Low-voltage distributed multi-energy complementary and coordinated green power consumption conversion system and optimization method

Abstract

The invention discloses a low-voltage distributed multi-energy complementary and coordinated green power consumption and conversion system and optimization method, and the method comprises the steps: data collection and state monitoring: collecting the output data of a distributed energy unit in real time through a communication module of an intelligent control unit, and improving the power supply reliability to 99.99%; a plug-and-play interface supports flexible access of newly-added distributed energy or loads, system architecture does not need to be reconstructed, and the system adapts to multiple scenes such as villages, industrial parks and micro-grids. The intelligent control unit is compatible with multi-protocol communication of DL698, Modbus and the like and can be seamlessly connected with an existing power grid dispatching system, landing application of the technology is accelerated, the consumption capacity, the operation efficiency and the reliability of low-voltage distributed green power are comprehensively improved through innovative integration of multi-energy complementary configuration, multi-form energy storage fusion, intelligent cooperative regulation and control and a gradient utilization technology, and the system is suitable for large-scale popularization and application. And a key technical support is provided for low-carbon transformation of a novel power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, in particular to a low-voltage distributed multi-energy complementary coordinated green power consumption conversion system and optimization method. BACKGROUND

[0002] With the transformation of global energy structure to low carbonization, the penetration rate of distributed renewable energy (such as photovoltaic, wind power, small hydropower) continues to rise, becoming the core support of green development of energy system. However, distributed energy has strong volatility and intermittent characteristics, and its large-scale grid connection brings serious challenges to the safe and stable operation of low-voltage distribution network: on the one hand, photovoltaic output is affected by light intensity and temperature, and wind power output is affected by wind speed, resulting in frequent "abandonment of light and wind", and the utilization rate of renewable energy is generally low; on the other hand, the traditional low-voltage distribution network is mainly based on alternating current architecture, and the access of distributed energy, energy storage devices and multi-element load lacks unified coordination mechanism, which easily causes voltage out-of-limit, frequency fluctuation and other problems, and even leads to local power grid collapse in serious cases. In the prior art, the consumption of distributed energy mainly depends on the battery energy storage system, but the traditional lithium battery energy storage has the defects of limited capacity, short service life and high cost, which is difficult to meet the long-time energy storage demand; as a clean and efficient secondary energy, hydrogen energy, although its electrolytic hydrogen production and fuel cell technology are gradually mature, but in the existing system, the hydrogen energy equipment and the power system lack deep coupling, and the synergistic mechanism of "electricity-hydrogen-electricity" closed loop conversion has not been formed, and the waste heat recovery rate is less than 30%. In terms of control, most distributed energy systems adopt centralized scheduling mode, relying on cloud platform to calculate and decide, which has problems of data transmission delay and local response lag, and cannot adapt to the regulation and control demand of millisecond power fluctuation; at the same time, as the core equipment of energy interconnection, the existing products of energy router mostly adopt fixed parameter configuration, lack of plug-and-play function, and the equipment access needs manual debugging, resulting in poor system expansibility and low deployment efficiency. In addition, the power consumption characteristics of multi-element load (such as residential electricity, electric vehicle charging, industrial and commercial load) in low-voltage distribution network are significantly different, and the traditional load management only relies on simple on-off control, without establishing a dynamic regulation and control mechanism based on priority, so that the power supply reliability of key load is difficult to guarantee. The existing optimization algorithm mostly focuses on a single target (such as economy), without considering the multi-dimensional demand of renewable energy utilization rate and grid fluctuation suppression, resulting in poor comprehensive benefit of system operation. Therefore, developing a low-voltage distributed energy system with multi-energy complementary coordination ability, efficient consumption and conversion of green power, and intelligent optimization and scheduling, has become the key to solving the current technical bottleneck. SUMMARY

[0003] In order to overcome the shortcomings of the prior art, one of the purposes of the present application is to provide a low-voltage distributed multi-energy complementary coordinated green power consumption conversion system and optimization method.

[0004] One of the purposes of the present application is achieved by adopting the following technical solutions: A low-voltage distributed multi-energy complementary coordinated green power consumption conversion system, comprising: Distributed energy unit: containing at least two of photovoltaic array, wind turbine, small hydropower device, connected with the system through DC bus or AC bus, for converting renewable energy into electric energy; Energy storage device: including at least one of lithium battery energy storage system, flow battery energy storage system, solid oxide electrolysis hydrogen production / fuel cell system, the solid oxide electrolysis hydrogen production / fuel cell system can automatically switch electrolysis hydrogen production mode and fuel cell power generation mode, realizing efficient conversion of electricity-hydrogen-electricity and multi-time scale energy storage; Intelligent control unit: integrating multi-objective optimization algorithm module, edge computing module and communication module, the communication module supports multiple communication protocols such as DL698, Modbus and Ethernet, for collecting real-time data of distributed energy unit, energy storage device and load, and realizing local rapid decision through edge computing module; Energy router: adopting plug and play design, containing main control module, communication module and interface module, the interface module provides DC power connection port, AC power connection port and data interface, supporting plug and play access of distributed energy unit, energy storage device and load; the main control module realizes power distribution and conversion of different energy forms through multi-port converter, and internally embeds intelligent identification chip to automatically identify the type and parameters of the access equipment; Load management module: containing intelligent circuit breaker, demand side response controller and load classification unit, the demand side response controller dynamically adjusts the power utilization strategy of adjustable load (such as electric vehicle charging pile, energy storage air conditioner) according to price signal or system power balance state, and the load classification unit divides the load into priority load and flexible load to ensure stable power supply for critical load; Low-voltage DC power network: adopting chain or ring network topology, connected with medium-voltage DC power network through DC transformer, realizing local consumption of distributed energy and decoupling of multi-energy flow; the bus voltage of the low-voltage DC power network is 380V or 220V, supporting flexible networking of distributed energy unit, energy storage device and load.

[0005] A green power consumption conversion optimization method, comprising the following steps: Data acquisition and state monitoring: real-time acquisition of output data of distributed energy unit, state of charge (SOC) of energy storage device, power utilization data of load and price signal of power grid through communication module of intelligent control unit, and Fourier transform analysis of current and voltage frequency domain signals of photovoltaic inverter grid-connected point to extract transformer harmonic characteristics for identifying island operation state; Multi-energy complementary model construction: a multi-energy complementary system mathematical model is established, which includes a distributed energy output prediction model, an energy storage charging and discharging model, a load demand model, and a power grid interaction model. The distributed energy output prediction adopts four-dimensional prediction technology (point prediction, interval prediction, probability prediction, and scenario prediction), and the prediction uncertainty is handled through a distributed robust optimization method; Optimal scheduling strategy generation: a multi-objective optimization algorithm (particle swarm optimization, genetic algorithm, or generalized eigenvector algorithm) is used to solve the mathematical model, generating a scheduling strategy with the goals of maximizing renewable energy utilization, minimizing operating costs, and minimizing power grid fluctuations. The scheduling strategy includes distributed energy output instructions, energy storage charging and discharging power instructions, and load regulation instructions; Coordination control and dynamic adjustment: the scheduling strategy is executed through the multi-port converter of the energy router, achieving power coordination control of distributed energy and energy storage devices. When the detected power grid frequency fluctuation exceeds the preset threshold, virtual inertia control and virtual damping control of the energy storage device are triggered to suppress frequency oscillation and reduce energy storage demand; Green power transformation and cascade utilization: when the distributed energy output is excessive, the excess power is converted into hydrogen energy storage through a solid oxide electrolysis hydrogen / fuel cell system, and the cascade utilization of waste heat in the electrolysis process is achieved through a heat integration subsystem to drive an absorption chiller or meet user heating needs.

[0006] Further, the topology of the low-voltage direct-current power network is a ring network, with direct-current tie switches configured to achieve multi-path power supply and improve system reliability.

[0007] Further, the main control module of the energy router is embedded with an artificial intelligence algorithm, which optimizes the model through historical data training and dynamically adjusts the energy distribution strategy to adapt to load changes and environmental conditions.

[0008] Further, the solid oxide electrolysis hydrogen / fuel cell system in the energy storage device, in the electrolysis hydrogen mode, evaporates and overheats the water in the water tank to the working temperature, then passes it into the fuel electrode, and at the same time heats the air to the working temperature and passes it into the air electrode. The generated hydrogen gas is stored in the hydrogen storage tank after heat exchange and cooling.

[0009] Further, the multi-objective optimization algorithm uses Pareto frontier solving technology to generate a set of non-inferior solutions for users or grid dispatching agencies to choose from, achieving a balance between economy and environmental protection.

[0010] Further, in the green power transformation process, the unreacted hydrogen gas and excess air generated by the fuel cell power generation are burned through the tail burner, and the high-temperature mixed gas generated is preheated through the heat integration subsystem to preheat the air / hydrogen or to supply user hot water needs.

[0011] Further, the edge computing module of the intelligent control unit communicates with the cloud energy management platform in both directions, receives power grid dispatching instructions and uploads system operation data, participates in power grid peak shaving and demand side response auxiliary services.

[0012] Further, the demand side response controller of the load management module supplies power to flexible loads such as electric vehicle charging piles and energy storage air conditioners during the low valley period of the power grid according to the time-of-use electricity price signal, and reduces the power consumption during the peak period.

[0013] Further, the interface module of the energy router integrates an intelligent identification chip, contains the type, parameters and state information of the access device, and realizes automatic identification and parameter configuration of the device.

[0014] Compared with the prior art, the present application has the following advantages: 1. The system effectively suppresses the volatility of a single energy source through the multi-energy complementary configuration of the distributed energy unit (combination of at least two of photovoltaic, wind power, and small hydropower), combined with four-dimensional prediction technology (point prediction, interval prediction, probability prediction, and scenario prediction) and distributed robust optimization method. When the output of renewable energy is excessive, the "electricity-hydrogen" conversion storage is realized through the solid oxide electrolysis hydrogen production / fuel cell system, which improves the energy density of traditional battery storage by 3-5 times, significantly improves the utilization rate of renewable energy, and reduces the measured light and wind rejection rate to below 5%. 2. The energy storage device innovatively integrates lithium batteries, flow batteries, and solid oxide electrolysis hydrogen production / fuel cell systems to realize the collaborative operation of multi-form energy storage of "electricity-chemical energy-hydrogen energy". The solid oxide system supports automatic switching between electrolytic hydrogen production and fuel cell power generation modes, and hydrogen energy can be used for emergency power generation. The waste heat in the production process is used through a heat integration subsystem for cascade utilization to drive absorption refrigeration or meet heating demand, and the overall energy efficiency is improved to more than 75%, which is 20-30% higher than that of traditional energy storage systems. 3. The intelligent control unit adopts a two-way communication architecture of edge computing and cloud platform, and the local decision response time is shortened to within 50 milliseconds, solving the delay problem of traditional centralized scheduling. The energy router realizes automatic access and parameter configuration of the device through plug-and-play design and intelligent identification chips, and the deployment efficiency is improved by 40%. The multi-port converter cooperates with the virtual inertia / damping control strategy to quickly suppress oscillation when the power grid frequency fluctuation exceeds the threshold, and the frequency regulation response speed is improved to 2 times that of the traditional scheme, ensuring continuous power supply for critical loads. 4. The multi-objective optimization algorithm, through Pareto front solving technology, comprehensively addresses the objectives of maximizing renewable energy utilization, minimizing operating costs, and minimizing grid fluctuations, generating a dynamic scheduling strategy. Combined with the demand-side response mechanism of the load management module (such as supplying power to flexible loads during off-peak hours), system operating costs are reduced by 15-20%. Simultaneously, hydrogen energy conversion and cascade utilization reduce reliance on purchased electricity, potentially reducing annual carbon emissions by 300-500 tons / MW, achieving both economic and environmental benefits. 5. The low-voltage DC power network adopts a ring topology and DC interconnection switch to achieve multi-path power supply, improving power supply reliability to 99.99%. Plug-and-play interfaces support flexible access for new distributed energy sources or loads without requiring system architecture reconstruction, adapting to diverse scenarios such as rural areas, industrial parks, and microgrids. The intelligent control unit is compatible with multiple communication protocols such as DL698 and Modbus, enabling seamless integration with existing power grid dispatching systems and accelerating technology application. In summary, this invention comprehensively improves the absorption capacity, operational efficiency, and reliability of low-voltage distributed green power through innovative integration of multi-energy complementary configuration, multi-form energy storage fusion, intelligent collaborative regulation, and cascade utilization technologies, providing key technical support for the low-carbon transformation of new power systems.

[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0016] Figure 1 This is a flowchart of the optimization method for the low-voltage distributed multi-energy complementary green power consumption and conversion system of the present invention. Detailed Implementation

[0017] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0018] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. DETAILED DESCRIPTION

[0020] 1. System hardware composition Distributed energy unit: 200kW photovoltaic array (monocrystalline silicon module, conversion efficiency 23%) + 150kW wind turbine (horizontal axis permanent magnet type, cut-in wind speed 3m / s), connected to 400V AC bus through DC / AC inverter, and then converted to 380V DC through DC transformer and connected to low-voltage DC network. Energy storage device: 100kWh lithium battery energy storage system (lithium iron phosphate, cycle life 8000 times) + 50kW solid oxide electrolysis hydrogen / fuel cell system (working temperature 850°C, electrolysis efficiency 72%), with 100Nm3 hydrogen storage tank and heat integration subsystem (including plate heat exchanger, absorption chiller). Intelligent control unit: edge computing gateway (computing power 2TOPS) is used, integrated with LSTM prediction model and NSGA-Ⅱ multi-objective optimization algorithm, communication module supports DL698.45 and ModbusTCP protocols, data sampling frequency 10Hz. Energy router: 4-port modular design (2-way DC 380V, 2-way AC 220V), built-in STM32H743 master chip and intelligent identification module, supporting automatic parameter configuration within 10 seconds after device access. Load management module: access three types of loads (priority: precision equipment load 50kW; flexible load: 10 electric vehicle charging piles 60kW, 2 energy storage air conditioners 30kW), demand side response controller response time ≤200ms. Low-voltage DC network: ring network topology is used, with 3 DC tie switches (break time 5ms), copper core cable (current carrying capacity 250A) is used for lines, and bus voltage fluctuation is controlled within ±5%.

[0021] 2. System operation process (1) Distributed energy output prediction: 96-point output curve of photovoltaic / wind power the next day (time resolution 15 minutes) is generated through four-dimensional prediction model at 0 o'clock every day, with prediction error ≤8%; (2) Multi-objective optimization scheduling: intelligent control unit updates scheduling strategy every day, at noon 12-14 photovoltaic output peak period, excess power (about 80kW) is distributed to solid oxide system for electrolytic hydrogen production after meeting the load demand. (3) Hydrogen energy conversion and cascade utilization: The 80°C residual heat generated by the electrolysis process heats domestic hot water (heat supply 15 kW) through a heat exchanger. In winter, when the heating mode is switched, the residual heat utilization rate increases to 60%; (4) Emergency response: When the grid frequency drops to 49.5 Hz, the lithium battery energy storage releases 20 kW of active power within 100 ms, and the fuel cell system starts the standby power generation mode, reaching 80% of the rated output within 3 seconds; (5) Load regulation: During the peak electricity price period (18-22), the demand-side response controller reduces the electric vehicle charging pile power from 60 kW to 30 kW, and the energy storage air conditioner switches to the energy storage release mode, reducing grid electricity purchase by 30 kWh / day.

[0022] Experiment 1: Renewable energy consumption capacity test Test purpose: To verify the system's effect on photovoltaic / wind power fluctuation suppression and the improvement of energy rejection rate Experimental scheme: Select typical weather data for 7 consecutive days (sunny days, cloudy days, and gusty weather), and compare the operation data of the traditional system without energy storage and the system of the present application Test equipment: Power analyzer (accuracy 0.2 level), weather station (sampling frequency 1 Hz), data logger Key data: Experimental conclusion: Through multi-energy complementation and hydrogen energy conversion coordination, the system significantly improves the renewable energy consumption capacity, and the voltage stability meets the requirements of GB / T12325-2020 standard.

[0023] Experiment 2: Energy storage system efficiency test Test purpose: To evaluate the "electricity-hydrogen-electricity" conversion efficiency and residual heat utilization effect Experimental scheme: Start electrolytic hydrogen production during the photovoltaic output surplus period (10:00-15:00), and after hydrogen storage, generate electricity through fuel cells during the peak load period (18:00-20:00), and simultaneously monitor energy flow and heat flow data Test equipment: Hydrogen flowmeter (accuracy 1.0 level), calorimeter, heat flowmeter Key data: Link Efficiency / Utilization Traditional lithium battery energy storage Hydrogen energy storage of the present invention Electrolytic hydrogen production efficiency 72% - - Fuel cell power generation efficiency 60% - - Electricity-hydrogen-electricity cycle efficiency 43.2% - - Waste heat recovery utilization rate 65% - - Comprehensive energy utilization efficiency 75.4% 85% (electricity-electricity) 75.4% (multi-energy) Long-term energy storage cost (yuan / kWh·year) - 0.35 0.28 Experimental conclusion: The hydrogen energy storage system has a lower electricity-electricity cycle efficiency than lithium batteries, but the long-term energy storage cost is reduced by 20%, and through residual heat recovery, multi-energy utilization is achieved, with better overall benefits.

[0024] Experimental conclusion: The hydrogen energy storage system has a lower electricity-electricity cycle efficiency than lithium batteries, but the long-term energy storage cost is reduced by 20%, and through residual heat recovery, multi-energy utilization is achieved, with better overall benefits. Experiment 3: Coordination control response test Test purpose: verify the real-time regulation performance of edge computing and energy router Experimental scheme: simulate the scenarios of grid frequency sudden drop (49.8 Hz→49.0 Hz within 0.5 seconds) and load sudden increase (50 kW→100 kW), and record the system response time and regulation effect Test equipment: oscilloscope (sampling rate 1 MHz), frequency generator, programmable load Key data: Experimental conclusion: edge computing and plug-and-play technology improve system response speed by more than 70%, significantly reduce communication load, and meet the demand for millisecond-level regulation.

[0025] Experiment four: verification of optimization algorithm effect Test purpose: evaluate the balance ability of multi-objective optimization algorithm in economy and environmental protection Experimental scheme: compare the monthly operation data of single economy optimization and multi-objective optimization of the present application Test equipment: energy management platform, electric / gas meter (accuracy 0.5 level) Key data: Experimental conclusion: the multi-objective optimization algorithm reduces the operation cost while significantly improving the proportion of green power utilization and reducing the pressure on grid peak regulation.

[0026] This embodiment verifies the technical advantages of the system in distributed energy consumption, multi-energy complementary coordination, intelligent optimization scheduling, etc. through 120 days of continuous operation test. The experimental data shows that the renewable energy utilization rate of the system is stable at more than 90%, the comprehensive energy efficiency is improved to 75%, the operation cost is reduced by 12-15%, and all performance indicators are better than existing technical solutions.

[0027] The above embodiments are only preferred embodiments of the present application, and cannot be used to limit the scope of protection of the present application. Any non-essential changes and substitutions made by those skilled in the art based on the present application are within the scope of the present application.

Claims

1. A low-voltage distributed multi-energy complementary and coordinated green power consumption and conversion system, characterized in that, include: Distributed energy unit: includes at least two of the following: photovoltaic array, wind turbine, and small hydropower device, connected to the system via DC bus or AC bus, used to convert renewable energy into electrical energy; Energy storage device: including at least one of lithium battery energy storage system, flow battery energy storage system, and solid oxide electrolysis hydrogen production / fuel cell system, wherein the solid oxide electrolysis hydrogen production / fuel cell system can automatically switch between electrolysis hydrogen production mode and fuel cell power generation mode to achieve efficient conversion of electricity to hydrogen to electricity and energy storage on multiple time scales; Intelligent control unit: integrates a multi-objective optimization algorithm module, an edge computing module, and a communication module. The communication module supports multiple communication protocols such as DL698, Modbus, and Ethernet. It is used to collect real-time data from distributed energy units, energy storage devices, and loads, and to achieve local rapid decision-making through the edge computing module. Energy Router: Adopting a plug-and-play design, it includes a main control module, a communication module, and an interface module. The interface module provides DC power connection ports, AC power connection ports, and a data interface, supporting plug-and-play access of distributed energy units, energy storage devices, and loads. The main control module realizes power distribution and conversion of different energy forms through a multi-port converter and has a built-in intelligent identification chip to automatically identify the type and parameters of the connected devices. Load management module: includes intelligent circuit breaker, demand-side response controller and load classification unit. The demand-side response controller dynamically adjusts the power consumption strategy of adjustable loads (such as electric vehicle charging piles and energy storage air conditioners) according to the electricity price signal or the system power balance status. The load classification unit divides the load into priority loads and flexible loads to ensure stable power supply to critical loads. Low-voltage DC power network: It adopts a chain or ring network topology and is connected to the medium-voltage DC power network through DC transformers to realize the local consumption of distributed energy and the decoupling of multiple energy flows; the bus voltage of the low-voltage DC power network is 380V or 220V, which supports flexible networking of distributed energy units, energy storage devices and loads.

2. A method for optimizing the green power consumption and conversion based on the system described in claim 1, characterized in that, Includes the following steps: Data acquisition and status monitoring: The output data of the distributed energy unit, the state of charge (SOC) of the energy storage device, the power consumption data of the load and the electricity price signal of the grid are collected in real time through the communication module of the intelligent control unit. The current and voltage frequency domain signals of the photovoltaic inverter grid connection point are analyzed by Fourier transform, and the transformer harmonic characteristics are extracted to identify the islanded operation status. Multi-energy complementary model construction: Establish a mathematical model of a multi-energy complementary system that includes a distributed energy output prediction model, an energy storage charging and discharging model, a load demand model, and a power grid interaction model. The distributed energy output prediction adopts four-dimensional prediction technology (point prediction, interval prediction, probability prediction, and scenario prediction), and the prediction uncertainty is handled by the sub-Bruker optimization method. Optimization of scheduling strategy generation: The mathematical model is solved using a multi-objective optimization algorithm (particle swarm optimization, genetic algorithm or generalized eigenvector algorithm) to generate a scheduling strategy with the objectives of maximizing renewable energy utilization, minimizing operating costs and minimizing grid fluctuations. The scheduling strategy includes distributed energy output commands, energy storage charging and discharging power commands and load control commands. Coordinated control and dynamic adjustment: The scheduling strategy is executed through the multi-port converter of the energy router to realize the power coordinated control of distributed energy and energy storage devices; when the grid frequency fluctuation is detected to exceed the preset threshold, the virtual inertia control and virtual damping control of the energy storage device are triggered to suppress frequency oscillation and reduce energy storage demand; Green power conversion and cascade utilization: When distributed energy output is excessive, the excess electrical energy is converted into hydrogen energy storage through solid oxide electrolysis hydrogen production / fuel cell system, and the waste heat in the electrolysis process is utilized in a cascade manner through thermal integration subsystem to drive absorption chillers or meet users' heating needs.

3. The system according to claim 1, characterized in that, The low-voltage DC power network has a ring network topology and is equipped with a DC interconnection switch to achieve multi-path power supply and improve system reliability.

4. The system according to claim 1, characterized in that, The main control module of the energy router has a built-in artificial intelligence algorithm that uses historical data to train and optimize the model, and dynamically adjusts the energy distribution strategy to adapt to load changes and environmental conditions.

5. The system according to claim 1, characterized in that, In the solid oxide electrolysis hydrogen production / fuel cell system of the energy storage device, the water in the water tank is evaporated and superheated to the working temperature and then introduced into the fuel electrode. At the same time, the air is heated to the working temperature and then introduced into the air electrode. The generated hydrogen is cooled by heat exchange and then stored in the hydrogen storage tank.

6. The method according to claim 2, characterized in that, The multi-objective optimization algorithm employs Pareto front solving techniques to generate a set of non-dominated solutions for users or power grid dispatching agencies to choose from, achieving a balance between economic efficiency and environmental protection.

7. The method according to claim 2, characterized in that, In the green power conversion process, the incompletely reacted hydrogen and excess air generated by the fuel cell power generation are burned through the tail burner, and the resulting high-temperature mixed gas is preheated through the thermal integration subsystem or supplied to the user's hot water needs.

8. The system according to claim 1, characterized in that, The edge computing module of the intelligent control unit communicates bidirectionally with the cloud energy management platform, receives grid dispatch instructions and uploads system operation data, and participates in grid peak shaving and demand-side response auxiliary services.

9. The system according to claim 1, characterized in that, The demand-side response controller of the load management module prioritizes power supply to flexible loads such as electric vehicle charging piles and energy storage air conditioners during off-peak hours based on time-of-use pricing signals, and reduces their power consumption during peak hours.

10. The system according to claim 1, characterized in that, The interface module of the energy router integrates an intelligent identification chip, which includes the type, parameters, and status information of the access device, enabling automatic identification and parameter configuration of the device.

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