct the operating status vector of the balcony photovoltaic energy storage system. The power curve construction module is used to acquire historical multi-source data of the balcony photovoltaic energy storage system in order to construct the photovoltaic power generation analysis curve and load power consumption analysis curve of the balcony photovoltaic energy storage system. The scheduling mode construction module is used to analyze the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system based on the photovoltaic power generation analysis curve, the load power consumption analysis curve and the electricity price information, so as to construct a multi-level scheduling mode for the balcony photovoltaic energy storage system. The multi-level scheduling mode includes: energy surplus mode, energy deficit mode, electricity price incentive mode and protection and standby mode. The scheduling scheme determination module is used to determine the target scheduling mode of the balcony photovoltaic energy storage system based on the operating state vector and the multi-level scheduling mode, so as to generate the energy scheduling scheme of the balcony photovoltaic energy storage system. The energy scheduling execution module is used to generate control commands for the balcony photovoltaic energy storage system based on the energy scheduling scheme, so as to execute the energy scheduling of the balcony photovoltaic energy storage system.

[0015] Compared to the problems described in the background technology, this invention achieves refined, automated, and economical operation of the balcony photovoltaic energy storage system. By collecting real-time power generation, state of charge, electricity price, power consumption, meteorological data, and user preferences, a comprehensive operating state vector is constructed, enabling the system to accurately perceive the dynamic changes of itself and the external environment. Combined with the photovoltaic power generation and load power consumption analysis curves constructed from historical data, the system can accurately predict future energy supply and demand trends. Based on this analysis, the constructed multi-level scheduling mode, including energy surplus, deficit, electricity price incentive, and protection standby modes, provides the system with a clear and structured decision-making framework capable of handling various operating scenarios. By matching the real-time state vector with the multi-level scheduling mode, this invention allows the system to automatically determine the optimal target scheduling mode and generate a specific energy scheduling scheme. This scheme not only considers the balance of energy supply and demand but also incorporates electricity price cost-effectiveness factors, realizing a shift from passive response to proactive optimization. Ultimately, the control commands generated based on the scheduling scheme directly drive the hardware devices to perform precise charging and discharging, electricity purchase and sale, and load management operations, ensuring the physical implementation of the scheduling strategy. The entire process forms a closed-loop control system from data perception, situational analysis, intelligent decision-making to precise execution, significantly improving the system's energy self-sufficiency rate, reducing user electricity costs, ensuring system operational safety, and achieving intelligent response to user electricity preferences, maximizing the comprehensive utilization value of distributed energy. Therefore, the multi-mode energy scheduling method for balcony photovoltaic energy storage systems provided in this embodiment of the invention can improve the adaptability and accuracy of multi-mode energy scheduling for balcony photovoltaic energy storage systems. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a multi-mode energy dispatching method for a balcony photovoltaic energy storage system according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of a multi-mode energy dispatching system for a balcony photovoltaic energy storage system provided in an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This application provides a multi-mode energy dispatching method for a balcony photovoltaic energy storage system. The executing entity of the multi-mode energy dispatching method for the balcony photovoltaic energy storage system includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the multi-mode energy dispatching method for the balcony photovoltaic energy storage system can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a multi-mode energy dispatching method for a balcony photovoltaic energy storage system according to an embodiment of the present invention. In this embodiment, the multi-mode energy dispatching method for the balcony photovoltaic energy storage system includes: S1. Collect real-time multi-source data of the user's household corresponding to the balcony photovoltaic energy storage system. The real-time multi-source data includes: real-time power generation, real-time state of charge, electricity price information, real-time power consumption, real-time meteorological data, and user electricity consumption preferences, so as to construct the operating state vector of the balcony photovoltaic energy storage system.

[0022] This invention, through the collection of real-time multi-source data from user households corresponding to a balcony photovoltaic energy storage system, can acquire key variables such as power generation, electricity consumption, energy storage, and electricity prices, transforming the system from a black box to a white box and providing a unique and accurate data foundation for all subsequent decisions. The real-time multi-source data refers to the collection of dynamic information from different sources, reflecting different dimensions of the system, that needs to be collected synchronously to construct the operational state vector of the balcony photovoltaic energy storage system. Real-time power generation refers to the instantaneous power value of the balcony photovoltaic panels converting solar energy into electrical energy. Real-time state of charge refers to the percentage of remaining charge in the energy storage battery relative to its total capacity at the current moment. Electricity price information refers to the price standards set by the power company for users to purchase and sell electricity from the grid. Real-time power consumption refers to the total power consumed by all operating electrical appliances within the user's household at the current moment. Real-time meteorological data refers to the set of dynamic meteorological parameters provided by data sources, reflecting the current and very short-term environmental conditions. User electricity preferences refer to the operational goals and strategy modes set by the user for the system based on their own needs, values, and lifestyle habits.

[0023] As an embodiment of the present invention, the real-time multi-source data collection of the balcony photovoltaic energy storage system corresponding to the user's household includes: Define the monitoring objectives of the balcony photovoltaic energy storage system; Based on the monitoring target, configure the balcony photovoltaic energy storage system with multiple sensors; Analyze the circuit layout of the user's home to construct a sensor distribution map of the multi-sensor system; Determine the communication protocol between the multi-sensor system and the balcony photovoltaic energy storage system; Based on the communication protocol and the sensor distribution map, a sensor network for the balcony photovoltaic energy storage system is constructed. Based on the sensor network, real-time multi-source data of the households are collected.

[0024] The monitoring target refers to the specific physical quantities, state parameters, and external information that need to be quantified, measured, and analyzed to achieve intelligent scheduling, safety protection, cost-effectiveness optimization, and user interaction of the balcony photovoltaic energy storage system. The multi-sensor refers to a set of devices deployed at various key nodes of the system to sense and convert specific physical quantities into readable electrical signals and digital data, such as power sensors, environmental sensors, and state sensors, in order to achieve the monitoring target. The circuit layout refers to the flow path, connection method, and physical distribution of key nodes of electrical energy within a user's home from its source to its load. The sensor distribution map is a logical diagram based on the analysis of the circuit layout, showing how deploying multiple sensors in the circuit can most accurately and effectively obtain the location of the monitoring target. The communication protocol refers to a set of rules, formats, and semantics that must be followed by different devices in the sensor network for data exchange. The sensor network refers to a complete system that automatically collects, transmits, aggregates, and processes real-time multi-source data after physically connecting and logically configuring all multi-sensors, data acquisition terminals, and network devices according to the communication protocol and sensor distribution map.

[0025] Optionally, the sensor distribution map of the multi-sensor system can be constructed using graph neural networks, such as Spatial-based GNNs, MPNNs, etc.

[0026] Optionally, the communication protocol between the multiple sensors and the balcony photovoltaic energy storage system can be determined by an AI model, such as a sequence model like LSTM or Transformer.

[0027] This invention, through the construction of an operating state vector for the balcony photovoltaic energy storage system, can uniformly model variables such as power generation, electricity consumption, energy storage, electricity price, and user preferences. This enables the scheduling algorithm to perform calculations based on global information, achieving a balance between multiple objectives such as economy, self-sufficiency, and equipment lifespan. The operating state vector refers to a multi-dimensional mathematical vector used to comprehensively and quantitatively describe all key dynamic information of the balcony photovoltaic energy storage system and its operating environment.

[0028] As an embodiment of the present invention, the construction of the operating state vector of the balcony photovoltaic energy storage system includes: The real-time multi-source data corresponding to the balcony photovoltaic energy storage system is preprocessed to obtain preprocessed real-time multi-source data. Determine the timestamp of the preprocessed real-time multi-source data; Based on the timestamp, the preprocessed real-time multi-source data is time-aligned to obtain aligned multi-source data; Define the vector format of the balcony photovoltaic energy storage system; Extract the multi-source feature vectors from the aligned multi-source data; Based on the vector format, the multi-source feature vectors are fused to obtain the running state vector.

[0029] The preprocessed real-time multi-source data refers to the raw data collected from various sensors and interfaces, which, after undergoing a series of standardized operations such as cleaning, verification, unit conversion, and formatting, becomes a clean, well-organized dataset ready for direct analysis. The timestamp is a high-precision, standardized time code that uniquely identifies the moment the data was generated. The aligned multi-source data refers to a dataset that integrates data from different sources and with different collection frequencies onto the same time grid, using a unified timestamp as a reference. The vector format is a predefined, standardized structural template containing all necessary feature components and their data types, units, and orders for constructing the running state vector. The multi-source feature vector is a set of raw numerical values ​​extracted from the aligned multi-source data and arranged in order.

[0030] Optionally, the aligned multi-source data can be obtained by methods such as interpolation, forward padding, or nearest neighbor sampling.

[0031] Optionally, the vector format of the balcony photovoltaic energy storage system can be defined declaratively using a schema language, such as JSON Schema.

[0032] S2. Obtain historical multi-source data of the balcony photovoltaic energy storage system to construct the photovoltaic power generation analysis curve and load power consumption analysis curve of the balcony photovoltaic energy storage system.

[0033] This invention, through obtaining historical multi-source data from the balcony photovoltaic energy storage system, can accurately predict power generation and consumption for the next few hours to a day by analyzing power generation, load power, and weather data from the past few months or even years, laying the foundation for subsequent energy dispatch. The historical multi-source data refers to a set of original and derived data with precise timestamps, collected at high frequency and persistently stored over a continuous time period from various components of the balcony photovoltaic energy storage system and its operating environment, encompassing multiple dimensions.

[0034] This invention, through constructing photovoltaic power generation analysis curves and load power consumption analysis curves for the balcony photovoltaic energy storage system, quantifies the conversion efficiency and performance degradation of the photovoltaic system by comparing actual power generation curves with theoretical power generation curves. It also analyzes historical load curves to identify power consumption patterns and, combined with power generation curve predictions, implements a forward-looking charging and discharging strategy for the energy storage system, improving economic efficiency. Specifically, the photovoltaic power generation analysis curve is a visual chart formed by connecting historical data points with time on the horizontal axis and photovoltaic power generation on the vertical axis. Similarly, the load power consumption analysis curve is a visual chart formed by connecting historical data points with time on the horizontal axis and household load power on the vertical axis.

[0035] As an embodiment of the present invention, the construction of the photovoltaic power generation analysis curve and load power consumption analysis curve of the balcony photovoltaic energy storage system includes: Extract historical meteorological data and historical power generation data corresponding to the balcony photovoltaic energy storage system from the historical multi-source data. The meteorological time-series features of the historical meteorological data and the power generation time-series features of the historical power generation data are extracted respectively. Analyze the causal relationship between the meteorological time series characteristics and the power generation time series characteristics; Based on the causal relationship, the historical meteorological data, and the historical power generation data, a photovoltaic power generation analysis curve of the balcony photovoltaic energy storage system is constructed. Extract the historical electricity consumption characteristics from the historical multi-source data, and extract the electricity preference characteristics of the users corresponding to the balcony photovoltaic energy storage system; Based on the historical electricity consumption characteristics and the electricity consumption preference characteristics, a user electricity consumption profile of the balcony photovoltaic energy storage system is constructed. Based on the user's electricity consumption profile, a load electricity consumption analysis curve for the balcony photovoltaic energy storage system is constructed.

[0036] The historical meteorological data refers to a set of historical environmental parameters related to photovoltaic power generation performance, recorded in chronological order. The historical power generation data refers to historical electrical output data recorded by the energy management controller of the balcony photovoltaic system, arranged in chronological order. The meteorological time-series characteristics refer to quantitative indicators extracted from historical meteorological data that reflect weather change patterns and trends, such as radiation intensity, temperature characteristics, and periodicity. The power generation time-series characteristics refer to quantitative indicators extracted from historical power generation data that reflect photovoltaic power generation behavior patterns and statistical characteristics, such as instantaneous power, total daily power generation, daily power variation curve, peak occurrence time, and power fluctuation. The causal relationship refers to the influence relationship between meteorological time-series characteristics and power generation time-series characteristics. The historical electricity consumption characteristics refer to statistical and behavioral patterns reflecting user electricity consumption habits extracted from historical household electricity consumption data recorded by the system, such as daily / weekly / monthly electricity consumption, peak electricity consumption periods, base load power, typical load power level, and operating time. The electricity consumption preference characteristics refer to personalized rules and expectations related to electricity consumption that users actively set or select through the human-computer interaction interface, such as critical load lists, movable load lists and their expected operating periods, and work and rest schedules. The user electricity consumption profile refers to typical electricity consumption pattern labels formed by grouping days with similar historical electricity consumption characteristics.

[0037] Optionally, the causal relationship between the meteorological time series characteristics and the power generation time series characteristics can be analyzed through statistical analysis, such as correlation analysis and regression analysis.

[0038] Optionally, the user electricity consumption profile of the balcony photovoltaic energy storage system can be constructed using clustering analysis algorithms, such as K-Means, DBSCAN, etc.

[0039] S3. Based on the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information, analyze the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system to construct a multi-level scheduling mode for the balcony photovoltaic energy storage system. The multi-level scheduling mode includes: energy surplus mode, energy deficit mode, electricity price incentive mode, and protection and standby mode.

[0040] This invention, based on the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information, analyzes the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system. It can accurately calculate the cost of using energy storage, photovoltaic power, or purchasing electricity from the grid at different times, and execute the lowest-cost energy combination to achieve peak-valley electricity price arbitrage and maximize user economic benefits. The energy supply and demand situation refers to the quantitative assessment and state description of the relationship between system energy supply and demand obtained through comparative analysis of the photovoltaic power generation analysis curve and the load power consumption analysis curve. The cost-effectiveness refers to the quantitative calculation and comparison of the costs and benefits of different energy sources to evaluate and guide the financial benefits of energy dispatch strategies.

[0041] As an embodiment of the present invention, the step of analyzing the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system based on the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information includes: Based on the electricity price information, fit the electricity price information curve of the balcony photovoltaic energy storage system; Unify the time axes of the photovoltaic power generation analysis curve, the load power consumption analysis curve, and the electricity price information curve; Based on the time axis, calculate the net power of the photovoltaic power generation analysis curve and the load power consumption analysis curve; Based on the net power, the net power curve of the balcony photovoltaic energy storage system is fitted to analyze the energy supply and demand situation of the balcony photovoltaic energy storage system. Extract the purchase price and sales price of electricity from the electricity price information curve; Based on the electricity purchase price, the electricity sales price, and the energy supply and demand situation, the cost-effectiveness of the balcony photovoltaic energy storage system is analyzed.

[0042] The electricity price information curve refers to a curve with time as the horizontal axis and electricity price as the vertical axis, showing the trend of the price of purchasing electricity from the grid (purchase price) and selling electricity to the grid (sales price) over time within the future forecast period. The time axis refers to a unified time reference benchmark used to align and synchronize the photovoltaic power generation analysis curve, load power consumption analysis curve, and electricity price information curve. Net power refers to the difference between photovoltaic power generation and load power consumption at any given moment on the unified time axis. The net power curve is a curve with time as the horizontal axis and net power as the vertical axis. By connecting the net power values ​​at each moment on the time axis in chronological order, it visually demonstrates the dynamic changes in the system's energy surplus and deficit throughout the entire forecast period. The purchase price refers to the unit price that the balcony photovoltaic energy storage system needs to pay when purchasing electricity from the grid, as shown on the electricity price information curve. The sales price refers to the unit price that the balcony photovoltaic energy storage system receives when selling excess electricity back to the grid, as shown on the electricity price information curve.

[0043] Optionally, the electricity price information curve of the balcony photovoltaic energy storage system can be constructed through time series decomposition and hybrid learning.

[0044] Optionally, the net power of the photovoltaic power generation analysis curve and the load power consumption analysis curve can be obtained by calculating the difference between the photovoltaic power generation on the photovoltaic power generation analysis curve and the load power consumption on the load power consumption analysis curve at the same time.

[0045] Optionally, the step of analyzing the cost-effectiveness of the balcony photovoltaic energy storage system based on the electricity purchase price, the electricity sales price, and the energy supply and demand situation includes: Based on the electricity purchase price, the electricity sales price, and the net power corresponding to the energy supply and demand situation, calculate the daily net cost of the balcony photovoltaic energy storage system without energy storage. Based on the energy supply and demand situation, determine the purchased power and sold power of the balcony photovoltaic energy storage system; Calculate the minimum total electricity cost of the balcony photovoltaic energy storage system based on the purchased power and the sold power. The daily energy storage revenue of the balcony photovoltaic energy storage system is calculated based on the net daily cost without energy storage and the minimum total electricity cost to determine the cost-effectiveness of the balcony photovoltaic energy storage system.

[0046] The net daily cost without energy storage refers to the daily net electricity expenditure calculated solely based on photovoltaic power generation, load electricity consumption, and grid interaction, assuming the system is not equipped with energy storage units. The purchased power refers to the instantaneous power absorbed from the grid at any given time when the system's net power is negative, in order to meet load demand. The sold power refers to the instantaneous power delivered to the grid at any given time when the system's net power is positive, supplying excess photovoltaic power. Minimizing total electricity cost refers to achieving the lowest possible total electricity expenditure over a complete cycle through optimized energy dispatch strategies after configuring energy storage units. Daily energy storage revenue refers to the daily electricity cost savings achieved by configuring and optimizing the use of energy storage units.

[0047] As another implementation, the net daily cost without energy storage is calculated using the following formula:

[0048] in, This represents the daily net cost without energy storage (unit: yuan). This indicates the total number of time periods in a day. This indicates the duration of each time step (in hours). Describes the minimum value function. Indicates time Net power (unit: kilowatts). Indicates time The electricity purchase price (unit: yuan / kWh). Represents the maximum value function. Indicates time Electricity sales price (unit: yuan / kWh).

[0049] As another implementation, the minimization of total electricity cost is calculated using the following formula:

[0050] in, This represents minimizing the total electricity cost (in yuan). This indicates the total number of time periods in a day. This indicates the duration of each time step (in hours). Describes the minimum value function. This indicates the purchased power (unit: kilowatts). This indicates the electrical power sold (unit: kilowatts). Indicates time The electricity purchase price (unit: yuan / kWh). Indicates time Electricity sales price (unit: yuan / kWh).

[0051] This invention, through the construction of a multi-level scheduling mode for the balcony photovoltaic energy storage system, decomposes a complex, long-term global optimization problem into multiple simple, short-term local sub-problems, reducing the computational resource requirements and improving the solution speed. The multi-level scheduling mode refers to a scheduling architecture that decomposes the energy management decision-making process into multiple levels according to time scale and objective priority, and allows for collaborative work. The energy surplus mode refers to the system automatically entering a state when photovoltaic power generation exceeds the real-time household electricity consumption; the core task of this mode is to efficiently handle excess electricity. The energy deficit mode refers to the system automatically entering a state when real-time household electricity consumption exceeds photovoltaic power generation; the core task of this mode is to make up for the power shortage at the lowest cost. The electricity price incentive mode refers to the system actively engaging in energy arbitrage based on electricity price signals, going beyond simple real-time power balance; the core task of this mode is to create direct economic benefits. The protection and standby mode refers to the system prioritizing equipment safety and entering a dormant or safe state when extreme conditions or faults occur; the core task of this mode is to ensure system safety and lifespan.

[0052] As an embodiment of the present invention, the multi-level scheduling mode for constructing the balcony photovoltaic energy storage system includes: Based on the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system, the mode switching variables of the balcony photovoltaic energy storage system are defined. Based on the mode switching variables, determine the mode triggering conditions and core objectives of the balcony photovoltaic energy storage system; Based on the aforementioned mode triggering conditions and core objectives, a multi-level scheduling mode for the balcony photovoltaic energy storage system is constructed.

[0053] The mode switching variables refer to a set of dynamic indicators used to quantitatively describe the current operating state and future trends of the system, such as net power and battery state of charge. The mode triggering conditions refer to the specific thresholds and logical rules set based on the mode switching variables. The core objective refers to the primary task and optimization direction set for each operating mode.

[0054] Optionally, the mode switching variables of the balcony photovoltaic energy storage system can be defined using value-based reinforcement learning methods, such as Q-Learning, Deep Q-Network (DQN), etc.

[0055] S4. Based on the operating state vector and the multi-level scheduling mode, determine the target scheduling mode of the balcony photovoltaic energy storage system to generate the energy scheduling scheme of the balcony photovoltaic energy storage system.

[0056] This invention, through its embodiment based on the operational state vector and the multi-level scheduling mode, determines that the target scheduling mode of the balcony photovoltaic energy storage system can seamlessly adapt to different operational scenarios, automatically matching the most suitable scheduling mode and demonstrating good environmental adaptability. The target scheduling mode refers to the unique optimal operational mode that the system should execute at the current moment, determined by preset decision logic based on the real-time collected operational state vector during the current scheduling cycle.

[0057] This invention, through generating an energy dispatch scheme for the balcony photovoltaic energy storage system, ensures that power generation, energy storage, power consumption, and grid-connected power are in dynamic balance at all times, maintaining system stability. The energy dispatch scheme refers to a set of specific, executable energy dispatch methods generated based on a determined target dispatch mode to meet the core objectives of that mode.

[0058] As an embodiment of the present invention, generating the energy dispatch scheme of the balcony photovoltaic energy storage system includes: If the target scheduling mode corresponding to the balcony photovoltaic energy storage system is the energy surplus mode, the energy storage unit of the balcony photovoltaic energy storage system is charged to obtain an energy storage charging scheme. Analyze the charging power of the energy storage charging scheme. If the net power corresponding to the target scheduling mode is greater than the charging power, sell the extra power that exceeds the charging power to obtain an energy sales scheme. If the target scheduling mode is an energy deficit mode, release the stored power of the energy storage unit to obtain an energy storage release scheme; If the net power is less than the stored power, the power deficit caused by the net power being less than the stored power is purchased to obtain an energy purchase plan. If the target scheduling mode is an electricity price incentive mode, the net power and the stored power are sold to obtain a peak-valley electricity price arbitrage scheme; If the target scheduling mode is protection and standby mode, execute the stop charging and discharging behavior of the energy storage unit and the disconnection interaction behavior of the corresponding power grid of the balcony photovoltaic energy storage system; Based on the aforementioned stop charging / discharging behavior and the aforementioned disconnection interaction behavior, a protection standby scheme for the balcony photovoltaic energy storage system is integrated. An energy dispatch scheme for the balcony photovoltaic energy storage system is integrated based on the protection standby scheme, the peak-valley electricity price arbitrage scheme, the energy purchase scheme, the energy storage release scheme, the energy sale scheme, and the energy storage charging scheme.

[0059] The energy storage charging scheme refers to a scheduling strategy, including specific charging power values, generated to prioritize the storage of net photovoltaic power in energy storage units under energy surplus mode. The energy selling scheme refers to a scheduling strategy, including specific electricity selling power values, generated to feed the excess power into the grid when net power exceeds energy storage charging demand under energy surplus mode. The energy storage release scheme refers to a scheduling strategy, including specific discharge power values, generated to compensate for the power gap using energy storage units under energy deficit mode. The energy purchase scheme refers to a scheduling strategy, including specific electricity purchase power values, generated to purchase the insufficient power from the grid when energy storage discharge is insufficient to fully compensate for the power gap under energy deficit mode. The peak-valley electricity price arbitrage scheme refers to a combined scheduling strategy, including specific discharge power and electricity selling power values, generated to actively release energy storage power (even net power) during peak electricity price periods to obtain economic benefits under electricity price incentive mode. The stop charging / discharging behavior refers to a strategy issued to the energy storage unit in protection and standby mode, prohibiting it from performing any charging or discharging operations. The aforementioned disconnection interaction behavior refers to a strategy issued to grid-connected equipment in protection and standby modes to prohibit it from purchasing or selling electricity to the grid. The aforementioned protection standby scheme refers to a complete set of combined strategies in protection and standby modes, consisting of stopping charging and discharging behavior and disconnection interaction behavior, aimed at ensuring system safety and putting it into an inactive state.

[0060] S5. Based on the energy dispatching scheme, generate control commands for the balcony photovoltaic energy storage system to execute the energy dispatching of the balcony photovoltaic energy storage system.

[0061] This invention, through the generation of control commands for the balcony photovoltaic energy storage system based on the energy dispatch scheme, can precisely control the charging and discharging power and the power purchased and sold at the watt level, and respond to state changes in real time. This ensures that the energy flow at every moment strictly follows the dispatch scheme, improving the effectiveness and stability of the balcony photovoltaic energy storage system's dispatch. The control commands refer to a standardized set of digital signals and commands generated by the energy management controller, used to directly drive the various hardware devices in the balcony photovoltaic energy storage system to perform specific actions.

[0062] As an embodiment of the present invention, the step of generating control commands for the balcony photovoltaic energy storage system based on the energy dispatch scheme includes: Extract the power value and mode flag of the energy dispatch scheme; Determine the instruction format for the balcony photovoltaic energy storage system; Based on the power value, the mode flag, and the instruction format, an instruction mapping table for the energy scheduling scheme is generated; The energy scheduling scheme and the instruction mapping table are mapped to obtain control instructions.

[0063] The power value refers to the specific, quantified power magnitude set for each energy flow path in the energy scheduling scheme. The mode flag is a logical identifier used in the energy scheduling scheme to indicate whether the system needs to enter a special operating state. The instruction format is the structural specification of control commands that can be recognized and followed by each hardware device within the system. The instruction mapping table is a correspondence table that establishes the conversion relationship between logical elements in the energy scheduling scheme and specific device control instructions.

[0064] Optionally, the instruction mapping table of the energy scheduling scheme can be constructed using a domain model, such as ML or a specific DSL.

[0065] The embodiments of the present invention can achieve real-time and quantitative control of the power of various paths such as photovoltaic power generation, energy storage charging and discharging, grid purchase and sale, and load power consumption by executing the energy scheduling of the balcony photovoltaic energy storage system, thereby realizing the precise scheduling of the energy of the balcony photovoltaic energy storage system.

[0066] Compared to the problems described in the background technology, this invention achieves refined, automated, and economical operation of the balcony photovoltaic energy storage system. By collecting real-time power generation, state of charge, electricity price, power consumption, meteorological data, and user preferences, a comprehensive operating state vector is constructed, enabling the system to accurately perceive the dynamic changes of itself and the external environment. Combined with the photovoltaic power generation and load power consumption analysis curves constructed from historical data, the system can accurately predict future energy supply and demand trends. Based on this analysis, the constructed multi-level scheduling mode, including energy surplus, deficit, electricity price incentive, and protection standby modes, provides the system with a clear and structured decision-making framework capable of handling various operating scenarios. By matching the real-time state vector with the multi-level scheduling mode, this invention allows the system to automatically determine the optimal target scheduling mode and generate a specific energy scheduling scheme. This scheme not only considers the balance of energy supply and demand but also incorporates electricity price cost-effectiveness factors, realizing a shift from passive response to proactive optimization. Ultimately, the control commands generated based on the scheduling scheme directly drive the hardware devices to perform precise charging and discharging, electricity purchase and sale, and load management operations, ensuring the physical implementation of the scheduling strategy. The entire process forms a closed-loop control system from data perception, situational analysis, intelligent decision-making to precise execution, significantly improving the system's energy self-sufficiency rate, reducing user electricity costs, ensuring system operational safety, and achieving intelligent response to user electricity preferences, maximizing the comprehensive utilization value of distributed energy. Therefore, the multi-mode energy scheduling method for balcony photovoltaic energy storage systems provided in this embodiment of the invention can improve the adaptability and accuracy of multi-mode energy scheduling for balcony photovoltaic energy storage systems.

[0067] like Figure 2The diagram shown is a functional module diagram of a multi-mode energy dispatching system for a balcony photovoltaic energy storage system according to the present invention.

[0068] The multi-mode energy dispatching system 200 for a balcony photovoltaic energy storage system described in this invention can be installed in an electronic device. Depending on the functions implemented, the multi-mode energy dispatching system includes an operating status determination module 201, a power curve construction module 202, a dispatching mode construction module 203, a dispatching scheme determination module 204, and an energy dispatching execution module 205. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0069] In this embodiment of the invention, the functions of each module / unit are as follows: The operating status determination module 201 is used to collect real-time multi-source data of the user household corresponding to the balcony photovoltaic energy storage system. The real-time multi-source data includes: real-time power generation, real-time state of charge, electricity price information, real-time power consumption, real-time meteorological data and user electricity consumption preferences, so as to construct the operating status vector of the balcony photovoltaic energy storage system. The power curve construction module 202 is used to acquire historical multi-source data of the balcony photovoltaic energy storage system in order to construct the photovoltaic power generation analysis curve and load power consumption analysis curve of the balcony photovoltaic energy storage system. The scheduling mode construction module 203 is used to analyze the energy supply and demand situation and cost-effectiveness of the balcony photovoltaic energy storage system based on the photovoltaic power generation analysis curve, the load power consumption analysis curve and the electricity price information, so as to construct a multi-level scheduling mode for the balcony photovoltaic energy storage system. The multi-level scheduling mode includes: energy surplus mode, energy deficit mode, electricity price incentive mode and protection and standby mode. The scheduling scheme determination module 204 is used to determine the target scheduling mode of the balcony photovoltaic energy storage system based on the operating state vector and the multi-level scheduling mode, so as to generate the energy scheduling scheme of the balcony photovoltaic energy storage system. The energy scheduling execution module 205 is used to generate control commands for the balcony photovoltaic energy storage system based on the energy scheduling scheme, so as to execute the energy scheduling of the balcony photovoltaic energy storage system.

[0070] In detail, the modules in the multi-mode energy dispatching system 200 of the balcony photovoltaic energy storage system described in this embodiment of the invention adopt the same characteristics as described above during use. Figure 1 The method used is the same as the multi-mode energy dispatching method for a balcony photovoltaic energy storage system described in the article, and can produce the same technical effect, so it will not be repeated here.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0072] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-mode energy scheduling method for balcony photovoltaic energy storage system, characterized in that, The method comprises: collecting real-time multi-source data of a balcony photovoltaic energy storage system corresponding to a user's home, wherein the real-time multi-source data comprises real-time power generation, real-time state of charge, electricity price information, real-time power consumption, real-time meteorological data and user power consumption preferences, to construct an operation state vector of the balcony photovoltaic energy storage system; obtaining historical multi-source data of the balcony photovoltaic energy storage system to construct a photovoltaic power generation analysis curve and a load power consumption analysis curve of the balcony photovoltaic energy storage system; based on the photovoltaic power generation analysis curve, the load power consumption analysis curve and the electricity price information, analyzing the energy supply and demand situation and cost benefit of the balcony photovoltaic energy storage system to construct a multi-level scheduling mode of the balcony photovoltaic energy storage system, wherein the multi-level scheduling mode comprises an energy surplus mode, an energy deficit mode, a price incentive mode and a protection and standby mode; based on the operation state vector and the multi-level scheduling mode, determining a target scheduling mode of the balcony photovoltaic energy storage system to generate an energy scheduling scheme of the balcony photovoltaic energy storage system; based on the energy scheduling scheme, generating a control instruction of the balcony photovoltaic energy storage system to execute energy scheduling of the balcony photovoltaic energy storage system.

2. The multi-mode energy dispatching method for balcony photovoltaic energy storage system according to claim 1, characterized in that, The method comprises: based on the photovoltaic power generation analysis curve, the load power consumption analysis curve and the electricity price information, analyzing the energy supply and demand situation and cost benefit of the balcony photovoltaic energy storage system, comprises: fitting an electricity price information curve of the balcony photovoltaic energy storage system based on the electricity price information; unifying the time axis of the photovoltaic power generation analysis curve, the load power consumption analysis curve and the electricity price information curve; based on the time axis, calculating the net power of the photovoltaic power generation analysis curve and the load power consumption analysis curve; based on the net power, fitting a net power curve of the balcony photovoltaic energy storage system to analyze the energy supply and demand situation of the balcony photovoltaic energy storage system; extracting the purchase price and the selling price of the electricity price information curve; 3. The multi-mode energy dispatching method for balcony photovoltaic energy storage system according to claim 2, characterized in that, based on the purchase price, the selling price and the energy supply and demand situation, analyzing the cost benefit of the balcony photovoltaic energy storage system. The method comprises: based on the purchase price, the selling price and the corresponding net power of the energy supply and demand situation, calculating the net cost of the balcony photovoltaic energy storage system on a day without energy storage; determining the purchased power and the sold power of the balcony photovoltaic energy storage system according to the energy supply and demand situation; calculating the minimum total electricity cost of the balcony photovoltaic energy storage system according to the purchased power and the sold power; 4. The multi-mode energy dispatching method for balcony photovoltaic energy storage system according to claim 1, characterized in that, calculating the energy storage day benefit of the balcony photovoltaic energy storage system according to the net cost on a day without energy storage and the minimum total electricity cost, to determine the cost benefit of the balcony photovoltaic energy storage system. The method comprises: defining the monitoring target of the balcony photovoltaic energy storage system; based on the monitoring target, configuring a multi-sensor of the balcony photovoltaic energy storage system; analyzing a circuit layout of the user's home to construct a sensor distribution map of the multi-sensor; determining a communication protocol between the multi-sensor and the balcony photovoltaic energy storage system; constructing a sensor network of the balcony photovoltaic energy storage system according to the communication protocol and the sensor distribution map; collecting real-time multi-source data of the user's home based on the sensor network.

5. The multi-mode energy dispatching method for balcony photovoltaic energy storage system according to claim 1, characterized in that, The constructing of the operation state vector of the balcony photovoltaic energy storage system comprises: preprocessing the real-time multi-source data corresponding to the balcony photovoltaic energy storage system to obtain preprocessed real-time multi-source data; determining a timestamp of the preprocessed real-time multi-source data; time aligning the preprocessed real-time multi-source data based on the timestamp to obtain aligned multi-source data; defining a vector format of the balcony photovoltaic energy storage system; extracting a multi-source feature vector of the aligned multi-source data; fusing the multi-source feature vector based on the vector format to obtain an operation state vector. 6.The balcony photovoltaic energy storage system multi-mode energy scheduling method of claim 1, wherein, The constructing of the photovoltaic power generation analysis curve and the load power consumption analysis curve of the balcony photovoltaic energy storage system comprises: extracting historical meteorological data and historical power generation data of the historical multi-source data corresponding to the balcony photovoltaic energy storage system; extracting meteorological time sequence features of the historical meteorological data and power generation time sequence features of the historical power generation data respectively; analyzing the causal relationship between the meteorological time sequence features and the power generation time sequence features; constructing a photovoltaic power generation analysis curve of the balcony photovoltaic energy storage system based on the causal relationship, the historical meteorological data, and the historical power generation data; extracting historical power consumption features of the historical multi-source data, and extracting power consumption preference features of the user's power consumption preference corresponding to the balcony photovoltaic energy storage system; constructing a user power consumption portrait of the balcony photovoltaic energy storage system according to the historical power consumption features and the power consumption preference features; constructing a load power consumption analysis curve of the balcony photovoltaic energy storage system based on the user power consumption portrait.

7. The method of claim 1, wherein, The constructing of the multi-level scheduling mode of the balcony photovoltaic energy storage system comprises: defining a mode switching variable of the balcony photovoltaic energy storage system according to an energy supply and demand situation and cost benefit corresponding to the balcony photovoltaic energy storage system; determining a mode triggering condition and a core target of the balcony photovoltaic energy storage system according to the mode switching variable; constructing a multi-level scheduling mode of the balcony photovoltaic energy storage system based on the mode triggering condition and the core target. 8.The balcony photovoltaic energy storage system multi-mode energy scheduling method of claim 1, wherein, The generating of the energy scheduling scheme of the balcony photovoltaic energy storage system comprises: if a target scheduling mode corresponding to the balcony photovoltaic energy storage system is an energy surplus mode, charging an energy storage unit of the balcony photovoltaic energy storage system to obtain an energy storage charging scheme; analyzing a charging power of the energy storage charging scheme, if a net power corresponding to the target scheduling mode is greater than the charging power, selling an extra power exceeding the charging power to obtain an energy selling scheme; if the target scheduling mode is an energy deficit mode, releasing a stored power of the energy storage unit to obtain an energy storage releasing scheme; if the net power is less than the stored power, purchasing a deficiency power lower than the stored power to obtain an energy purchasing scheme; If the target scheduling mode is the price incentive mode, the net power and the storage power are sold to obtain a peak-valley electricity price arbitrage scheme; If the target scheduling mode is the protection and standby mode, a stop charging and discharging behavior of the energy storage unit and a cut-off interaction behavior of the balcony photovoltaic energy storage system to the corresponding power grid are executed; Based on the stop charging and discharging behavior and the cut-off interaction behavior, a protection standby scheme of the balcony photovoltaic energy storage system is integrated; According to the protection standby scheme, the peak-valley electricity price arbitrage scheme, the energy purchase scheme, the energy storage release scheme, the energy sale scheme and the energy storage charging scheme, an energy scheduling scheme of the balcony photovoltaic energy storage system is integrated.

9. The method of claim 1, wherein, The calculation of the energy scheduling scheme includes: extracting a power value and a mode flag of the energy scheduling scheme; determining an instruction format of the balcony photovoltaic energy storage system; generating an instruction mapping table of the energy scheduling scheme according to the power value, the mode flag and the instruction format; and mapping the energy scheduling scheme and the instruction mapping table to obtain a control instruction.

10. A balcony photovoltaic energy storage system multi-mode energy scheduling system, characterized in that, The system includes: an operating state determination module configured to collect real-time multi-source data of a balcony photovoltaic energy storage system corresponding to a user's home, wherein the real-time multi-source data includes real-time power generation, real-time state of charge, electricity price information, real-time power consumption, real-time weather data and user power consumption preferences, so as to construct an operating state vector of the balcony photovoltaic energy storage system; a power curve construction module configured to obtain historical multi-source data of the balcony photovoltaic energy storage system, so as to construct a photovoltaic power generation analysis curve and a load power consumption analysis curve of the balcony photovoltaic energy storage system; a scheduling mode construction module configured to analyze an energy supply and demand situation and a cost benefit of the balcony photovoltaic energy storage system based on the photovoltaic power generation analysis curve, the load power consumption analysis curve and the electricity price information, so as to construct a multi-level scheduling mode of the balcony photovoltaic energy storage system, wherein the multi-level scheduling mode includes an energy surplus mode, an energy deficit mode, a price incentive mode and a protection and standby mode; a scheduling scheme determination module configured to determine a target scheduling mode of the balcony photovoltaic energy storage system based on the operating state vector and the multi-level scheduling mode, so as to generate an energy scheduling scheme of the balcony photovoltaic energy storage system; an energy scheduling execution module configured to generate a control instruction of the balcony photovoltaic energy storage system based on the energy scheduling scheme, so as to execute energy scheduling of the balcony photovoltaic energy storage system.

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