Multi-source energy and multi-mode operation management method and system of smart power grid

Abstract

The invention relates to a multi-source energy and multi-mode operation management method and system of an intelligent power grid, and the method comprises the steps: obtaining commercial power energy data, photovoltaic energy data and energy storage system state parameters, carrying out the initial electric energy distribution of an AC output interface and a bidirectional module, and obtaining an initial electric energy distribution scheme; obtaining direct current load demand data through a direct current output interface, and performing route dynamic selection in combination with the energy storage system state parameters and the initial electric energy distribution scheme to obtain a route execution result; constructing an energy scheduling optimization instruction of the bidirectional module based on the initial electric energy distribution scheme and the routing execution result; and performing cooperative regulation and control on the AC output interface, the DC output interface and the bidirectional module according to the energy scheduling optimization instruction to obtain an energy flow dynamic optimization scheme. According to the invention, intelligent dynamic regulation and control can be carried out, and the operation flexibility and stability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy management, and in particular to a multi-source energy and multi-mode operation management method and system for a smart grid. Background Art

[0002] Energy routers are becoming increasingly important in the Energy Internet, enabling flexible conversion, efficient distribution, and intelligent management of multiple energy sources. However, existing energy management methods typically focus solely on optimizing a single energy source, neglecting the synergies between multiple energy sources, resulting in low overall efficiency. Furthermore, traditional systems lack flexibility and intelligent control when dealing with complex load demands, impacting system performance and stability. Summary of the Invention

[0003] The main purpose of the present invention is to provide a multi-source energy and multi-mode operation management method and system for a smart grid, which can perform intelligent dynamic regulation and enhance operational flexibility and stability.

[0004] To achieve the above objectives, the present invention provides a multi-source energy and multi-mode operation management method for a smart grid, comprising: Obtaining utility power data, photovoltaic energy data, and energy storage system status parameters, performing preliminary power distribution on the AC output interface and bidirectional modules, and obtaining an initial power distribution plan; Acquiring DC load demand data through a DC output interface, and dynamically selecting a route based on the energy storage system state parameters and the initial power distribution plan to obtain a route execution result; constructing energy scheduling optimization instructions for the bidirectional module based on the initial power distribution plan and the routing execution result; The AC output interface, the DC output interface and the bidirectional module are coordinated and regulated according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution.

[0005] Furthermore, the acquisition of mains energy data, photovoltaic energy data, and energy storage system status parameters, and preliminary power distribution to the AC output interface and the bidirectional module to obtain an initial power distribution plan includes: Performing power sampling on the mains energy data input from the power grid to obtain a mains power value; Performing maximum power point tracking on the photovoltaic energy data input by the photovoltaic circuit to obtain a photovoltaic available power value; Acquiring the energy storage system state parameters of the energy storage system through the bidirectional module, and performing power analysis to obtain battery charge and discharge power values; Calculating a total available power value of the system according to the mains power value, the photovoltaic available power value, and the battery charging and discharging power value; Performing AC load identification based on the AC output interface to obtain AC load power demand; The power gap calculation and electric energy distribution are performed on the total available power value of the system and the power demand of the AC load according to a preset safety management mechanism to obtain the initial electric energy distribution plan.

[0006] Furthermore, the DC load demand data is obtained through the DC output interface, and a dynamic route selection is performed in combination with the energy storage system state parameters and the initial power distribution plan to obtain a route execution result, including: Performing real-time load demand identification on the DC output interface to obtain DC load demand data; Performing processing route identification according to the energy storage system state parameters and the initial electric energy distribution plan to obtain initial processing route data; performing load analysis on the DC load demand data according to the energy storage system state parameters and the initial power distribution plan to obtain a load demand analysis result; Performing route selection according to the load demand analysis result and the initial processing route data to obtain a route selection result; According to the routing selection result, routing is performed on the DC output interface and the AC output interface respectively to obtain the routing execution result.

[0007] Furthermore, performing route selection according to the load demand analysis result and the initial processing routing data to obtain a routing selection result includes: Performing gap feature extraction on the load demand analysis result to obtain a power gap change rate parameter and a power gap duration parameter; performing routing parameter parsing on the initially processed routing data to obtain a first routing mode parameter and a second routing mode parameter; Performing a demand urgency assessment based on the power gap change rate parameter and the power gap duration parameter to obtain a load demand urgency level; Dual routing applicability evaluations are performed on the first routing mode parameters and the second routing mode parameters respectively according to the load demand urgency level, and routing comparisons are performed to obtain the routing selection result.

[0008] Furthermore, the energy scheduling optimization instruction of the bidirectional module is constructed based on the initial power distribution plan and the routing execution result, including: Decomposing the initial electric energy distribution plan into module instructions to obtain storage and charging module instructions, optical charging module instructions, and V2G module instructions; Performing module dynamic constraints on the bidirectional module according to the storage and charging module instructions and the energy storage system state parameters to obtain storage and charging module setting values; Allocate the bidirectional module according to the routing execution result and the optical charging module instruction vector to obtain an optical charging module setting value; Performing bidirectional scheduling on the bidirectional module according to the V2G module instruction vector and the routing execution result to obtain a V2G flow direction setting value; A scheduling instruction is constructed for the storage and charging module setting value, the optical charging module setting value and the V2G flow direction setting value to obtain the energy scheduling optimization instruction.

[0009] Furthermore, the AC output interface, the DC output interface, and the bidirectional module are collaboratively controlled and constructed according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution, including: performing AC voltage and frequency coordinated adjustment on the AC output interface according to the energy scheduling optimization instruction to obtain AC output parameters; Performing a DC bus voltage dynamic adjustment process on the DC output interface according to the energy scheduling optimization instruction to obtain a DC output parameter; Selecting a working mode for the bidirectional module according to the energy scheduling optimization instruction to obtain an integrated working mode state; Performing power allocation processing on the bidirectional module according to the energy scheduling optimization instruction to obtain a module power allocation state; A collaborative scheme is constructed for the AC output parameters, the DC output parameters, the integrated working mode state and the module power distribution state to obtain a dynamic energy flow optimization scheme.

[0010] Furthermore, selecting the working mode of the bidirectional module according to the energy scheduling optimization instruction to obtain an integrated working mode state includes: According to the energy scheduling optimization instruction, the system parameters are extracted through the bidirectional module to obtain real-time irradiance data, electricity price period parameters, energy storage system capacity information, V2G scheduling instruction parameters and vehicle battery capacity value; Performing photovoltaic decision analysis on the bidirectional module according to the real-time irradiance data to obtain a photovoltaic processing routing working mode; Performing storage and charging decision analysis on the bidirectional module according to the electricity price period parameters and the energy storage system capacity information to obtain a storage and charging module working mode; Performing mode constraint judgment on the bidirectional module according to the V2G scheduling instruction parameter and the vehicle battery capacity value to obtain a V2G module operating mode; Mode state encapsulation processing is performed on the photovoltaic processing routing working mode, the storage and charging module working mode, and the V2G module working mode to obtain the integrated working mode state.

[0011] Furthermore, performing photovoltaic decision analysis on the bidirectional module according to the real-time irradiance data to obtain a photovoltaic processing routing working mode includes: Extracting features from the functional configuration information of the bidirectional module to obtain a conversion support identifier; Performing a threshold comparison on the real-time irradiance data according to a preset irradiance intensity threshold to obtain an irradiation status indicator; Performing dominant mode configuration on the bidirectional module based on the irradiation state identifier and the conversion support identifier to obtain a configuration processing result; The configuration processing result is encapsulated into a working mode to obtain the photovoltaic processing routing working mode.

[0012] The present invention further provides a multi-source energy and multi-mode operation management system for a smart grid, which is applied to any of the multi-source energy and multi-mode operation management methods for a smart grid described above, comprising: A collection module is used to obtain mains energy data, photovoltaic energy data and energy storage system status parameters, perform preliminary power distribution on the AC output interface and the bidirectional module, and obtain an initial power distribution plan; An analysis module, configured to obtain DC load demand data through a DC output interface, and dynamically select a route based on the energy storage system state parameters and the initial power distribution plan to obtain a route execution result; an association module, configured to construct an energy scheduling optimization instruction for the bidirectional module based on the initial electric energy distribution plan and the routing execution result; A processing module is provided, wherein the processing module coordinates and controls the AC output interface, the DC output interface and the bidirectional module according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution.

[0013] The present invention provides a multi-source energy and multi-mode operation management method and system for a smart grid, which has the following beneficial effects: By acquiring multi-source energy data, including utility power, photovoltaics, and energy storage systems, and combining it with real-time monitoring of the energy storage system's status, it is possible to coordinate the scheduling of various energy sources, effectively solving the problem of dispersed and inefficient energy utilization in traditional systems. By dynamically acquiring AC and DC load demands and optimizing energy scheduling based on routing execution results, the system can perform intelligent dynamic regulation when faced with complex and volatile load demands, thereby enhancing the system's operational flexibility and stability. Energy scheduling optimization instructions are generated based on routing selection results and the initial power distribution plan, and key system modules are coordinated and controlled, further improving the efficiency and accuracy of energy flow and reducing energy waste and unnecessary energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a multi-source energy and multi-mode operation management method for a smart grid provided by the present invention; Figure 2 This is the second flow chart of a multi-source energy and multi-mode operation management method for a smart grid provided by the present invention; Figure 3 This is a structural diagram of a multi-source energy and multi-mode operation management system for a smart grid provided by the present invention.

[0015] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] Reference Figure 1-2 As shown, the present invention provides a multi-source energy and multi-mode operation management method for a smart grid, comprising: Step S1: Obtaining mains energy data, photovoltaic energy data, and energy storage system status parameters, performing preliminary power distribution on the AC output interface and bidirectional modules, and obtaining an initial power distribution plan; Step S2: obtaining DC load demand data through the DC output interface, and dynamically selecting a route based on the energy storage system state parameters and the initial power distribution plan to obtain a route execution result; Step S3: constructing energy scheduling optimization instructions for the bidirectional module based on the initial power distribution plan and the routing execution result; Step S4: Coordinately control the AC output interface, the DC output interface, and the bidirectional module according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Sensors and data acquisition modules collect real-time operating status data from the mains, photovoltaic (PV) system, and energy storage system, including voltage, current, power, battery state of charge (SOC), and PV irradiance. The grid-connected energy data is acquired by the grid-connected data acquisition module, while the bidirectional module provides PV energy data. The battery management system (BMS) collects energy storage system status, primarily including battery voltage, current, and charge. This data is input into the energy management system (EMS), which then performs preliminary energy allocation based on the PV access method (e.g., Method 1: PV array grid-connected via a DC-DC bidirectional module with MPPT, Method 2: PV array grid-connected via an AC-DC bidirectional module with MPPT) and the current battery status. The core objective of this preliminary energy allocation plan is to develop a preliminary power allocation plan based on real-time data from various energy sources, including battery charging and discharging, PV power consumption, and AC load power supply. This plan is based on a rational power balance between different input sources to ensure efficient energy utilization during system operation. At this stage, the coordinated work of the AC output interface and the bidirectional module must also be considered to ensure that electric energy is distributed according to priority and provide basic data support for subsequent routing selection and energy scheduling.

[0020] Step S2: Obtain DC load demand data through the DC output interface. DC load demand data includes load power, operating voltage, and current, and is typically collected by load sensors and the EMS. Dynamic routing is performed based on the energy storage system's status parameters (such as battery state of charge and charge / discharge capacity) and the initial energy distribution plan. Routing selection determines whether to supply energy from the energy storage system, the grid, or direct PV supply based on current load demand and energy status. For example, if the DC load demand is high and the energy storage system has sufficient charge, the routing strategy might be to supply energy directly to the DC load. Alternatively, if the battery charge is low, energy may be obtained from the PV system or the mains. The routing strategy in this step needs to be adjusted in real time based on the load type, power demand, and current state of the energy storage system to ensure flexibility in responding to varying energy inputs and load demands. During this process, the energy management system needs to calculate the optimal route and provide corresponding routing execution results, which will serve as a basis for subsequent energy scheduling. The routing execution results should clearly indicate how to optimally distribute energy from various sources, such as the mains, PV system, and energy storage system.

[0021] Step S3: Based on the initial energy distribution plan and routing execution results, the energy management system optimizes the energy scheduling of bidirectional modules (e.g., DC-DC modules, AC-DC modules, energy storage modules, and V2G modules). This optimization process involves real-time adjustment of energy flow distribution based on the battery's current state of charge, load demand, V2G device status, and available energy input. For example, when the battery charge is high, the energy storage system may enter discharge mode, releasing energy to the DC load or the grid. Alternatively, when the battery charge is low, the system may prioritize charging from the mains or photovoltaic input. Furthermore, if load demand is high, the energy management system dynamically schedules the bidirectional modules based on the energy distribution plan and routing execution results to ensure that energy flows along different routes meet load demand. The key to this step is precise scheduling and control of the bidirectional modules, optimizing energy flow, avoiding energy waste, and improving overall system efficiency. By optimizing the scheduling of the bidirectional modules, an optimal balance can be achieved between battery charging and discharging, photovoltaic grid connection, V2G device charging and discharging, and load power supply, thereby improving the energy utilization of the entire system. On this basis, optimized energy scheduling instructions are generated and transmitted to each module for execution.

[0022] Step S4: According to the energy scheduling optimization instructions generated previously, the energy management system coordinates and controls the AC output interface, DC output interface and bidirectional module. The goal of this coordinated control is to achieve optimal power distribution among various energy modules (mains power, photovoltaic, energy storage system, electric vehicle, smart load connected to the AC output interface, DC load, etc.). First, according to the energy scheduling optimization instructions, the system performs voltage and frequency stabilization control on the AC output interface to ensure that the AC load can obtain a stable power supply. At the same time, the voltage of the 150kW DC output interface needs to be dynamically adjusted according to the load demand, and the voltage is adjusted within a range to adapt to different types of DC loads. Through the coordinated control of the bidirectional modules, the following are achieved: Mains power to AC output: The 90kW mains power from the grid is directly transmitted to the 180kWmax AC output interface through the safety management module, and then output to the smart load through the AC output interface, realizing various control and protection functions including anti-backflow.

[0023] Mains to V2G module: A 30kW AC-DC bidirectional module (V2G channel) enables bidirectional energy flow between the mains and V2G devices.

[0024] Mains to energy storage / DC loads: supports energy storage charging and energy storage discharge to the grid; the grid supplies power to DC loads, and DC loads (primarily electric vehicles) discharge to the grid. The grid charges the energy storage system's energy storage batteries (40-90kWh) via a 30kW AC-DC storage and full-time multiplexing bidirectional module. When the batteries discharge, energy is fed back into the system through this module; the grid supplies power to DC loads through this channel. Mains power and photovoltaic / wind power connected to DC loads: Photovoltaic / wind power and other power sources supply power to the grid through a 30kW AC-DC optical, fully time-multiplexed bidirectional module (with MPTT function); mains power and DC loads achieve bidirectional energy flow through this channel.

[0025] Battery discharge channel: The battery forms a discharge channel through a 30kW DC-DC module (with MPTT function) to power the DC load.

[0026] Photovoltaic power generation channel: 30kWp photovoltaic is connected to the system through a 30kW DC-DC module (with MPTT function), which can charge the energy storage battery, power DC loads, and supply power to the grid.

[0027] The DC load is connected to multiple bidirectional modules through a 150kW DC output interface.

[0028] The system will dynamically optimize the power distribution strategy based on the current load demand data and real-time changes in energy flow, adjust the energy flow between modules, and make immediate adjustments according to the instructions of the energy management system.

[0029] The present invention provides a multi-source energy and multi-mode operation management method for a smart grid. By acquiring multi-source energy data such as mains electricity, photovoltaics, and energy storage systems, and combining it with real-time monitoring of the status of the energy storage system, it can achieve coordinated scheduling between various energy sources, effectively solving the problems of dispersed and inefficient energy utilization in traditional systems. By dynamically acquiring AC and DC load demands and optimizing energy scheduling based on routing execution results, the system can perform intelligent dynamic regulation when facing complex and highly volatile load demands, thereby enhancing the operational flexibility and stability of the system. Energy scheduling optimization instructions are generated based on the routing selection results and the initial power distribution plan, and key modules of the system are coordinated and regulated, further improving the efficiency and accuracy of energy flow and reducing energy waste and unnecessary energy consumption.

[0030] In one embodiment, the utility power data, photovoltaic energy data, and energy storage system status parameters are obtained, and preliminary power distribution is performed on the AC output interface and the bidirectional module to obtain an initial power distribution plan, including: During system operation, the AC input interface is connected to the mains power side, and the mains power is first input through the safety management module. This module integrates a high-precision cross-sampling protection circuit, samples the voltage and current signals in real time, and inputs the sampled values ​​into the digital signal processing unit. After the sampled signal passes through the RMS calculation circuit, the mains power value is extracted. This power value reflects the available active power output capacity of the mains at the current moment. The sampling frequency is not less than 10kHz to ensure that power fluctuations are captured. The obtained mains power value is also transmitted as an input parameter to the energy management system EMS to participate in the subsequent total available power calculation and power scheduling strategy generation. Since the mains route is the basic channel for energy supply to the entire system, the sampling process needs to have a redundant monitoring mechanism and form a data synchronization mechanism with other energy routes to ensure that the system accurately references the power value for output balancing in emergency or peak scenarios.

[0031] The PV circuit input is connected to the system through a module. In Method 1, the PV panel output is directly connected to a DC-DC module with MPPT functionality, which integrates three sampling channels: light intensity, voltage, and current. The MPPT control logic dynamically tracks PV energy data, extracting the current maximum available output power while continuously adjusting the output voltage and current operating points. This process combines real-time irradiance data with module temperature correction parameters to improve the accuracy of maximum power point determination. The MPPT module feeds the available PV power value to the EMS, which contributes to the calculation of the total system power. This power value also influences the logic for allocating PV energy between the three routes: battery charging, DC load power supply, and solar charging feedback. This process also distinguishes from Method 2, where the MPPT logic is integrated into the AC-DC module. After the energy management system identifies the access method flag, the corresponding tracking mechanism is activated and the applicable range of the PV power value is simultaneously adjusted (supporting only AC output or grid feedback).

[0032] The energy storage system is connected to the system via bidirectional DC-DC modules. These modules are divided into a bidirectional storage-charging module and a bidirectional photovoltaic-charging module based on their specific functions. The storage-charging module primarily performs charging and discharging operations during periods of low utility power and peak utility power prices, while the photovoltaic-charging module coordinates energy storage and participates in energy flow when photovoltaic power is sufficient or insufficient. The system uses a communication interface to query the current energy storage status parameters of the two DC-DC modules, including battery state of charge, voltage, current, temperature, current mode (charging / discharging / standby), and internal status flags. These parameters are extracted from the storage-charging and photovoltaic-charging routers, ensuring consistent status across multiple routers.

[0033] After processing, the state parameters are analyzed for power. The charging and discharging powers are calculated based on the energy storage voltage and the current operating current, taking into account the module's operating mode and the policy instructions issued by the energy management system. In the charging scenario, if the storage-charging multiplexing module is in the mains charging phase, the maximum safe charging power must be determined in combination with the current mains load capacity. In the discharging scenario, the maximum discharge power is estimated based on the battery's state of charge upper limit, temperature rise, and parallel channel capacity. The photovoltaic charging multiplexing module focuses more on using batteries to compensate for DC load demand when photovoltaic output is insufficient, and its analysis logic is sensitive to output routing selection.

[0034] The AC-DC bidirectional module connects to V2G devices. While it doesn't directly regulate the battery, its state parameters can serve as external reference inputs to influence the battery's coordinated response strategy in certain linkage scenarios, such as nighttime V2G charging. All power analysis results are centrally integrated and serve as a core data source for total power calculation.

[0035] The total available power (TAP) of the system is defined as the maximum real-time power output capability that the entire system can provide to the load during the current operating period. The three aforementioned power data points—mains power, PV available power, and battery charge and discharge power—are integrated to form the system's total power ceiling. PV power, under Mode 1, supports multi-directional DC distribution and is fully factored into the total power. Mains power, acting as a constant input source, participates in both AC power supply and battery charging. Battery power must be capped at a discharge power ceiling based on the current SOC and a temperature correction factor, excluding transient power fluctuations. This comprehensive calculation also incorporates real-time load power feedback for power verification to ensure that the TAP is not inflated. As the core variable for system-wide energy allocation, the TAP must be updated synchronously with the output control logic, with a closed-loop control cycle of less than 100ms to ensure timely response in dynamic scenarios.

[0036] The AC output interface consists of an AC output module, an AC load terminal, and a sampling circuit. The EMS uses this interface to obtain the current AC power demand signal. The power demand is obtained by sampling the voltage and current waveforms and digitally processing them, representing the actual AC load value required to maintain power supply to the system. The identification process also includes determining load characteristics (steady-state / burst, inductive / resistive, etc.), and building a load prediction model based on historical operating data and device registration information. This process is suitable for mixed load scenarios and supports the parallel connection of loads such as household electricity, industrial loads, and electric vehicles. The load identification algorithm generates the current effective load power demand value and provides feedback, which serves as an input factor for power scheduling and power differential calculations. If this power exceeds the total available power range, the system will issue an early warning and intervene with energy storage release or load degradation mechanisms.

[0037] Power gap calculation compares the system's total available power with real-time load power demand to determine the current power surplus or shortage. A safety management mechanism is introduced to assess constraints, including the DC voltage protection range (200-750 VDC), AC voltage and frequency stability range, maximum energy storage discharge capacity, and dynamic PV output limits. When a power gap exists, priority is given to matching critical loads, while remaining loads are mitigated through load-side negotiation or power interruption. The energy allocation strategy generates an initial energy distribution plan based on the time-of-use strategy (price / time period) and routing priorities (mains to PV to energy storage). This plan specifies the proportion of utility power supply, the PV power supply route (DC or AC), the energy storage module scheduling method, and whether to activate the V2G channel. This initial energy distribution plan structurally describes the output distribution of each energy route, serving as the basis for the execution-layer energy scheduling module. It is synchronized with the safety protection unit for safety and legality verification.

[0038] This embodiment, by introducing multi-source energy and multi-mode operation management methods into the smart grid system, can flexibly obtain and process the state parameters of the mains, photovoltaic energy and energy storage system, thereby achieving efficient power distribution. Specifically, by performing power sampling on the mains energy data input from the power grid, the mains power value can be obtained in real time, ensuring the accuracy and stability of the mains power supply. Performing maximum power point tracking on the photovoltaic energy data input from the photovoltaic circuit can ensure that the photovoltaic system always outputs the maximum available power under different lighting conditions, thereby improving the efficiency of photovoltaic energy utilization. Through the storage-charging multiplexing and light-charging multiplexing functions of the bidirectional DC-DC module, the state parameters of the energy storage system can be effectively obtained, and the charge and discharge power values ​​of the battery can be accurately analyzed. This process can not only efficiently store energy during the off-peak period of the mains power, but also dynamically compensate for the load demand when the photovoltaic output is insufficient, thereby achieving flexible scheduling and optimal utilization of the energy storage system.

[0039] In one embodiment, DC load demand data is obtained through a DC output interface, and a dynamic route selection is performed in combination with energy storage system state parameters and an initial power distribution plan to obtain a route execution result, including: The 150kW DC output interface, connected to the system's DC bus, provides power to loads including DC fast chargers for electric vehicles, industrial DC equipment, and data center power supplies. Real-time load demand identification relies on high-precision current and voltage monitoring modules, which continuously track and provide feedback on metrics such as instantaneous power changes, voltage fluctuations, and load response rates at the load port. When a load connection, voltage drop, or current surge is detected, the system generates real-time load demand data based on the load's historical characteristics and configuration rules. This data includes not only the current load power value, fluctuation range, and duration, but also includes tags such as whether it possesses rigid power supply characteristics (such as charging station startup) and whether it has regulation margin (such as flexible load soft start logic). This data is transmitted in real time to the energy routing scheduling core module and serves as a key input for dynamic power scheduling and route selection. This process has a direct impact on subsequent route identification. If the load has rigid, high-priority demand, the system will prioritize stable and efficient routing. If the load exhibits delayed response characteristics, it may participate in peak shaving or flexible load adjustment.

[0040] Processing route identification relies on the system's current energy storage battery status parameters and the initial energy distribution strategy. Energy storage system status parameters primarily include real-time battery voltage, current, temperature, state of charge (SOC), whether it is in the dischargeable range, and historical charge and discharge rates. Based on these parameters, the system determines whether the energy storage system has the ability to supply power. If the battery is currently in a low state of charge (SOC <30%) or the current temperature exceeds the limit, making it unsuitable for discharge, the route is not included in the priority processing route. Furthermore, the initial energy distribution plan is often based on predefined strategies such as the load type hierarchy, time-of-day electricity pricing strategies, and the power supply capabilities of renewable energy sources (such as photovoltaics). For example, during periods of high sunlight, photovoltaic routes are prioritized; during peak electricity pricing periods, energy storage routes are prioritized. The system screens all possible routes (such as photovoltaic to DC-DC to DC bus, energy storage to DC-DC to load, and mains to AC-DC bidirectional modules to DC loads) for feasibility, eliminates currently unavailable routes, and outputs initial processing route data that is feasible. This routing data guides subsequent load analysis and final route selection.

[0041] Load demand analysis uses the real-time demand of DC loads as input and integrates multiple variables, such as energy storage status, electricity price period, and power source priority configuration, to make a judgment. During the analysis, the system predicts the compatibility between the load's continuous power demand and the energy storage's available power supply duration. For example, if a load demand is 15kW and expected to last 30 minutes, the system will infer whether it can provide power for the entire cycle based on the current energy storage system's remaining discharge capacity (estimated by the battery pack's state of charge). It also considers the electricity price category of the current time period to determine whether the discharge window is economically appropriate. If the current load is non-critical and the energy storage system is in poor condition, the system will favor PV routing or utility routing to support the load. The analysis output is divided into two dimensions: "Load Power Supply Suitability" and "Recommended Route Label," indicating whether the load can be met by a particular route and whether a more optimal route exists. This analysis result is directly input into the route selection module and serves as a weighted decision factor to influence the final route selection.

[0042] Route selection is performed under the dual constraints of route identification and load demand analysis. The system compares all initially processed routes with the best compatibility with the current load and prioritizes the route selected. For example, if the energy storage system SOC exceeds 80%, the load demand is 10 kW, and it is during peak electricity pricing, the energy storage route has sufficient capacity and superior economics, and the system marks that route as the preferred route. In some scenarios, multiple routes may provide combined power (for example, PV and energy storage supplying DC loads in parallel). The system uses a real-time power matching algorithm to allocate power among the routes, resulting in a composite route selection result. The final route selection result includes not only the route number but also the dynamic output power allocation ratio (for example, PV provides 60% and battery provides 40%), which is transmitted as structured data to the route execution module. This step ensures the coordination of all routes, optimal energy efficiency, and comprehensive economic dispatch.

[0043] The routing execution phase activates the corresponding energy conversion and transmission modules based on the generated routing results. If the routing result is "PV to DC-DC to DC output," the system immediately activates the PV MPPT controller, starts the corresponding DC-DC converter module, and feeds the converted DC power into the DC bus, ultimately outputting it to the load interface via the bus. If the routing result includes "energy storage to DC-DC to DC load," the system synchronously dispatches the energy storage management system, authorizing discharge and starting the corresponding DC-DC bidirectional converter to coordinate DC current transmission. Regarding the AC output interface, if the routing involves "mains to storage module to AC load" or "battery to storage module to AC inverter," the system synchronously executes the inverter action, converting DC to stable AC output to ensure energy supply to critical AC loads. During the routing execution process, physical parameters such as module temperature rise, current limit, and voltage fluctuation are continuously monitored, and execution status is fed back to the system scheduling core in real time. The final routing execution result, consisting of "power supply success, output power stability, and module status codes," provides closed-loop data support for subsequent adaptive energy routing scheduling.

[0044] This embodiment, by applying a multi-source energy and multi-mode operation management method in a smart grid system, enables real-time dynamic selection and optimized allocation of different energy sources, thereby improving the energy efficiency and stability of the entire system. Through real-time load demand identification, the system can accurately obtain the instantaneous power changes and demand characteristics of the load, enhancing the load response speed and power supply stability. Using energy storage system state parameters and the initial power distribution plan for route identification and selection can fully tap the potential of the energy storage system and economically dispatch according to different electricity price periods and load priorities, reducing operating costs. Load demand analysis ensures efficient matching of load demand with energy supply routes, reducing unnecessary energy conversion losses. The route execution module accurately dispatches multiple energy sources such as photovoltaics, energy storage, and mains electricity based on the selected route, achieving efficient energy supply to DC and AC loads and improving the overall energy efficiency of the system. Through the application of the above method, the smart grid system demonstrates significant flexibility and reliability when facing complex and changing load demand and energy supply environments.

[0045] In one embodiment, routing selection is performed based on the load demand analysis result and the initially processed routing data to obtain a routing selection result, including: Load demand analysis is a key component of smart grid multi-source energy and multi-mode operation management. Load demand analysis reveals the current system's actual load demand, including the magnitude, changing trends, and urgency of power demand. Detailed load demand analysis reveals the load's power shortfall change rate and duration parameters. These parameters reflect the load's power demand changes over a given period and the duration of these changes.

[0046] The power gap change rate parameter refers to the rate of change of the load power demand per unit time, reflecting the fluctuation of load demand. A higher power gap change rate may indicate that the load demand is changing rapidly, requiring the system to respond quickly to meet the load demand. The power gap duration parameter refers to the duration of the load power demand gap, reflecting the stability and duration of the load demand. If the power gap duration is longer, it indicates that the load demand gap is relatively stable, and the system can use this characteristic to schedule for a longer period of time.

[0047] After obtaining the load demand analysis results, the initial processing routing data needs to be parsed. This data includes the basic parameters of each route available in the system, such as the route's power transmission capacity, routing mode, and routing response time. By parsing these routing parameters, the first routing mode parameters and the second routing mode parameters can be obtained. These routing parameters will provide basic data support for subsequent route selection.

[0048] Extracting power gap characteristics from load demand analysis results involves data processing and analysis. By processing load demand data, key characteristic parameters of load demand can be extracted. The power gap change rate parameter reflects the changing trend of load demand over a unit of time and can be obtained through time series analysis of load demand data. The power gap duration parameter reflects the duration of the load demand gap and can be obtained through statistical analysis of consecutive time periods of the load demand gap.

[0049] Extracting these parameters facilitates a more nuanced assessment of load demand. For example, a higher power gap change rate parameter may indicate a rapid change in load demand, requiring the system to respond more quickly to meet it. The power gap duration parameter, on the other hand, helps the system determine the duration of the load demand gap, enabling longer-term scheduling and optimization.

[0050] Initially processed routing data contains detailed parameter information for each available route in the system. By parsing this routing data, the specific mode parameters for each route can be obtained. The primary and secondary routing mode parameters are key parameters for the two primary routes, including the route's power transmission capability, response time, and reliability.

[0051] The route parameter parsing process requires detailed analysis and processing of each route's basic data. For example, a route's power transmission capacity can be determined based on its design parameters and actual operating data, its response time can be determined based on the speed of its control system, and its reliability can be assessed based on its historical failure data and maintenance records.

[0052] By analyzing the routing parameters, more detailed data support can be provided for subsequent routing selection, ensuring that the system can make the best routing selection based on load requirements.

[0053] Demand urgency assessment is a key step in route selection. By comprehensively evaluating the power gap change rate and power gap duration parameters, we can determine the load demand urgency level. The demand urgency level reflects the urgency of the load demand and can be categorized into different levels, such as high, medium, and low.

[0054] A higher power gap change rate parameter and a shorter power gap duration parameter may indicate that the load demand is changing rapidly and urgently, requiring a quick system response. Based on these parameters, the urgency of the load demand can be assessed and the corresponding urgency level can be obtained.

[0055] After determining the load demand urgency level, a dual-route suitability assessment is performed on both the primary and secondary routing mode parameters. This assessment determines the adaptability and superiority of each route under the current load demand. The assessment includes aspects such as the route's response speed, power transmission capacity, and reliability.

[0056] By comparing the two routes, the optimal route selection result can be obtained. If the load demand is more urgent, the system may prioritize the route with faster response speed; if the load demand is more stable, the system may choose the route with stronger power transmission capacity and higher reliability.

[0057] This embodiment selects routes based on the load demand analysis results and the initial processing route data, and can flexibly select the optimal route for energy distribution according to the real-time demand of the load and the current state of the system, thereby achieving accurate matching and efficient response to load demand. Gap feature extraction of load demand to obtain power gap change rate parameters and power gap duration parameters helps to accurately assess the urgency of load demand, thereby ensuring that the system can respond quickly in emergency situations and meet the needs of high-priority loads. Routing parameter analysis and dual-route applicability evaluation provide a comprehensive evaluation basis for different routes, ensuring that the selected route is the most suitable under the current load demand, thereby improving the reliability and efficiency of energy distribution.

[0058] In one embodiment, based on the initial power distribution plan and the routing execution result, constructing energy scheduling optimization instructions for the bidirectional module includes: Decomposing the initial power distribution plan into its modules is the first step in building the overall energy dispatch solution. Its purpose is to break down the overall power distribution plan into specific operational instructions for each module. First, it's important to understand the initial power distribution plan. This plan is generated by the energy management system, which distributes power to the AC output interface, DC output interface, and time-division multiplexing bidirectional modules based on real-time data on utility power input, photovoltaic input, energy storage battery status, V2G device status, and photovoltaic access mode. This plan includes specific allocation ratios and routing for each energy source to ensure efficient system operation.

[0059] After the initial power distribution plan is generated, the system will decompose the overall energy distribution demand into various modules. The storage and charging module instructions are determined based on the charging and discharging requirements of the energy storage battery and the current battery status parameters, including charging requirements during off-peak electricity price periods and discharging requirements during peak electricity price periods. The photovoltaic charging module instructions are determined based on the adequacy of photovoltaic input and the grid return demand. If the photovoltaic input is sufficient, the excess electricity can be returned to the grid. The V2G module instructions involve the charging and discharging requirements of electric vehicles, especially during peak electricity price periods, when the electric energy of electric vehicles needs to be fed back to the grid through the V2G module.

[0060] By decomposing the initial power distribution plan into module instructions, specific operation instructions for each module are obtained. These instructions will guide each module to perform specific energy conversion and distribution operations to achieve the overall energy scheduling goal of the system.

[0061] Dynamically constraining the bidirectional module based on the charging and storage module instructions and the energy storage system's state parameters ensures that the module can effectively convert and distribute energy according to these instructions. The charging and storage module instructions, obtained in the previous step, contain the specific requirements for battery charging and discharging. The energy storage system's state parameters include the battery's current state—key parameters such as capacity, charging voltage, and current limit.

[0062] The bidirectional module's operating mode and setpoints are dynamically adjusted based on the storage and charging module's instructions and the energy storage system's status parameters. For example, during off-peak electricity price periods, if the storage and charging module instructs the battery to charge, the system checks parameters such as the current battery SOC and charging voltage to ensure that charging proceeds within a safe and efficient range. If the battery is nearing full charge, the system may adjust the charging current and reduce the charging power to preserve battery life.

[0063] Dynamic module constraints go beyond simple setpoint adjustments and include real-time monitoring and feedback mechanisms to ensure the stable operation of the storage and charging modules throughout their operation and their ability to respond to emergencies such as overcharge protection and excessive temperatures. Through these dynamic constraints, the system can determine the setpoints for the storage and charging modules, which in turn guide their specific charging and discharging operations, ensuring efficient and safe energy distribution.

[0064] Bidirectional modules are assigned based on the routing results and the photovoltaic module's instruction vector to ensure that the photovoltaic module can effectively convert and distribute power according to the instructions. The photovoltaic module's instruction vector, generated in the first step, contains the specific distribution requirements for photovoltaic power, including grid return requirements. The routing results are generated based on the real-time photovoltaic input status and the current system load.

[0065] Specific operational assignments are made to the solar charging module. For example, when the PV input is sufficient, the solar charging module may be instructed to return excess power to the grid. The system checks the current PV input power and grid demand to determine the specific operating mode and setpoints for the solar charging module. If grid demand is high, the system may increase the solar charging module's grid return power to meet grid demand.

[0066] The photovoltaic module's setpoints are more than just simple power settings; they also include various operational details, such as MPPT tracking parameters and voltage adjustments, to ensure stable operation and efficient power conversion and distribution. Through these distribution operations, the system can determine the photovoltaic module's setpoints, which guide the module's specific power distribution operations, ensuring efficient utilization of photovoltaic power and stable grid operation.

[0067] Bidirectional module scheduling is performed based on the V2G module's command vector and routing results to ensure the V2G module can effectively convert and distribute energy according to instructions. The V2G module's command vector, generated in the first step, captures the EV's charging and discharging requirements. This is especially true during peak electricity price periods, when the V2G module must feed EV energy back to the grid. Routing results are generated based on the real-time EV status and the system's current load.

[0068] The V2G module performs specific operational scheduling. For example, during peak electricity prices, the V2G module instructs the electric vehicle to feed power back to the grid. The system checks the current charging status of the electric vehicle and the grid's demand to determine the specific operating mode and setpoints for the V2G module. If grid demand is high but the electric vehicle has sufficient charge, the system may increase the V2G module's feedback power to meet the grid's demand.

[0069] The V2G flow setting is more than just a simple power setting; it also includes various operational details, such as charge and discharge current adjustment and communication protocol matching, to ensure stable operation of the V2G module and efficient energy conversion and distribution. Through these two-way scheduling operations, the system can obtain the V2G flow setting, which will guide the V2G module in specific energy distribution operations, ensuring efficient use of electric vehicle energy and stable operation of the power grid.

[0070] The purpose of constructing scheduling instructions for the storage module settings, the photovoltaic module settings, and the V2G flow direction settings is to aggregate the specific settings of each module to form the final energy scheduling optimization instructions. The storage module settings, the photovoltaic module settings, and the V2G flow direction settings have been obtained in the previous steps and represent the specific operating parameters of each module.

[0071] Comprehensive analysis and optimization of each module's setpoints ensures the efficiency and safety of the overall energy scheduling solution. For example, the system adjusts the power allocation ratio of each module based on real-time load conditions and grid demand to ensure that none of the modules conflict or overload. Furthermore, the system considers the synergy between modules, such as the coordinated operation of storage and charging modules and solar charging modules, to ensure balanced and stable energy distribution.

[0072] By constructing scheduling instructions for each module's setpoints, the system generates final energy scheduling optimization instructions. These optimization instructions guide each module in performing specific energy conversion and allocation operations, ensuring efficient system operation and energy utilization. Energy scheduling optimization instructions are more than just a simple summary of setpoints; they also include various operational details and protection mechanisms to ensure stable system operation and safe response.

[0073] This embodiment can effectively optimize energy distribution and improve energy utilization efficiency by decomposing and scheduling instructions for each module based on the initial power distribution plan. In this process, the instruction decomposition and dynamic constraints of the storage and charging module, the photovoltaic module and the V2G module ensure that each module can be flexibly scheduled under different electricity price periods and load demands. By adjusting the charging and discharging operations of the storage and charging module according to the real-time battery status and load demand, the system can achieve efficient energy storage and discharge while ensuring battery safety. The coordinated scheduling of the photovoltaic module and the V2G module enables the photovoltaic energy and the energy of electric vehicles to be contributed to the power grid to the greatest extent, improving the utilization rate of photovoltaic power and the stability of the power grid. Through this series of scheduling optimizations, the system can achieve accurate power distribution in different working modes, ensure load balance between the power grid and each module, and avoid energy waste and the risk of system overload.

[0074] In one embodiment, the AC output interface, DC output interface, and bidirectional module are collaboratively controlled and constructed according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution, including: In a multi-source energy system, the AC output interface must precisely adjust voltage and frequency according to grid requirements to ensure that the output power matches grid requirements. Energy dispatch optimization commands combine real-time energy input information (such as utility power, photovoltaics, and energy storage batteries) with grid demand data and use control algorithms to adjust the AC output voltage and frequency to maintain stability within the grid's specified range. This process not only ensures that the system output meets grid standards but also requires real-time monitoring of grid changes to ensure that the AC output can quickly respond to grid fluctuations. Dynamic coordinated regulation ensures that the AC voltage and frequency remain stable under varying load conditions, thereby improving system efficiency and reliability. Ultimately, the derived AC output parameters are fed back to the system control center for further optimization of power distribution.

[0075] Voltage regulation of the DC output interface is also a critical component of the energy management system. The DC bus voltage needs to be dynamically adjusted based on the system's real-time load demand and the power generation / charging status of the PV and energy storage batteries. Energy dispatch optimization commands determine the DC bus voltage adjustment based on the current system status (including PV input, energy storage battery SOC, and grid demand) to ensure that the DC load receives the required voltage. To adapt to changes in system load, the DC bus voltage can be flexibly adjusted between 200V and 750V. The DC output interface precisely controls the bus voltage based on battery charging, DC load, and grid return power requirements. This dynamic adjustment enables the system to maintain efficient operation under varying load conditions and minimize energy losses.

[0076] Bidirectional modules play a vital role in smart grids, enabling charging and discharging operations based on system needs. To optimize energy flow utilization and management, the modules' operating modes must be flexibly selected based on energy scheduling optimization instructions. Depending on the status of various energy sources in the system (such as utility power, photovoltaics, and energy storage batteries), the system commands the modules to switch between different modes, including charging, discharging, and DC load supply. This regulation ensures that each module operates optimally, enabling control of battery charging and discharging, and even enabling interaction with the grid when needed. The coordinated regulation of the bidirectional modules maximizes energy efficiency and system flexibility. Whenever load fluctuations or changes in energy input occur, the modules immediately adjust their operating modes based on optimization instructions, ensuring smooth system operation.

[0077] Power allocation within the bidirectional modules is a critical step in ensuring balanced energy flow. Energy scheduling optimization commands comprehensively consider the system's various energy input sources, load demand, and battery status to develop the optimal power allocation plan. Based on these commands, the bidirectional modules precisely allocate power to various routes based on different operating modes. For example, in charging mode, the bidirectional modules direct excess energy to the battery; in discharging mode, the modules supply stored battery energy to DC loads or the grid. In certain situations, when grid demand is high, the bidirectional modules prioritize returning energy to the grid. All of this power allocation processing must be performed in accordance with real-time energy scheduling optimization commands to ensure that each module and each energy route operates optimally. The final output of the power allocation status provides real-time feedback to various modules, including the grid, loads, and energy storage batteries, ensuring stable operation of the entire system.

[0078] Building a coordinated solution is a key step in the entire energy management process. This step requires integrating the output parameters, operating mode status, and power allocation status of each module to generate a comprehensive dynamic energy flow optimization solution. By analyzing the parameters of the AC output, DC output, and bidirectional modules, the system can determine the optimal energy scheduling plan. For example, during peak electricity price periods, battery energy may need to be discharged to the grid; during daytime hours when photovoltaic power generation is sufficient, the system prioritizes powering DC loads. All decisions are based on real-time power demand, the generation / charging status of photovoltaic and energy storage batteries, and grid scheduling requirements. Ultimately, the dynamic energy flow optimization solution integrates the operating status of all modules and coordinates the operation of various energy routes to ensure maximum system efficiency while also guaranteeing safety and reliability. Through this coordinated control, the system not only efficiently manages power but also adjusts its operating strategy in real time according to different operating conditions, ensuring efficient energy utilization and stable system operation.

[0079] This embodiment can achieve dynamic adjustment of the system under different load and energy input conditions by coordinating the AC output interface, DC output interface and bidirectional module according to the energy scheduling optimization instructions. This control method ensures efficient matching of the power grid and load demand, thereby improving the overall energy utilization efficiency of the system. At the same time, by adjusting the AC voltage frequency, DC bus voltage and the working mode of the bidirectional module in real time, the stable operation of the system can be guaranteed in different scenarios, the distribution and flow of electricity can be optimized, and the flexibility and response speed can be improved. Especially under complex load demands and multi-source energy conditions, it can be quickly adjusted according to real-time energy scheduling optimization instructions to ensure the maximum utilization of renewable energy such as photovoltaics and energy storage batteries, thereby reducing dependence on mains electricity and improving the autonomous utilization rate of energy.

[0080] In one embodiment, the bidirectional module is operated in a mode selected according to the energy scheduling optimization instruction to obtain an integrated operating mode state, including: Energy scheduling optimization commands extract system parameters from bidirectional modules. Bidirectional modules refer to DC-DC and AC-DC modules, which enable energy conversion and scheduling between different routes. These modules enable the system to collect real-time irradiance data from PV system sensors, detecting current solar irradiance levels and assessing PV power generation capacity. Electricity price period parameters are also extracted. These parameters, typically provided by the smart grid, include current electricity price information, helping the system schedule energy during peak and off-peak periods. Energy storage system capacity information, primarily battery capacity data, is provided by the battery management system and reflects the current capacity and health of the batteries. V2G scheduling command parameters include the grid's charging and discharging requirements for electric vehicle batteries. These commands are transmitted via the V2G channel. Vehicle battery capacity values ​​represent the current capacity of the electric vehicle's batteries. These values, provided by the onboard battery management system, help the system determine whether the vehicle can participate in V2G energy scheduling. By extracting these system parameters, energy scheduling optimization commands enable more precise scheduling and control of the bidirectional modules.

[0081] After acquiring real-time irradiance data, the system analyzes photovoltaic (PV) decisions. Irradiance data directly impacts the efficiency of PV power generation. By analyzing this data, the system determines the optimal operating mode for PV processing routing. DC-DC and AC-DC bidirectional modules play a key role in this process. The DC-DC bidirectional module adjusts the flow of PV energy, for example, feeding PV energy directly into the DC bus for battery charging or DC load powering. The AC-DC bidirectional module converts PV energy into AC power and returns it to the grid or supplies AC loads. In good sunlight conditions, the system may prioritize directing PV energy to the battery for charging via the DC-DC bidirectional module, ensuring efficient PV energy utilization. In poor sunlight conditions, the system may choose to convert PV energy into AC power via the AC-DC bidirectional module for return to the grid or supply AC loads. By analyzing irradiance data, the system dynamically adjusts the PV processing routing mode to ensure optimal PV energy utilization.

[0082] After obtaining electricity price period parameters and energy storage system capacity information, the system performs a storage and charging decision analysis. The electricity price period parameters determine the electricity price level for the current period. The storage and charging decision analysis aims to select the optimal energy storage strategy for different electricity price periods. AC-DC and DC-DC bidirectional modules play a key role in this process. The AC-DC bidirectional module converts mains power into DC power during off-peak periods and stores it in the battery. During peak periods, it converts the stored energy into AC power and returns it to the grid, reducing electricity costs during high-price periods. The DC-DC bidirectional module schedules energy during the charging and discharging of the energy storage battery, ensuring that the battery does not overcharge or over-discharge. The storage and charging module's operating mode adjusts based on the electricity price period parameters and energy storage system capacity information. During off-peak periods, the system prioritizes converting mains power into DC power and storing it in the battery. During peak periods, the system prioritizes releasing stored energy, reducing electricity costs during high-price periods. Through storage and charging decision analysis, the system optimizes the energy storage system's operating efficiency, reduces energy waste, and preserves battery life.

[0083] The V2G module's operating mode selection depends on the V2G dispatch command parameters and the vehicle's battery capacity. The V2G module here refers to a V2G-specific AC-DC bidirectional module that enables energy dispatch between electric vehicles and the power grid. When the power grid issues a V2G dispatch command, the system analyzes the electric vehicle's charging and discharging requirements based on the command parameters. The grid dispatch system transmits the dispatch command via the V2G channel, which includes the grid's charging and discharging requirements for the electric vehicle's battery and the vehicle's battery's current status. The vehicle's battery capacity, provided by the onboard battery management system, reflects the current capacity and health of the electric vehicle's battery. Based on this data, the system determines mode constraints to ensure a balance between the electric vehicle's energy output and battery protection. For example, when the vehicle's battery capacity is low, the system limits discharge to prevent damage to the electric vehicle's battery. When the battery capacity is sufficient, the vehicle can discharge to provide the required energy to the grid. By determining the mode constraints, the system dynamically adjusts the V2G module's operating mode to ensure effective interaction with the grid and the safety of the electric vehicle's battery.

[0084] After the operating modes of all modules are determined, the system encapsulates the operating modes of the PV processing and routing modules, the storage and charging modules, and the V2G modules into a single state. This encapsulation process integrates the operating modes of each module to generate a comprehensive operating mode state. The PV processing and routing mode is primarily determined by the DC-DC bidirectional module and the AC-DC bidirectional module. By analyzing real-time irradiance data, the system selects the operating mode that best suits the current lighting conditions. The storage and charging module mode is primarily determined by the AC-DC bidirectional module and the DC-DC bidirectional module. By analyzing electricity price period parameters and energy storage system capacity information, the system selects the optimal energy storage strategy. The V2G module mode is primarily determined by the V2G-dedicated AC-DC bidirectional module. By analyzing V2G scheduling command parameters and vehicle battery capacity, the system selects the optimal energy scheduling strategy. By encapsulating the operating modes of each module, the system generates a comprehensive operating mode state, ensuring that the operating modes of each module do not conflict with each other and that energy flow is efficiently distributed within the system. The comprehensive working mode state is the final description of the entire system operation strategy. Through this state, the system can make real-time optimization adjustments based on load demand, energy supply and grid scheduling to maintain efficient system operation and maximize energy utilization.

[0085] This embodiment extracts system parameters based on energy scheduling optimization instructions, and can obtain information on irradiance, electricity price period, energy storage system capacity, V2G scheduling instructions, and vehicle battery capacity in real time, ensuring that the system can perform optimal energy scheduling based on actual conditions at different time points. By performing photovoltaic decision analysis on the bidirectional module based on real-time irradiance data, the working mode of the photovoltaic processing route can be dynamically adjusted to maximize the utilization efficiency of photovoltaic energy, and the optimal route selection can be achieved whether it is directly supplying power, storing energy, or returning energy to the grid. Storage and charging decision analysis is performed based on electricity price period parameters and energy storage system capacity information, and energy is stored during off-peak electricity price periods and released during peak electricity price periods, thereby effectively reducing overall electricity costs and maximizing economic benefits. By judging the mode constraints of V2G scheduling instruction parameters and vehicle battery capacity values, it is ensured that the V2G module finds the best balance between grid demand and electric vehicle battery protection, which can both respond to grid demand and protect vehicle batteries and extend their service life.

[0086] In one embodiment, a photovoltaic decision analysis is performed on the bidirectional module based on real-time irradiance data to obtain a photovoltaic processing routing working mode, including: This step conducts in-depth analysis of the bidirectional module's internal functional structure, primarily aiming to identify the functional configuration capabilities of each subcomponent within the module, particularly the ACDC and DC-DC components. The ACDC component is the core object, and its current functional configuration status is obtained through its configuration interface or status register. The focus is on determining whether the component supports maximum power point tracking (MPPT), a key input for system energy allocation and scheduling. The extracted information is encapsulated as a conversion support flag, indicating whether the module possesses MPPT control capabilities. This process not only evaluates the device's hardware capabilities but also considers the impact of firmware versions, configuration files, and the current enabled mode on functionality. The generation of this flag serves as a precursor to subsequent routing and functional configuration. If the ACDC component itself is unable to perform MPPT, the system's PV scheduling must shift to the DC-DC components. The conversion support flag, a Boolean or enumeration type, records the module's ability to adapt to PV energy and serves as a prerequisite for the entire energy scheduling process.

[0087] The system acquires current irradiance data from a light sensor connected to the system and compares it in real time with a preset irradiance threshold (e.g., 150W / m²). This generates an irradiance status indicator reflecting the current solar input status. This status indicator is categorized using a hierarchical approach, typically including "high irradiance" and "low irradiance." When the irradiance exceeds the threshold, the status indicator is set to "high irradiance," indicating that the PV array output capacity is good and suitable for MPPT optimization. When it falls below the threshold, the system sets the status indicator to "low irradiance," indicating that the system is about to enter a phase of partial compensation or utility power supply. This process, along with the transition support indicator, forms the dual-variable basis for mode selection. The result not only influences the current power allocation routing but also influences the priority of MPPT control strategy activation. The stability of the irradiance status indicator directly affects the accuracy of PV routing configuration. Therefore, raw data smoothing is typically performed using methods such as sliding average and denoising filtering to ensure the stability of the decision logic.

[0088] The core of dominant mode configuration lies in building a set of rule-based mapping logic that combines the irradiation status flag and conversion support flag output from the first two steps. Based on this result, the DC-DC and ACDC components in the bidirectional module are configured for specific modes. If the irradiation status is high and the ACDC module supports MPPT, the system enters collaborative MPPT mode. The DC-DC components retain basic MPPT functionality, while the ACDC components implement a dynamic compensation MPPT strategy to address rapid irradiation changes or load disturbances. In this mode, the PV array can operate as close to the MPP as possible, improving overall energy efficiency. If the irradiation status is high but the ACDC components do not support MPPT, the system implements single MPPT dominant mode. The DC-DC components assume all MPPT responsibilities, while the ACDC components switch to a pure inverter mode, performing only power conversion without intervening in the control strategy. If the irradiation status is low, the system activates forced compensation mode. In this mode, the DC-DC components maintain basic MPPT functionality, while the ACDC components initiate a compensation MPPT mechanism. The voltage reference point and power tracking target are dynamically adjusted based on the remaining PV capacity, approaching the energy harvesting limit under low irradiation conditions. The final configuration result is encapsulated as the configuration processing result, which serves as the key basis for further pattern recognition and routing execution of the system.

[0089] The completed module configuration is packaged, integrating and encoding parameters such as the currently enabled functional mode, real-time irradiance, and the MPPT capability status of the AC-DC modules into a unified operating mode identifier. This identifier is a composite data structure, including but not limited to the mode type (e.g., coordinated MPPT, single MPPT dominant, or forced compensation), the current irradiance value (unit: W / m²), the AC-DC MPPT capability status, and the DC-DC configuration status code. This identifier is used in the real-time scheduling interface and serves as a key record for operation logs and energy strategy evaluation. The packaging process utilizes data templates and parameter mapping to ensure a standardized and scalable data structure suitable for various energy management systems. This operating mode identifier serves as a decision output in the control loop of the system's EMS, guiding subsequent energy routing scheduling, time-sharing strategy execution, and safety protection mechanism matching. This completes a closed-loop control logic from environmental status perception to functional module configuration and operating mode expression, establishing a complete link between photovoltaic processing routing decision-making and application.

[0090] This embodiment can accurately identify the MPPT capability of the ACDC component by extracting the characteristics of the functional configuration information of the bidirectional module, thereby making dynamic adjustments based on the real-time irradiance data, thereby achieving efficient utilization of photovoltaic energy. The real-time irradiance data is compared based on a preset irradiation intensity threshold to generate an irradiation status identifier to ensure that the system can make the optimal configuration under different lighting conditions. Based on the irradiation status identifier and the conversion support identifier, the dominant mode of the bidirectional module is configured, so that in high irradiation conditions, the collaborative MPPT or single MPPT dominant mode can be flexibly selected to ensure the energy conversion efficiency and stability of the system under various irradiation conditions. In low irradiation conditions, the system is configured through a forced compensation mode, which effectively improves the energy collection capability under low light conditions. Finally, by encapsulating the configuration processing results into a working mode, a unified photovoltaic processing routing working mode is generated, which facilitates real-time scheduling of the system and historical record analysis.

[0091] Reference Figure 3 As shown, the present invention further provides a multi-source energy and multi-mode operation management system for a smart grid, which is applied to any of the multi-source energy and multi-mode operation management methods for a smart grid described above, comprising: A collection module is used to obtain mains energy data, photovoltaic energy data and energy storage system status parameters, perform preliminary power distribution on the AC output interface and the bidirectional module, and obtain an initial power distribution plan; An analysis module, configured to obtain DC load demand data through a DC output interface, and dynamically select a route based on the energy storage system state parameters and the initial power distribution plan to obtain a route execution result; an association module, configured to construct an energy scheduling optimization instruction for the bidirectional module based on the initial electric energy distribution plan and the routing execution result; A processing module is provided, wherein the processing module coordinates and controls the AC output interface, the DC output interface and the bidirectional module according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution.

[0092] The present invention provides a multi-source energy and multi-mode operation management system for a smart grid. By acquiring multi-source energy data such as mains electricity, photovoltaics, and energy storage systems, and combining it with real-time monitoring of the status of the energy storage system, it can achieve coordinated scheduling between various energy sources, effectively solving the problems of dispersed and inefficient energy utilization in traditional systems. By dynamically acquiring AC and DC load demands and optimizing energy scheduling based on routing execution results, the system can perform intelligent dynamic regulation when faced with complex and highly volatile load demands, thereby enhancing the operational flexibility and stability of the system. Energy scheduling optimization instructions are generated based on the routing selection results and the initial power distribution plan, and key modules of the system are coordinated and regulated, further improving the efficiency and accuracy of energy flow and reducing energy waste and unnecessary energy consumption.

[0093] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-source energy and multi-mode operation management method for a smart grid, characterized in that: include: Obtaining utility power data, photovoltaic energy data, and energy storage system status parameters, performing preliminary power distribution on the AC output interface and bidirectional modules, and obtaining an initial power distribution plan; Acquiring DC load demand data through a DC output interface, and dynamically selecting a route based on the energy storage system state parameters and the initial power distribution plan to obtain a route execution result; constructing energy scheduling optimization instructions for the bidirectional module based on the initial power distribution plan and the routing execution result; The AC output interface, the DC output interface and the bidirectional module are coordinated and regulated according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution.

2. The multi-source energy and multi-mode operation management method of the smart grid according to claim 1, characterized in that: The acquisition of mains energy data, photovoltaic energy data, and energy storage system status parameters, performing preliminary power distribution on the AC output interface and the bidirectional module, and obtaining an initial power distribution plan includes: Performing power sampling on the mains energy data input from the power grid to obtain a mains power value; Performing maximum power point tracking on the photovoltaic energy data input by the photovoltaic circuit to obtain a photovoltaic available power value; Acquiring the energy storage system state parameters of the energy storage system through the bidirectional module, and performing power analysis to obtain battery charge and discharge power values; Calculating a total available power value of the system according to the mains power value, the photovoltaic available power value, and the battery charging and discharging power value; Performing AC load identification based on the AC output interface to obtain AC load power demand; The power gap calculation and electric energy distribution are performed on the total available power value of the system and the power demand of the AC load according to a preset safety management mechanism to obtain the initial electric energy distribution plan.

3. The multi-source energy and multi-mode operation management method of the smart grid according to claim 1, characterized in that: The DC load demand data is obtained through the DC output interface, and a route is dynamically selected in combination with the energy storage system state parameters and the initial power distribution plan to obtain a route execution result, including: Performing real-time load demand identification on the DC output interface to obtain DC load demand data; Performing processing route identification according to the energy storage system state parameters and the initial electric energy distribution plan to obtain initial processing route data; performing load analysis on the DC load demand data according to the energy storage system state parameters and the initial power distribution plan to obtain a load demand analysis result; Performing route selection according to the load demand analysis result and the initial processing route data to obtain a route selection result; According to the routing selection result, routing is performed on the DC output interface and the AC output interface respectively to obtain the routing execution result.

4. The multi-source energy and multi-mode operation management method of the smart grid according to claim 3, characterized in that: The performing route selection according to the load demand analysis result and the initial processing route data to obtain a route selection result includes: Performing gap feature extraction on the load demand analysis result to obtain a power gap change rate parameter and a power gap duration parameter; performing routing parameter parsing on the initially processed routing data to obtain a first routing mode parameter and a second routing mode parameter; Performing a demand urgency assessment based on the power gap change rate parameter and the power gap duration parameter to obtain a load demand urgency level; Dual routing applicability evaluations are performed on the first routing mode parameters and the second routing mode parameters respectively according to the load demand urgency level, and routing comparisons are performed to obtain the routing selection result.

5. The multi-source energy and multi-mode operation management method of the smart grid according to claim 1, characterized in that: The step of constructing the energy scheduling optimization instruction of the bidirectional module based on the initial electric energy distribution plan and the routing execution result includes: Decomposing the initial electric energy distribution plan into module instructions to obtain storage and charging module instructions, optical charging module instructions, and V2G module instructions; Performing module dynamic constraints on the bidirectional module according to the storage and charging module instructions and the energy storage system state parameters to obtain storage and charging module setting values; Allocate the bidirectional module according to the routing execution result and the optical charging module instruction vector to obtain an optical charging module setting value; Performing bidirectional scheduling on the bidirectional module according to the V2G module instruction vector and the routing execution result to obtain a V2G flow direction setting value; A scheduling instruction is constructed for the storage and charging module setting value, the optical charging module setting value and the V2G flow direction setting value to obtain the energy scheduling optimization instruction.

6. The multi-source energy and multi-mode operation management method of the smart grid according to claim 1, characterized in that: The collaborative control and construction of the AC output interface, the DC output interface, and the bidirectional module according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution includes: performing AC voltage and frequency coordinated adjustment on the AC output interface according to the energy scheduling optimization instruction to obtain AC output parameters; Performing a DC bus voltage dynamic adjustment process on the DC output interface according to the energy scheduling optimization instruction to obtain a DC output parameter; Selecting a working mode for the bidirectional module according to the energy scheduling optimization instruction to obtain an integrated working mode state; Performing power allocation processing on the bidirectional module according to the energy scheduling optimization instruction to obtain a module power allocation state; A collaborative scheme is constructed for the AC output parameters, the DC output parameters, the integrated working mode state and the module power distribution state to obtain a dynamic energy flow optimization scheme.

7. The multi-source energy and multi-mode operation management method of the smart grid according to claim 6, characterized in that: The selecting the working mode of the bidirectional module according to the energy scheduling optimization instruction to obtain an integrated working mode state includes: According to the energy scheduling optimization instruction, the system parameters are extracted through the bidirectional module to obtain real-time irradiance data, electricity price period parameters, energy storage system capacity information, V2G scheduling instruction parameters and vehicle battery capacity value; Performing photovoltaic decision analysis on the bidirectional module according to the real-time irradiance data to obtain a photovoltaic processing routing working mode; Performing storage and charging decision analysis on the bidirectional module according to the electricity price period parameters and the energy storage system capacity information to obtain a storage and charging module working mode; Performing mode constraint judgment on the bidirectional module according to the V2G scheduling instruction parameter and the vehicle battery capacity value to obtain a V2G module operating mode; Mode state encapsulation processing is performed on the photovoltaic processing routing working mode, the storage and charging module working mode, and the V2G module working mode to obtain the integrated working mode state.

8. The multi-source energy and multi-mode operation management method of the smart grid according to claim 7, characterized in that: The performing photovoltaic decision analysis on the bidirectional module according to the real-time irradiance data to obtain a photovoltaic processing routing working mode includes: Extracting features from the functional configuration information of the bidirectional module to obtain a conversion support identifier; Performing a threshold comparison on the real-time irradiance data according to a preset irradiance intensity threshold to obtain an irradiation status indicator; Performing dominant mode configuration on the bidirectional module based on the irradiation state identifier and the conversion support identifier to obtain a configuration processing result; The configuration processing result is encapsulated into a working mode to obtain the photovoltaic processing routing working mode.

9. A multi-source energy and multi-mode operation management system for a smart grid, characterized in that: The multi-source energy and multi-mode operation management method for a smart grid as described in any one of claims 1 to 8 above comprises: A collection module is used to obtain mains energy data, photovoltaic energy data and energy storage system status parameters, perform preliminary power distribution on the AC output interface and the bidirectional module, and obtain an initial power distribution plan; An analysis module, configured to obtain DC load demand data through a DC output interface, and dynamically select a route based on the energy storage system state parameters and the initial power distribution plan to obtain a route execution result; an association module, configured to construct an energy scheduling optimization instruction for the bidirectional module based on the initial electric energy distribution plan and the routing execution result; A processing module is provided, wherein the processing module coordinates and controls the AC output interface, the DC output interface and the bidirectional module according to the energy scheduling optimization instruction to obtain a dynamic energy flow optimization solution.

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