A multi-source interaction energy operation optimization method and system

Abstract

The application discloses a kind of multi-source interaction energy operation optimization method and system, belong to integrated energy system optimization scheduling technical field.System setting renewable energy power generation unit, electric energy storage unit, hydrogen energy coupling unit, gas supply unit, cold and heat energy cascade utilization unit, multiple load unit and collaborative management platform, construct AC and DC hybrid networking, hydrogen energy two-way coupling, three-stage waste heat cascade utilization Multi-energy closed-loop topology structure.The application is through multi-source data preprocessing and output prediction, digital twin working condition identification, multi-time scale adaptive nested scheduling, multi-energy flow layered interaction, closed-loop correction and fault self-healing complete operation system, solve the problem that traditional system new energy consumption capacity is weak, multi-energy coupling single, extensive waste heat utilization, scheduling adaptability is poor, realize multi-energy flow collaborative closed-loop operation, effectively improve system energy supply stability and operation economy, adapt high proportion renewable energy park energy supply scene.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization and scheduling technology, and specifically discloses a multi-source interactive energy operation optimization method and system, which is applicable to distributed integrated energy supply in scenarios such as industrial parks, commercial complexes, and urban and rural communities. Background Technology

[0002] With the continuous advancement of dual-carbon goals and the large-scale grid connection and application of renewable energy sources such as photovoltaics and wind power, distributed integrated energy systems are gradually becoming the mainstream form of energy supply in industrial parks and an important carrier for achieving energy consumption reduction and efficiency improvement and energy structure upgrading.

[0003] Existing traditional energy supply systems generally adopt an architecture where cooling, heating, electricity, and gas subsystems operate independently and are simply connected in parallel. These energy links are isolated, possessing only unidirectional energy conversion capabilities and failing to form a multi-energy flow, bidirectional closed-loop interactive topology. On the one hand, wind and solar power output exhibits significant fluctuations. Traditional systems rely solely on short-term electrochemical energy storage for power smoothing and redundancy absorption, resulting in a single absorption path, limited regulation capacity, and a high likelihood of curtailment of solar and wind power, leading to low on-site utilization rates of new energy sources. On the other hand, existing systems lack a tiered and matched waste heat utilization system, resulting in the mixed use of waste heat resources of different temperature grades. This leads to energy mismatch, significant energy loss, and difficulty in improving overall energy efficiency.

[0004] At the dispatching and operation level, existing integrated energy dispatching schemes mostly adopt single-time-scale optimization strategies with fixed control logic. They cannot adaptively adjust dispatching weights and control strategies based on real-time system output levels, load status, and equipment operating status, making it difficult to adapt to various complex operating conditions such as new energy fluctuations, dynamic load changes, and equipment anomalies. At the same time, traditional dispatching models lack digital twin real-time mapping correction mechanisms and systematic fault self-healing and reconfiguration logic, resulting in accumulated operational deviations, insufficient adaptability to operating conditions, and poor dynamic operational stability of the system.

[0005] In existing technologies, publicly available multi-energy complementary microgrid systems for industrial parks only implement a combined electricity, heat, and cooling (CCHP) structure, without setting up bidirectional coupling links for gas and hydrogen energy. This limits the dimensions of energy interaction and prevents the realization of deep coupling and closed-loop operation of multiple networks involving electricity, hydrogen, heat, and gas. Publicly available multi-energy flow collaborative scheduling methods only employ single-time-scale optimization logic, resulting in a single scheduling level. This fails to simultaneously address the multi-layered control requirements of day-ahead planning economy, intraday fluctuation correction, and real-time power stability, and also indicates insufficient adaptability to different operating conditions.

[0006] In summary, existing technologies generally suffer from insufficient multi-energy coupling depth, extensive waste heat utilization modes, limited new energy absorption capacity, poor adaptive scheduling mechanisms, and lack of systematic fault self-healing capabilities. They lack a complete technical solution that ranges from multi-energy coupling topology and waste heat cascade architecture to multi-timescale adaptive scheduling and closed-loop self-healing operation, and thus cannot meet the requirements for efficient, stable, and economical closed-loop operation of integrated energy systems in scenarios with a high proportion of renewable energy. Summary of the Invention

[0007] To address the aforementioned problems, this invention provides a multi-source interactive energy operation optimization system, comprising a renewable energy power generation unit, an electric energy storage unit, a hydrogen energy coupling unit, a gas supply unit, a cascade utilization unit for cold and heat energy, a multi-load unit, and a collaborative management and control platform; The renewable energy generation unit, the energy storage unit, and the hydrogen energy coupling unit are all connected to the AC / DC hybrid bus to realize the collection of multiple power sources and bidirectional throughput. The hydrogen energy coupling unit is equipped with multi-port coupling interfaces for electricity, gas, and waste heat, and integrates equipment for electric hydrogen production, hydrogen storage, hydrogen power generation, and waste heat recovery, realizing bidirectional coupling and conversion of multiple energy sources such as electricity, hydrogen, heat, and gas. The waste heat output end of the hydrogen energy coupling unit is connected to the cold and heat energy cascade utilization unit. The cold and heat energy cascade utilization unit builds a three-level waste heat reuse architecture of high temperature refrigeration, medium temperature heating and low temperature preheating. The cold and heat energy cascade utilization unit respectively accepts the waste heat medium of hydrogen energy coupling unit and gas supply unit, and centrally completes the step-by-step matching and recycling of multi-grade waste heat, and outputs cold and heat energy to the outside in a unified manner. The gas supply unit is equipped with multiple independent gas supply circuits. One circuit is connected to the power generation equipment to produce high-temperature flue gas and waste heat, which is then connected to the cold and heat energy cascade utilization unit as a high-temperature heat source input. The remaining circuits are used to achieve peak-shaving heating and end-user gas supply, respectively. The multi-load unit includes rigid loads and flexibly adjustable loads, supporting load shifting and tiered switching. The multi-load unit is connected to the AC / DC hybrid bus, the cold and heat energy cascade utilization unit, and the gas supply unit, respectively, and receives electrical energy, cold energy, heat energy, and gas supply output. The collaborative management and control platform is connected to renewable energy power generation units, electric energy storage units, hydrogen energy coupling units, gas supply units, cold and heat energy cascade utilization units, and multi-load units. It integrates data sensing, digital twin modeling, multi-objective optimization scheduling and fault self-healing functions, collects the operation data of each unit and issues control commands to coordinate and manage the energy equipment and interconnected energy flows across the entire domain. The system is based on an AC / DC hybrid network, multi-directional hydrogen energy coupling and waste heat cascade utilization architecture to achieve closed-loop collaborative interactive operation of multiple energy flows.

[0008] This invention also provides a multi-source interaction energy operation optimization method, which, using the above-mentioned system, includes the following steps: S1. Collect system power, heat, cold, gas, hydrogen operation data and meteorological data in a unified manner, complete clock alignment, data cleaning and dimension normalization processing, and combine deep learning models to complete the graded rolling prediction of new energy output. S2. Construct a multi-energy flow mechanism simulation model, correct the model parameters online based on real-time operating data, and establish a synchronous mapping relationship between the physical system and the virtual model; classify the typical operating conditions of the system by combining clustering algorithms with operating characteristics. S3. With operating costs, carbon emissions, and curtailment rate as optimization objectives, and matching energy balance constraints and equipment operation constraints, the scheduling weights and control strategies are dynamically adjusted based on real-time operating conditions to establish a three-level nested scheduling mechanism of day-ahead pre-scheduling, intraday rolling optimization, and real-time correction. S4. Set a graded priority strategy for energy surplus consumption and energy gap supplementation, and complete multi-source energy matching and flexible load regulation based on the principles of waste heat cascade matching and temperature range matching. S5. Performs hourly operation deviation correction and daily full parameter iteration update of the model; completes fault identification by combining wavelet packet feature extraction with support vector machine (SVM) classifier, and matches corresponding power compensation, load regulation, energy transfer and self-healing reconstruction strategies for faults in core equipment such as energy storage, refrigeration and hydrogen production to maintain continuous system operation.

[0009] Preferably, in S1, the PTP protocol is used to complete global clock alignment, the 3σ criterion and sliding filter are used to complete data noise reduction and impurity removal, and the min-max algorithm is used to complete data normalization; the LSTM model is used to perform long-period day-ahead prediction and short-period ultra-short-term rolling prediction respectively, and output the new energy power output prediction results.

[0010] Preferably, in S2, a multi-energy flow mechanism model is constructed based on the conservation of energy and mass, and the recursive least squares method is used to correct the equipment efficiency and pipeline loss parameters online; the fuzzy C-means clustering algorithm is used to classify the operating states into four categories: normal operating condition, new energy power generation operating condition, power supply gap operating condition, and equipment failure operating condition.

[0011] Preferably, in S3, the day-ahead scheduling uses the improved NSGA-III algorithm to solve the multi-objective optimal solution set, and combines it with the TOPSIS algorithm to screen the scheduling scheme; intraday rolling optimization corrects short-term power output fluctuations, and real-time correction control uses MPC model predictive control to smooth out system power deviations.

[0012] Preferably, in the S4 state of energy surplus, redundant energy is consumed according to the priority of load absorption, energy storage charging, electrolysis hydrogen production, waste heat replacement for energy supply, and surplus electricity grid connection; in the state of energy shortage, the energy supply gap is supplemented according to the priority of energy storage discharge, hydrogen power generation, gas combined cycle power, grid power purchase, and flexible load shedding; waste heat resources are reused layer by layer according to the corresponding demand based on temperature range.

[0013] Preferably, S5 performs hourly operation deviation correction and daily full parameter iterative update of the model; fault identification is completed by wavelet packet feature extraction combined with SVM classifier, and corresponding power compensation, load regulation, energy transfer self-healing reconfiguration strategies are matched for faults in core equipment such as energy storage, refrigeration, and hydrogen production to maintain continuous system operation.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. High proportion of renewable energy consumption: Through the dual energy storage system of "electric energy storage + hydrogen energy storage and release" and combined with the two-way conversion of electricity and gas, the renewable energy consumption rate is increased to more than 90%, effectively solving the problem of curtailment of solar and wind power.

[0015] 2. Significantly improved overall energy efficiency: Through the cascade utilization of waste heat, complementary multi-energy paths, and closed-loop coupling of cold-heat-electricity-gas, the overall energy utilization efficiency of the system can reach more than 85%, which is far higher than that of traditional distributed supply systems.

[0016] 3. Significantly enhanced scheduling accuracy and reliability: Multi-timescale rolling optimization matches the fluctuation characteristics of different time dimensions, and digital twin closed-loop feedback improves scheduling accuracy; the fault autonomous reconfiguration strategy enables the system power supply reliability to reach 99.99%.

[0017] 4. Balancing economic efficiency and environmental friendliness: Multi-objective optimized scheduling balances operating costs and carbon emissions, reducing operating costs by 15%-25% and carbon emissions by more than 40% compared to traditional systems. Attached Figure Description

[0018] Figure 1 is a schematic diagram of the AC / DC hybrid bus interconnection control principle in an embodiment of the present invention; Figure 2 is a detailed topology diagram of the hydrogen energy four-port coupling unit in an embodiment of the present invention; Figure 3 is a diagram showing the energy flow of the three-stage waste heat utilization system in an embodiment of the present invention. Figure 4 This is a layered architecture diagram of the central collaborative management and control platform according to an embodiment of the present invention; Figure 5 This is an overall flowchart of the efficient integrated interaction method in this embodiment of the invention; Figure 6 This is a schematic diagram of the multi-time-scale optimized scheduling timing in an embodiment of the present invention; Figure 7 This is a schematic diagram of multi-timescale scheduling closed-loop interaction in an embodiment of the present invention; Figure 8 This is a logic block diagram of working condition identification and scheduling mode switching in an embodiment of the present invention; Figure 9 This is a multi-energy flow energy interaction priority logic diagram in an embodiment of the present invention; Figure 10 This is a flowchart illustrating the digital twin proactive defense and fault self-healing process in an embodiment of the present invention. Figure 11 is a typical daily energy balance curve in an embodiment of the present invention; Figure 12. Bar chart comparing the performance indicators of the combined system using different control methods. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without innovative effort are within the scope of protection of the present invention.

[0020] I. Implementation methods of multi-source interactive energy operation optimization system This system adopts a core topology of "AC / DC hybrid bus + three-stage waste heat utilization + hydrogen energy bidirectional coupling" to achieve strong coupling and full closed-loop interaction of four energy flows: cold, heat, electricity and gas, which is different from the traditional multi-energy system with simple parallel connection of equipment.

[0021] 1. Renewable energy power generation unit (source layer) Internal topology and connections The photovoltaic power generation array adopts a string modular architecture, which is divided into N photovoltaic strings, each string corresponding to one string inverter. The DC side is connected to the DC750V DC bus through a DC combiner cabinet. The permanent magnet direct-drive wind turbine generator is connected to the AC10kV / 380V AC bus via a full-power converter and is equipped with low-voltage ride-through and reactive power support functions. An integrated weather station is deployed in an open area of ​​the factory, and is hardwired to the power output prediction module.

[0022] Core technical parameters Photovoltaic modules: Monocrystalline silicon PERC modules, conversion efficiency ≥23%, operating temperature coefficient -0.38% / ℃; String inverter: MPPT efficiency ≥ 99.5%, maximum efficiency 98.8%, supports 1.2 times overload operation; Wind turbine: Horizontal axis permanent magnet direct drive type, single unit capacity 1MW, rated wind speed 12m / s, cut-in wind speed 3m / s, cut-out wind speed 25m / s; Meteorological data collection elements: total horizontal irradiance, direct normal irradiance, ambient temperature and humidity, wind speed and direction, and air pressure. Sampling frequency: 1 time / min.

[0023] 2. Electrical energy storage unit (reservoir-short-term energy storage) Internal structure and connections It consists of lithium iron phosphate battery clusters, combiner cabinets, bidirectional energy storage converters (PCS), battery management systems (BMS), and perfluorohexanone fire suppression systems; The DC side is connected to a DC 750V DC bus, and the AC side is connected to an AC 380V AC bus. The BMS is connected to the central control platform via a CAN bus.

[0024] Core technical parameters Cell specifications: 280Ah lithium iron phosphate cell, rated voltage 3.2V, cycle life ≥6000 cycles (80% DOD); Battery cluster architecture: 1P12S per cluster, rated voltage 768V, single cluster capacity 215kWh; PCS: Three-level bidirectional topology, rated efficiency ≥94.5%, active response time ≤100ms, supports primary frequency modulation and black start; Operating constraints: SOC operating range 20%~90%, maximum charge / discharge rate 0.5C, ramp rate ≤10% rated power / minute.

[0025] 3. Hydrogen Energy Coupling Unit (Transition - Core Innovation Point, Core of Two-Way Interaction Between Electricity and Gas) This unit serves as the core coupling hub for the system's four-port electrical-hydrogen-heat-gas system, enabling bidirectional conversion across the entire electrical-hydrogen chain. It also possesses four major functions: long-term energy storage, waste heat energy supply, gas blending, and direct load supply. It is a crucial node connecting the three major energy networks of electricity, heating, and gas, distinguishing it from traditional hydrogen energy systems that only produce hydrogen through electricity and perform single-function energy storage. This unit forms four external coupling ports: a DC electrical port (connected to the DC bus), an AC electrical port (fuel cells connected to the AC bus), a gas coupling port (blended with hydrogen and connected to the gas pipeline network), and a waste heat heating port (connected to the medium-temperature heating pipeline network).

[0026] The complete subsystem consists of: an alkaline electrolysis hydrogen production subsystem, a hydrogen purification subsystem, a hydrogen compression subsystem, a high-pressure hydrogen storage tank group, a pressure regulating subsystem, a proton exchange membrane fuel cell power generation system, and a waste heat recovery circuit.

[0027] Connection topology and quantization parameters: 1) Electrolysis hydrogen production branch The DC input terminal of the alkaline electrolyzer is connected to the DC bus via a DC circuit breaker, and the water inlet is connected to the EDI pure water system. Rated working pressure: 1.6 MPa; working temperature: 80-85℃; current density: 0.25-0.4 A / cm³. 2 DC power consumption ≤4.8kWh / Nm 3 H2; Equipped with a gas-liquid separator, alkali circulation pump, and cooler, the initial purity of hydrogen produced is ≥99.9%.

[0028] 2) Purification and compression branch The palladium membrane purification unit produces hydrogen with an outlet purity of ≥99.999%, meeting the standards for fuel cell-grade hydrogen. Diaphragm hydrogen compressor, three-stage compression, outlet pressure 35MPa, discharge volume 300Nm 3 / h.

[0029] 3) Hydrogen storage branch 4 units with a volume of 12.5m³ 3 High-pressure hydrogen storage tank, total water volume 50m³ 3 The hydrogen storage capacity is approximately 500 Nm³ at a rated pressure of 35 MPa. 3 This meets the system's peak-shaving needs for 3-5 days. It is equipped with a high-precision pressure transmitter, safety valve, rupture disc, and hydrogen concentration sensor (detection accuracy ≤1ppm), with a leakage alarm threshold of 25%LEL.

[0030] 4) Fuel cell power generation branch The outlet of the hydrogen storage tank is connected to the anode of the fuel cell via a pressure reducing valve assembly (35MPa→0.8MPa); Proton exchange membrane fuel cell (PEMFC) with a rated power of 1MW, an operating temperature of 65~75℃, and a rated power generation efficiency of 45% (low calorific value). Waste heat recovery rate ≥40%, outlet hot water temperature 60~70℃, connected to the return water side of the heating network; cold start time ≤30s, supporting second-level power response.

[0031] 4. Gas supply unit (Gas-Gas-Electric-Heat Tri-generation) Internal piping topology The municipal medium-pressure gas pipeline interface, gas filter and pressure regulating device, is divided into three independent circuits: First route: connected to a micro gas turbine for gas-to-electricity conversion and high-temperature waste heat recovery; Second route: Connect to a gas-fired boiler for peak heating and backup; The third route: directly supplies the gas load of the industrial park (canteen, industrial gas equipment, etc.).

[0032] Core technical parameters Gas pressure regulating device: inlet pressure 0.2 / 0.4MPa, outlet pressure 5 / 10kPa, equipped with overpressure shut-off valve and turbine flow meter; Micro gas turbine: regenerative type, rated power 1.5MW, power generation efficiency 33%, exhaust gas temperature 520℃, exhaust gas volume 5.5kg / s, NO x Emissions ≤30mg / m³ 3 The exhaust outlet is directly connected to a high-temperature waste heat exchanger. Gas-fired boiler: horizontal three-pass wet-back type, rated evaporation capacity 2t / h, rated outlet water temperature 95℃, thermal efficiency ≥93%, proportional burner, load adjustment range 30%~100%.

[0033] 5. Cold and heat energy supply unit (transfer floor - three-stage waste heat utilization) By adopting the principles of temperature matching and tiered utilization, a three-level waste heat utilization framework of "high-temperature cooling - medium-temperature heating - low-temperature preheating" is constructed to achieve progressive matching of energy quality and full recovery of waste heat, which is one of the core innovations in improving comprehensive energy efficiency.

[0034] Level 1: High-temperature waste heat cooling (400~520℃) The 520°C high-temperature flue gas discharged from the gas turbine is sent to the waste heat boiler to generate 150°C saturated steam. The high-temperature steam drives a double-effect lithium bromide absorption chiller to produce 7°C chilled water, realizing heat-to-cold conversion. The exhaust temperature of the chiller drops to 120°C, and the domestic hot water is first heated to 80°C by the flue gas-water heat exchanger and then reused in the second-stage medium-temperature heating layer. After the flue gas is cooled to 50°C, it enters the low-temperature economizer to preheat the domestic water supply, realizing the step-by-step utilization of waste heat and avoiding the waste of high-grade heat source in low-grade form.

[0035] Level 2: Medium-temperature waste heat heating (60~80℃) The waste heat of 65~70℃ from the fuel cell cooling water circuit, the waste heat of 80~85℃ from the electrolyzer cooling circuit, and the 80℃ hot water recovered from the flue gas of the lithium bromide chiller are all fed into the plate heat exchanger group to collect the medium-temperature waste heat and heat the return water to 60℃; priority is given to supplying domestic hot water and building heating, and the insufficient part is supplemented by gas boiler.

[0036] Level 3: Low-temperature waste heat preheating (30~40℃) The condensation heat of electric chillers and lithium bromide chillers is used to preheat the cold water makeup water through a condensation heat recovery device. Raising the water supply temperature from 15℃ to 30℃ reduces heating energy consumption by approximately 15%.

[0037] Core equipment parameters Dual-effect lithium bromide absorption chiller: rated cooling capacity 2MW, COP=1.35, driving heat source temperature ≥110℃, chilled water supply and return water temperature difference 5℃; Screw-type electric chiller: rated cooling capacity 3MW, COP=4.2, frequency conversion adjustment range 20%~100%; Plate waste heat exchanger: heat exchange efficiency ≥92%, design pressure 1.6MPa; Electric boiler: electrode type, rated thermal power 2MW, thermal efficiency ≥98%, used as a backup for heating under extreme conditions.

[0038] 6. Multi-element load unit (load layer) Load Classification and Characteristics Rigid loads: Production power, lighting, and basic cooling / heating / gas loads, which are not adjustable and account for approximately 80%; Flexible and movable loads: cold and heat storage tanks and non-critical production processes can be moved within 1 to 4 hours, accounting for about 10%; Flexible interruptible loads: office air conditioning, domestic hot water, and auxiliary equipment, which can be interrupted for 0.5 to 2 hours, accounting for about 10%.

[0039] Terminal Configuration Each flexible load circuit is equipped with an intelligent circuit breaker and load controller, supports the Modbus-RTU protocol, has a command response time of ≤5s, and can receive power regulation and switching commands from the central platform.

[0040] 7. Centralized Coordination and Control Platform (Brain Layer) The system adopts a four-layer industrial internet architecture consisting of "perception layer - network layer - platform layer - application layer" to achieve closed-loop management and control of the entire system.

[0041] Perception layer Deploy 120+ monitoring nodes, covering electrical parameters, thermal parameters, status parameters, and environmental parameters; sampling frequency is tiered: electrical parameters once per second, thermal parameters once per 10 seconds, and fault signal events triggering.

[0042] Network layer Backbone: Industrial Ethernet ring network, 1000Mbps bandwidth, redundant design; Backup: 5G wireless communication for remote nodes and emergency communication; Protocol: Southbound Modbus-RTU, MQTT; Northbound OPCUA; IEEE 1588PTP clock synchronization, synchronization accuracy ≤1ms; Performance: End-to-end transmission latency ≤50ms, data packet loss rate ≤0.1%.

[0043] Platform layer Data platform: Real-time database (InfluxDB) stores time-series data, supporting concurrent writes to millions of measurement points; relational database stores configuration and historical data; Digital Twin Engine: Based on Modelica, a multi-energy flow mechanism model is built, combined with LSTM data-driven correction, and the model simulation accuracy is ≥95%; it supports real-time mapping, working condition simulation, and fault prediction. Optimized solver engine: Built-in improved NSGA-III algorithm, Gurobi solver, and MPC model prediction controller.

[0044] Application layer It includes six major functional modules: multi-energy optimization scheduling, equipment monitoring and alarm, energy efficiency analysis, demand response, fault self-healing, and human-machine interaction.

[0045] II. Implementation Methods of Multi-Source Interactive Energy Operation Optimization This method adopts the technical path of "multi-timescale rolling optimization + hierarchical execution + digital twin closed-loop correction". All steps have quantitative criteria and clear algorithms, which can be directly implemented in code.

[0046] S1 All-Dimensional State Perception and Data Normalization Processing Synchronous data acquisition: The edge gateway synchronously acquires operational data and meteorological data from all nodes, and achieves system-wide clock alignment via the PTP protocol, with a timestamp error of ≤1ms; Data cleaning: Outliers are removed using the 3σ criterion, and random noise is removed using a 5-point moving average filter; Normalization: Perform min-max normalization on data of different dimensions, map to the [0,1] interval, and unify the input to the prediction and optimization model; Hierarchical prediction: Current forecast: LSTM deep learning model, inputting historical power output, weather forecast, and seasonal factors, with a step size of 1 hour and a duration of 24 hours, predicts a photovoltaic prediction error of ≤8%. Ultra-short-term forecast: Rolling updates, step size 15 minutes, duration 4 hours, photovoltaic forecast error ≤3%.

[0047] S2 Digital Twin Construction and Online Operating Condition Identification Twin modeling Mechanism layer: Differential equation models for power flow, heat pipeline, gas pipeline, and hydrogen energy system are established based on energy conservation and mass conservation. Correction layer: Based on actual operating data, the equipment efficiency and pipeline loss parameters are corrected online daily using the recursive least squares method; Mapping layer: Physical data at 1Hz drives the twin model to run, achieving synchronization between virtual and real states.

[0048] Automatic working condition identification The Fuzzy C-means (FCM) clustering algorithm is used to automatically identify four operating conditions based on four feature quantities: output deviation, load level, and equipment status. Normal operating conditions: Output-load deviation ≤10%, all equipment is functioning normally; Operating condition: Renewable energy output exceeds 120% of the load plus the upper limit of energy storage charging, posing a risk of energy curtailment; Energy shortage condition: Peak load > 110% of total energy supply capacity, indicating an energy supply gap; Fault condition: One or more devices are taken out of service.

[0049] S3 Multi-Timescale Multi-Objective Optimization Scheduling Decision (Core Mathematical Model) This scheduling method is an adaptive multi-time-scale scheduling driven by operating conditions. The scheduling target weight, adjustment subject, and time step are dynamically adjusted under different operating conditions, which is different from the traditional fixed-mode scheduling method.

[0050] S3.1 Optimize scheduling (24h, step size 1h) Total operating cost (electricity purchase cost + gas cost + operation and maintenance cost); Total carbon emissions Carbon emission factor; Renewable energy curtailment rate; : Weighting coefficients objectively assigned by the entropy weighting method.

[0051] The core constraints include: Power balance constraints Thermodynamic equilibrium constraints Cooling balance constraint Hydrogen energy balance constraints Equipment operation constraints: upper and lower limits of output, ramp rate, upper and lower limits of energy storage SOC, upper and lower limits of hydrogen storage pressure, pipeline pressure constraints, etc.

[0052] Solution algorithm: Improve NSGA-III by introducing an adaptive crossover and mutation operator, with a population size of 100 and 200 iterations. After generating the Pareto solution set, the TOPSIS method is used to select the optimal compromise solution.

[0053] S3.2 Intraday Rolling Optimization (15-minute step, 4-hour rolling window) The ultra-short-term forecast data is updated every 15 minutes. Based on the current day plan, the equipment output for the next 4 hours is adjusted. The focus is on adjusting the electrolyzer, fuel cell, energy storage, and flexible load to smooth out 15-minute fluctuations and minimize deviations from the current day plan.

[0054] S3.3 Real-time calibration control (1-minute step size, MPC control) Model predictive control is adopted with a prediction step size of 10 minutes and a control step size of 1 minute. Based on the real-time power deviation, the active power of the energy storage PCS and the switching of flexible loads are adjusted to maintain the AC bus frequency deviation ≤ ±0.2Hz and the tie line power deviation ≤ ±5%.

[0055] S4 hierarchical energy interaction execution S4.1 Source-Storage-Gas Two-Way Interaction Rules (Quantified Priority) Energy surplus scenarios (consumption priority from high to low): basic electrical load → electric energy storage charging → electrolyzer hydrogen production → electric refrigeration / electric boiler to replace gas energy supply → surplus electricity to the grid; Energy gap scenarios (energy replenishment priority from high to low): Electric storage discharge → Fuel cell power generation → Gas turbine combined cycle power → Grid power purchase → Cut off flexible interruptible loads.

[0056] S4.2 Hot-Cold Cascade Interaction Rules Strictly adhere to the principle of temperature matching: Cold source priority: Lithium bromide absorption refrigeration > Electric refrigeration; Heat source priority: waste heat recovery (gas turbine + fuel cell) > gas boiler > electric boiler; Waste heat should be utilized in stages according to temperature, and high-grade heat energy should not be directly supplied to low-grade loads.

[0057] S4.3 Load-side response execution logic When the power supply gap lasts for more than 5 minutes and the generation side regulation is exhausted, the load that can be shifted should be called first, followed by the load that can be interrupted; non-critical loads should be cut off first to ensure the basic loads for production and people's livelihood.

[0058] S5 Dynamic Feedback Correction and Fault Self-Healing Reconstruction Closed-loop feedback correction Every hour, compare the actual value with the planned value. If the deviation is greater than 10%, trigger the twin model parameter correction and adjust the initial value of the next scrolling window. Daily model calibration is performed every morning at midnight to update the equipment efficiency matrix.

[0059] Fault diagnosis and self-healing Fault diagnosis: Wavelet packet decomposition to extract signal features + SVM classifier, diagnostic accuracy ≥95%, time ≤30s; Typical refactoring strategies: Energy storage failure: The fuel cell starts up within 30 seconds to smooth out fluctuations, and the gas turbine increases its output for backup; Electrical refrigeration failure: Increase the output of lithium bromide refrigeration, release the cold storage tank, and cut off non-critical cooling loads if necessary; Electrolytic cell malfunction: Excess electricity is converted into electrical energy storage and absorbed. Once the energy storage is full, the electric boiler is started to heat and store the heat, thus avoiding energy waste.

[0060] Example 1: Multi-energy coupled micro-power supply system for cold-heating-electricity-gas in northern administrative villages 1. Application Scenarios This example is applied to an administrative village in a northern plain region, with a total of 320 households. The village includes a village service center, a health clinic, and a small grain processing workshop, with a total building area of ​​42,000 square meters. Energy needs include residential electricity, winter heating, summer cooling, domestic hot water, cooking gas, and electricity and heat load for agricultural product processing. The village is covered by the township municipal gas pipeline network and has unused rooftops and open space at the village entrance suitable for deploying photovoltaic and small-scale wind power, making it a typical rural distributed integrated energy application scenario.

[0061] 2. System Configuration This embodiment uses the cold-heat-electric-pneumatic multi-energy coupled micro-power supply system described in this invention, and the specific configuration of each unit is as follows: (1) Renewable energy power generation unit Photovoltaic power generation array: Total installed capacity of 1.2MW, using monocrystalline silicon PERC modules with a conversion efficiency of 23.2%, divided into 12 strings, corresponding to 12 100kW string inverters with an MPPT efficiency of 99.6%, and the DC side is connected to the DC 750V DC bus via a combiner cabinet.

[0062] Wind turbine generator set: 1 500kW permanent magnet direct drive horizontal axis wind turbine, rated wind speed 11m / s, cut-in wind speed 2.8m / s, cut-out wind speed 25m / s, connected to AC380V AC bus via full power converter, with low voltage ride-through capability.

[0063] It is equipped with one integrated weather station to collect data such as horizontal irradiance, normal irradiance, wind speed and direction, and ambient temperature and humidity, with a sampling frequency of 1 time / min.

[0064] (2) Energy storage unit The lithium iron phosphate battery energy storage system is configured with 300kW / 600kWh, using 280Ah cells, with 1P12S per cluster and a total of 3 clusters; it is equipped with a three-level bidirectional PCS, with a rated efficiency of 94.5% and an active power response time of ≤100ms; the SOC operating range is set to 20%~90%, and the maximum charge / discharge rate is 0.5C.

[0065] (3) Hydrogen energy coupling unit (electric-gas bidirectional interaction core) Alkaline electrolytic cell: rated power 200kW, working pressure 1.6MPa, working temperature 82℃, current density 0.35A / cm³ 2 DC power consumption ≤4.7kWh / Nm 3 H2, rated hydrogen production 42 Nm 3 / h, with an initial hydrogen production purity of ≥99.92%, and equipped with a gas-liquid separator and an alkaline solution circulation system.

[0066] Hydrogen purification and compression: Palladium membrane purification unit, outlet hydrogen purity ≥99.999%, meeting fuel cell-grade hydrogen standards; diaphragm hydrogen compressor, three-stage compression, outlet pressure 35MPa, discharge volume 50Nm³. 3 / h.

[0067] High-pressure hydrogen storage tank assembly: 2 units, each with a volume of 6.25m³. 3 High-pressure hydrogen storage tank, total water volume 12.5m³ 3 The hydrogen storage capacity is approximately 125 Nm³ at a rated pressure of 35 MPa. 3 It meets the system's 3-day peak shaving requirements; it is equipped with a pressure transmitter and a hydrogen leak sensor, with a leak alarm threshold of 25% LEL.

[0068] Proton exchange membrane fuel cell (PEMFC): rated power 100kW, power generation efficiency 45% (low calorific value), operating temperature 68℃, waste heat recovery rate ≥42%, outlet hot water temperature 65℃, connected to the return water side of the heating network, cold start time ≤30s.

[0069] (4) Gas supply unit The municipal medium-pressure gas pipeline interface is connected to the system via a gas filter and pressure regulating device. The inlet pressure is 0.2~0.4MPa, and the outlet pressure is stable at 8kPa. It is equipped with an overpressure shut-off valve and a turbine flow meter.

[0070] Configure one 0.5t / h horizontal gas-fired boiler with a thermal efficiency of ≥93%, a proportional burner, and a load adjustment range of 30%~100%, for use as a heating backup and peak shaving unit.

[0071] The gas supply is divided into three routes: priority is given to supplying heat generated by micro gas turbines for power generation, followed by supplying heat to gas boilers, and the remaining amount is directly supplied to villagers for cooking.

[0072] (5) Cold and heat energy supply unit (three-stage waste heat utilization) Heating subunit: fuel cell waste heat plate heat exchanger (heat exchange power 80kW, heat exchange efficiency 92%), gas boiler, 150kW electrode electric boiler, connected to the heating network, supply and return water temperature 60 / 45℃.

[0073] Refrigeration subunits: 500kW screw-type electric chiller (COP=4.2, frequency conversion range 20%~100%), 300kW double-effect lithium bromide absorption chiller (COP=1.35, driving heat source temperature ≥110℃), chilled water supply and return water 7 / 12℃.

[0074] Waste heat cascade architecture: high-temperature flue gas from gas boiler drives lithium bromide refrigeration (first-stage high-temperature waste heat), waste heat from fuel cell supplies heating and domestic hot water (second-stage medium-temperature waste heat), and condensation heat from refrigeration unit preheats domestic water supply (third-stage low-temperature waste heat), increasing the water supply temperature from 15℃ to 30℃.

[0075] (6) Multi-unit load Total peak power load: 850kW; peak winter heating load: 700kW; peak summer cooling load: 650kW; average daily cooking gas consumption: 180Nm³. 3 .

[0076] The flexible load accounts for 18%, including residential air conditioning load, hot water storage load, and non-critical processing load. Each flexible circuit is equipped with an intelligent circuit breaker and load controller, supports the Modbus-RTU protocol, and has a command response time of ≤5s.

[0077] (7) Centralized collaborative management and control platform It adopts a four-layer industrial internet architecture of "perception layer - network layer - platform layer - application layer" and deploys more than 120 monitoring nodes; it adopts 5G + industrial Ethernet dual-link communication and IEEE1588PTP clock synchronization accuracy ≤1ms.

[0078] It features a built-in digital twin engine and a multi-timescale optimized scheduling module, along with an improved NSGA-III solution algorithm and an MPC real-time controller, enabling closed-loop management of the entire system.

[0079] 3. Typical winter daily operational interaction process Taking a typical sunny day in winter as an example, the system operates strictly according to the efficient integration and interaction method described in this invention. The core process is as follows: 0:00-6:00 (nighttime off-peak period): Renewable energy output is 0, and the system identifies it as a normal energy shortage condition. Priority is given to using hydrogen stored in the hydrogen storage tank to drive fuel cell power generation to supply the basic power load, and the waste heat from the fuel cell is simultaneously supplied to the basic heating load at night; the energy storage SOC is maintained at 50% for thermal standby, and the gas boiler is in a low-output standby state.

[0080] 6:00-10:00 (load rise period): Photovoltaic output gradually increases, prioritizing the supply of electricity to residential and industrial users; surplus electricity is used to charge energy storage, increasing the SOC from 50% to 88%; gas boilers operate at reduced output to supplement the heating load gap.

[0081] 10:00-14:00 (Peak Photovoltaic Output Period): Peak photovoltaic output reaches 1.08MW, exceeding the total power load, and the system is identified as operating under peak output conditions. After the energy storage is fully charged to 90% SOC, all surplus electricity is input into the electrolyzer to produce hydrogen for storage, and the hydrogen storage tank pressure rises from 12MPa to 28MPa; the fuel cell operates at reduced output, and the heating load is mainly supplied by waste heat recovery and supplemented by gas boilers. During this period, zero gas consumption and zero grid power purchase are achieved, with a renewable energy consumption rate of 100%.

[0082] 14:00-18:00 (early evening peak): Photovoltaic output gradually decreases, while heating and electricity loads increase simultaneously. Priority is given to discharging energy storage to supplement the power gap. Once the SOC drops to 25%, fuel cells are activated to supplement energy, and waste heat continues to provide heating. If there is still a small gap, a small amount of grid power is supplemented, and gas boilers maintain low output as standby.

[0083] 18:00-24:00 (Evening Load Period): Photovoltaic output is 0, and residential load is at its peak. Fuel cells continue to operate, combined with off-peak electricity from the grid for power supply and heating; the hydrogen storage tank pressure drops to 14MPa, and the electric energy storage is charged to 50% using off-peak prices to ensure peak-shaving capacity for the next day.

[0084] Example 2: Multi-energy coupling power supply system for township agricultural product processing industrial parks 1. Application Scenarios This embodiment is applied to an agricultural product processing industrial park in a township, with a total building area of ​​110,000 square meters. It includes production and processing workshops, cold chain storage, office facilities, and staff dormitories. It has diverse loads such as production power, process heating, cold chain refrigeration, domestic hot water, and cooking gas. The load fluctuates greatly during the day and is highly sensitive to the reliability of energy supply and operating costs.

[0085] 2. System Core Configuration This embodiment fully adopts the system architecture and efficient integration and interaction method of the present invention, and the core equipment configuration is as follows: Renewable energy generation units: 5MW rooftop photovoltaic system, 1MW permanent magnet direct-drive wind turbine generator; Energy storage unit: 1MW / 2MWh lithium iron phosphate energy storage system; Hydrogen energy coupling unit: 800kW alkaline electrolyzer, 300Nm 3 High-pressure hydrogen storage tank assembly, 500kW PEM fuel cell; Gas supply unit: 1.5MW regenerative micro gas turbine, 2t / h gas boiler; Cold and heat energy supply unit: 2MW double-effect lithium bromide absorption chiller, 3MW screw electric chiller, 2MW electrode electric boiler, and equipped with multi-stage waste heat recovery device; Centralized collaborative management and control platform: The four-layer architecture platform described in this invention is equipped with a multi-time-scale digital twin scheduling system.

[0086] 3. Operational characteristics The system smooths out load fluctuations during production shifts through bidirectional electricity-hydrogen conversion. During peak solar power generation in the daytime, hydrogen is produced and stored, and during peak production at night, hydrogen is converted into electricity to supplement energy. Waste heat from the gas turbine is matched to the needs of process heating, cold chain refrigeration, and domestic hot water according to temperature and grade.

[0087] Example 3: System Operation Example under Extreme Weather Conditions 1. Operating Condition Settings Based on the village-level system in Example 1, extreme weather conditions of three consecutive days of cloudy and rainy weather with no wind were simulated. The total output of renewable energy was less than 8% of the rated installed capacity, which was far below the daily load demand. This was used to verify the system's power supply reliability and fault self-healing capability.

[0088] 2. Operation control process Automatic operating condition identification: The central control platform maps the operating status in real time through a digital twin, combines the weather forecast for the next 3 days, and uses a fuzzy C-means clustering algorithm to automatically determine the "extreme energy shortage condition" and trigger the extreme operating condition scheduling strategy.

[0089] Long-term hydrogen energy storage and release: On the first day, the hydrogen stored in the hydrogen storage tank will be used first, the fuel cell will operate at full output, and electricity and heat will be supplied at the same time, replacing the purchase of electricity from the grid and the consumption of gas; the full amount of hydrogen storage can support the basic living load for about 36 hours.

[0090] Gas-fired combined heat and power (CHP) supplementary energy: After the hydrogen storage pressure drops to the lower limit of 5MPa, the gas turbine + gas boiler CHP mode is started, and all the waste heat of the flue gas is recovered to drive heating / cooling, realizing the cascade utilization of gas energy and reducing gas consumption.

[0091] Flexible load demand response: During periods of energy shortage, agricultural product processing loads are automatically shifted to off-peak periods on the power grid, and non-critical office air conditioning loads are cut off to ensure that residents' basic living energy needs are not affected.

[0092] Fault self-healing verification: When the simulated electric energy storage unit suddenly failed and exited operation, the system completed the fault location within 30 seconds through the fault diagnosis module and automatically switched the power supply path: the fuel cell boosted its output in seconds to smooth power fluctuations, and the gas turbine boosted its output synchronously as a backup, and no power supply interruption occurred throughout the process.

[0093] Comparative Example 1: Traditional Distributed Energy System 1. System Configuration Using the same administrative villages and load levels as in Example 1, the traditional rural energy distribution model was adopted: Electricity: All supplied by the 10kV municipal power grid, with no renewable energy sources or energy storage devices; Heating: Centralized heating via an independent 2t / h gas-fired boiler, without any waste heat recovery device; Refrigeration: Centralized cooling system with independent 700kW electric chillers; Gas supply: Municipal gas is directly connected to villagers' cooking and gas boilers, without energy coupling and conversion.

[0094] 2. Operating Method The four subsystems of electricity, heat, cooling, and gas are planned and operated independently, without any coordinated scheduling mechanism. Each piece of equipment starts and stops independently according to load demand.

[0095] Comparative Example 2: Conventional Combined Heat and Cooling (CHP) System (Prior Technology) 1. System Configuration Using the same administrative village and load level as in Example 1, a typical multi-energy complementary combined heat and power system from the prior art was adopted: Renewable energy and energy storage: Configured with 1.2MW photovoltaic and 300kW / 600kWh energy storage, with parameters completely consistent with Example 1; Energy conversion: Equipped with a 1MW gas turbine + lithium bromide absorption chiller to achieve combined gas-electricity-heat-cooling; Key differences: It lacks a hydrogen energy bidirectional coupling unit and does not have the capability for bidirectional long-term energy storage between electricity and gas; it adopts traditional day-ahead static scheduling, without multi-timescale rolling optimization and without a digital twin closed-loop correction mechanism.

[0096] 2. Operating Method Photovoltaic power is prioritized for electricity supply, and surplus electricity is used to charge energy storage. Once the energy storage is fully charged, the photovoltaic power is directly curtailed. Gas turbines are started and stopped according to the day-ahead load plan, and waste heat is used for cooling and heating. There is no cross-time scale gas-electric synergy or multi-energy path optimization.

[0097] Simulations were conducted for Example 1, Comparative Example 1, and Comparative Example 2 under the same load level and the same annual meteorological data. The comparison of core performance indicators is shown in the table below: The absorption rate of Embodiment 1 of this invention is 21.4 percentage points higher than that of Comparative Embodiment 2, with the core gain coming from the hydrogen energy bidirectional coupling unit. Existing technologies rely only on short-term electrochemical energy storage, and once the storage is fully charged, the solar energy can only be wasted, making it impossible to transfer energy across days or time periods. In contrast, this invention converts surplus solar power during the day into hydrogen for long-term storage through a bidirectional "electricity → hydrogen → electricity" conversion, completely solving the problem of time mismatch between renewable energy output and load, and achieving a high absorption rate.

[0098] The overall energy efficiency of this invention is 33.5 percentage points higher than that of Comparative Example 1 and 18.4 percentage points higher than that of Comparative Example 2. This is due to two main factors. First, the three-stage waste heat utilization architecture matches the waste heat from gas and fuel cells to the cooling, heating, and water preheating needs according to temperature grade, avoiding the waste of high-grade heat energy being used at a low grade. Second, the system benefits from dynamic optimization of multi-energy paths, allowing it to select the most energy-efficient energy supply combination in real time. In contrast, traditional distributed energy supply systems involve unidirectional energy conversion and cumulative losses, resulting in the lowest energy efficiency.

[0099] Compared with Comparative Example 1, the annual operating cost of this invention is reduced by 34.5%, and carbon emissions are reduced by 60.2%; compared with Comparative Example 2, the annual operating cost is reduced by 20.8%, and carbon emissions are reduced by 40.6%. The benefits mainly come from three parts: first, a high proportion of renewable energy directly replaces grid-purchased electricity and gas consumption; second, multi-objective optimized scheduling realizes peak-valley arbitrage and optimal gas allocation; and third, deep waste heat recovery reduces the additional energy consumption for heating and cooling.

[0100] This invention significantly improves energy supply reliability by employing a four-level energy redundancy system of "electric energy storage + hydrogen energy storage and release + gas backup + flexible load" combined with a fault self-healing reconfiguration strategy. In extreme scenarios with no renewable energy sources or grid failures, it can independently guarantee basic energy supply for more than 72 hours relying on hydrogen storage, which is far superior to existing technical solutions and is particularly suitable for weak grid scenarios such as rural and remote areas.

[0101] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computers or available storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-source interactive energy operation optimization system, characterized in that, It includes renewable energy power generation units, electric energy storage units, hydrogen energy coupling units, gas supply units, cascade utilization units of cold and heat energy, multi-load units, and a collaborative management and control platform; The renewable energy generation unit, the energy storage unit, and the hydrogen energy coupling unit are all connected to the AC / DC hybrid bus to realize the collection of multiple power sources and bidirectional throughput. The hydrogen energy coupling unit is equipped with multiple coupling interfaces for electricity, gas, and waste heat, and integrates equipment for electric hydrogen production, hydrogen storage, hydrogen power generation, and waste heat recovery, realizing bidirectional coupling and conversion of multiple energy sources such as electricity, hydrogen, heat, and gas. The waste heat output end of the hydrogen energy coupling unit is connected to the cold and heat energy cascade utilization unit. The cold and heat energy cascade utilization unit builds a three-level waste heat reuse architecture of high temperature refrigeration, medium temperature heating and low temperature preheating. The cold and heat energy cascade utilization unit respectively accepts the waste heat medium of hydrogen energy coupling unit and gas supply unit, and centrally completes the step-by-step matching and recycling of multi-grade waste heat, and outputs cold and heat energy to the outside in a unified manner. The gas supply unit is equipped with multiple independent gas supply circuits. One circuit is connected to the power generation equipment to produce high-temperature flue gas and waste heat, which is then connected to the cold and heat energy cascade utilization unit as a high-temperature heat source input. The remaining circuits are used for peak-shaving heating and end-user gas supply, respectively. The multi-load unit includes rigid loads and flexibly adjustable loads, supporting load shifting and tiered switching. The multi-load unit is connected to the AC / DC hybrid bus, the cold and heat energy cascade utilization unit, and the gas supply unit, respectively, and receives electrical energy, cold energy, heat energy, and gas supply output. The collaborative management and control platform is connected to renewable energy power generation units, electric energy storage units, hydrogen energy coupling units, gas supply units, cold and heat energy cascade utilization units, and multi-load units. It integrates data sensing, digital twin modeling, multi-objective optimization scheduling and fault self-healing functions, collects the operation data of each unit and issues control commands to coordinate and manage the energy equipment and interconnected energy flows across the entire domain. The system is based on an AC / DC hybrid network, multi-directional hydrogen energy coupling and waste heat cascade utilization architecture to achieve closed-loop collaborative interactive operation of multiple energy flows.

2. A multi-source interactive energy operation optimization method, characterized in that, The multi-source interactive energy operation optimization system according to claim 1 includes the following steps: S1. Collect system power, heat, cold, gas, hydrogen operation data and meteorological data in a unified manner, complete clock alignment, data cleaning and dimension normalization processing, and combine deep learning models to complete the graded rolling prediction of new energy output. S2. Construct a multi-energy flow mechanism simulation model, correct the model parameters online based on real-time operating data, and establish a synchronous mapping relationship between the physical system and the virtual model; classify the typical operating conditions of the system by combining clustering algorithms with operating characteristics. S3. With operating costs, carbon emissions, and curtailment rate as optimization objectives, and matching energy balance constraints and equipment operation constraints, the scheduling weights and control strategies are dynamically adjusted based on real-time operating conditions to establish a three-level nested scheduling mechanism of day-ahead pre-scheduling, intraday rolling optimization, and real-time correction. S4. Set a graded priority strategy for energy surplus consumption and energy gap supplementation, and complete multi-source energy matching and flexible load regulation based on the principles of waste heat cascade matching and temperature range matching. S5. Performs hourly operation deviation correction and daily full parameter iteration update of the model; completes fault identification by combining wavelet packet feature extraction with SVM classifier, and matches corresponding power compensation, load regulation, energy transfer and self-healing reconfiguration strategies for faults in core equipment such as energy storage, refrigeration and hydrogen production to maintain continuous system operation.

3. The energy operation optimization method for multi-source interaction according to claim 2, characterized in that, S1 uses the PTP protocol to achieve global clock alignment, uses the 3σ criterion and sliding filter to reduce data noise and impurities, and uses the min-max algorithm to normalize data. It uses the LSTM model to perform long-period day-ahead forecasting and short-period ultra-short-term rolling forecasting respectively, and outputs the new energy power output forecasting results.

4. The energy operation optimization method for multi-source interaction according to claim 2, characterized in that, S2 constructs a multi-energy flow mechanism model based on energy and mass conservation, and uses the recursive least squares method to correct equipment efficiency and pipeline loss parameters online. It also uses the fuzzy C-means clustering algorithm to classify four operating states: normal operating condition, new energy power generation condition, power supply gap condition, and equipment failure condition.

5. The energy operation optimization method for multi-source interaction according to claim 2, characterized in that, In S3, the day-ahead scheduling uses an improved NSGA-III algorithm to solve for the optimal solution set of multiple objectives, and combines the TOPSIS algorithm to select scheduling schemes. Intraday rolling optimization corrects short-term output fluctuations, and real-time correction control uses MPC model predictive control to smooth out system power deviations.

6. The energy operation optimization method for multi-source interaction according to claim 2, characterized in that, In S4, when there is an energy surplus, redundant energy is consumed according to the priority of load absorption, energy storage charging, electrolysis hydrogen production, waste heat replacement for energy supply, and surplus electricity grid connection. When there is an energy shortage, the energy supply gap is filled according to the priority of energy storage discharge, hydrogen power generation, gas combined cycle power, grid power purchase, and flexible load shedding. Waste heat resources are reused layer by layer according to the corresponding demand based on the temperature range.

7. The energy operation optimization method for multi-source interaction according to claim 2, characterized in that, S5 performs hourly operational deviation correction and daily full parameter iteration updates of the model. Fault identification is achieved by combining wavelet packet feature extraction with SVM classifier. For faults in core equipment such as energy storage, refrigeration, and hydrogen production, corresponding power compensation, load regulation, and energy transfer self-healing reconfiguration strategies are matched to maintain continuous system operation.

Images