Distributed energy management system of zero-carbon park based on micro-grid technology

Abstract

The application belongs to the technical field of micro-grid, and discloses a zero-carbon park distributed energy management system based on micro-grid technology, which comprises a distributed renewable energy power generation unit, a multi-type energy storage unit, a flexible adjustable load unit and an energy management master control system. The application integrates renewable energy power generation, multi-type energy storage and flexible load to build an efficient and cooperative energy system; the system adopts an advanced model prediction control algorithm to realize the optimal scheduling of source-grid-load-storage, significantly improve the renewable energy utilization rate and effectively reduce the carbon emission intensity; the multi-time scale energy storage architecture ensures the stability and reliability of system operation, and can quickly switch to island mode in the event of power grid failure; the intelligent digital twin platform provides accurate fault diagnosis and operation and maintenance support, and the modular design gives the system good expansibility.

Description

Technical Field

[0001] This invention belongs to the field of microgrid technology, specifically a zero-carbon distributed energy management system for industrial parks based on microgrid technology. Background Technology

[0002] A zero-carbon industrial park distributed energy management system based on microgrid technology is a comprehensive energy solution integrating renewable energy generation, multiple types of energy storage, flexible load regulation, and intelligent energy management. Through localized energy production, storage, consumption, and collaborative optimization, it aims to achieve the strategic goals of energy self-sufficiency and net-zero carbon emissions within the industrial park. Based on a microgrid architecture, the system integrates advanced technologies such as model predictive control (MPC), artificial intelligence power prediction, and blockchain-based green electricity trading. It can dynamically coordinate distributed energy resources within the park, ensuring an optimal balance between economy, reliability, and low carbon emissions. However, the application of existing microgrid technology in zero-carbon industrial parks still faces numerous technical and management challenges.

[0003] First, renewable energy is characterized by significant volatility and intermittency, which can easily lead to power imbalances within the park. The reliance on energy storage systems and backup power sources increases the complexity of system design and construction costs. Second, existing multi-energy coupling coordination control strategies are at high risk of voltage instability and frequency drift in islanded mode. The energy management algorithm lacks real-time performance and global optimization capabilities, which can easily lead to delayed dispatch response.

[0004] Furthermore, existing power electronic equipment is prone to harmonic interference during grid-connected and off-grid switching, increasing the technical requirements for filtering and protection devices. In terms of economics, microgrid systems have high initial investment and long construction cycles, and the carbon trading market mechanism is still immature, making it difficult to effectively realize the economic benefits of green energy. At the management level, point-to-point green electricity trading lacks unified metering and settlement standards, cross-regional microgrid projects face compatibility and policy approval challenges, and the system security is insufficient, making it vulnerable to cyberattacks. Some industrial parks have low participation in intelligent operation and maintenance and demand response, and the dynamic electricity pricing mechanism and profit distribution model are not yet perfect. These problems jointly restrict the comprehensive promotion and efficient application of existing microgrids in zero-carbon industrial parks. Summary of the Invention

[0005] The purpose of this invention is to provide a zero-carbon distributed energy management system for industrial parks based on microgrid technology, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a zero-carbon distributed energy management system for industrial parks based on microgrid technology, which includes: distributed renewable energy generation units, multiple types of energy storage units, flexible and adjustable load units, and an energy management control system;

[0007] Distributed renewable energy generation units: used to provide clean electricity for the park and connected to the energy management control system via signal transmission;

[0008] Multiple types of energy storage units: used to collaboratively achieve energy time shifting, power buffering, long-term backup and carbon emission reduction; electrochemical energy storage smooths out fluctuations from seconds to days, and hydrogen energy storage solves cross-seasonal energy transfer;

[0009] Flexible and adjustable load unit: Relying on the load aggregation control platform, load data is collected in real time through edge computing terminals. Combined with the MPC algorithm, the load curve is dynamically adjusted to achieve source-load interaction optimization while meeting user comfort or travel needs.

[0010] Energy management control system: Based on the collected power generation, energy storage status and load data, it performs energy prediction, optimized scheduling and real-time control.

[0011] Preferably, the energy management control system includes:

[0012] The renewable energy power generation forecasting module, based on an LSTM neural network model, processes input meteorological data and outputs a predicted power generation value for the next 24 hours.

[0013] ;

[0014] In the formula, These represent the irradiance, wind speed, and ambient temperature at time t, respectively.

[0015] Among them, the calculation of energy management string capacity and number of components

[0016] The number N of components in a single string is determined by the inverter input voltage range and component parameters:

[0017] ;

[0018] In the formula:

[0019] Inverter maximum input voltage

[0020] Safety margin

[0021] Open-circuit voltage of the component at the lowest ambient temperature:

[0022] ;

[0023] In the formula, Open-circuit voltage temperature coefficient

[0024] Standard test temperature

[0025] String power matching;

[0026] Total power of the string: Inverter tolerances must be met:

[0027] ;

[0028] In the formula, Component nominal maximum power:

[0029] String mismatch loss coefficient.

[0030] Preferably, the energy management control system aims to minimize carbon emissions and operating costs.

[0031] ;

[0032] in, Purchase electricity at time t. For grid electricity price, Let be the carbon emissions at time t. These are the weighting coefficients;

[0033] Actual power generation Irradiation ,temperature Influence:

[0034] ;

[0035] Power temperature coefficient;

[0036] Standard test irradiance;

[0037] String efficiency monitoring:

[0038] String performance ratio Used for fault diagnosis:

[0039] ;

[0040] in, For theoretical power, if This will trigger an alarm.

[0041] Preferably, the energy storage unit's charging and discharging strategy satisfies:

[0042] ;

[0043] In the formula, For charge / discharge efficiency, For energy storage capacity;

[0044] Based on the above, the following formula for diagnosing photovoltaic string faults can be summarized:

[0045] Detection of abnormal string current-voltage characteristics:

[0046] Under normal operating conditions, the string operating point should be close to the maximum power point; if it deviates, a fault may exist.

[0047] ;

[0048] In the formula, MPP current predicted by irradiance and temperature

[0049] Actual measured current

[0050] Shadow occlusion positioning formula:

[0051] If some components in the string are blocked, its output voltage It will rise abnormally:

[0052] ;

[0053] In the formula, Number of components obscured

[0054] The operating voltage of a normal component.

[0055] Preferably, the hydrogen storage unit produces hydrogen through water electrolysis. satisfy:

[0056] ;

[0057] In the formula, This is the priority coefficient for hydrogen production. For load demand.

[0058] Preferably, the adjustment amount of the flexible load satisfy:

[0059] ;

[0060] In the formula For temperature control load adjustment coefficient For the set temperature and the actual temperature.

[0061] Preferably, the energy management main control system monitors the grid voltage and frequency. When the voltage or frequency deviates from a preset range, the system switches to islanding mode within a millisecond response time, and the following power balance must be met in islanding mode:

[0062] ;

[0063] In the formula, The actual power generation capacity of distributed renewable energy generation units at any given time. The discharge power of the energy storage unit at all times. The total power demand of the flexible load units in the park at any given time. The charging power of the energy storage unit at any time.

[0064] Preferably, the carbon emissions are calculated as follows:

[0065] ;

[0066] In the formula, The carbon emission factor for energy unit j. Its output power.

[0067] Preferably, the green electricity trading within the park adopts smart contracts, and the transaction price... Dynamically updated:

[0068] ;

[0069] In the formula, Let be the demand and supply at time t, respectively. This represents the price sensitivity coefficient.

[0070] Preferably, the grid connection or islanding switching logic of the islanding mode is as follows:

[0071] If a grid voltage deviation is detected or frequency deviation ,exist Switch to island mode;

[0072] Based on the above, the basic framework for MPC rolling optimization is as follows:

[0073] MPC uses rolling time-domain optimization to solve for the optimal control sequence in the future prediction time domain in each control cycle, only executing the first step of control input, and then re-optimizing in the next cycle;

[0074] The general form of an optimization problem:

[0075] ;

[0076] In the formula: System status; Control input; Control the time domain.

[0077] The beneficial effects of this invention are as follows:

[0078] 1. This invention constructs an organically coupled structure of distributed renewable energy power generation units, multiple types of energy storage units, and flexible load units, and relies on an energy management control system for unified optimization and scheduling. This significantly improves the energy utilization efficiency and renewable energy absorption capacity within the park. The system can collect the operating status of each unit in real time, flexibly adjust the energy storage charging and discharging and load response strategies, achieve localized energy balance, effectively reduce dependence on the external power grid, improve the park's energy self-sufficiency rate, and provide an efficient and feasible technical path for the construction of zero-carbon parks.

[0079] 2. This invention achieves a deep integration of multi-energy coordinated control and safe operation assurance by introducing a model predictive control rolling optimization algorithm and an islanding mode real-time switching mechanism. The system can switch to islanding mode in milliseconds under abnormal grid conditions to maintain system power balance and effectively avoid the risks of voltage fluctuations and frequency deviations. At the same time, through MPC real-time rolling optimization, the operating status of energy storage and load is dynamically adjusted to ensure that the system can maintain economy and stability under complex operating scenarios, which greatly improves the operational resilience of the microgrid system.

[0080] 3. This invention achieves accurate prediction of renewable energy power generation based on LSTM neural networks and introduces a dynamic green electricity trading mechanism driven by blockchain smart contracts within the park, effectively improving the intelligence and economic efficiency of energy management. The system can flexibly adjust the electricity trading price within the park according to the prediction results and real-time load fluctuations, realize point-to-point energy settlement, reduce transaction settlement risks, and enhance user participation. Through multi-source data fusion and smart contract collaborative application, the system provides an efficient, transparent, and secure support platform for the market-oriented operation of distributed energy. Attached Figure Description

[0081] Figure 1 This is a block diagram of the overall system usage of the present invention. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] like Figure 1 As shown, this embodiment of the invention provides a zero-carbon distributed energy management system for industrial parks based on microgrid technology. The system includes: distributed renewable energy generation units (photovoltaic, wind power), multiple types of energy storage units (electrochemical energy storage, hydrogen energy storage), flexible and adjustable load units (temperature-controlled load, electric vehicles), and an energy management control system (EMS).

[0084] Distributed renewable energy power generation units (photovoltaics, wind power); used to directly replace fossil fuels, provide clean electricity, and support the park's net-zero carbon emission goal;

[0085] Multiple types of energy storage units (electrochemical energy storage, hydrogen energy storage) are used to synergistically achieve energy time shifting, power buffering, long-term backup and carbon emission reduction; electrochemical energy storage smooths out fluctuations from the second to the day, hydrogen energy storage solves cross-seasonal energy transfer, and MPC algorithm optimizes control to ensure power supply reliability and support carbon trading, achieving the triple goals of technology, economy and environmental protection;

[0086] Flexible and adjustable load unit (temperature-controlled load, electric vehicle); relying on the load aggregation control platform, load data is collected in real time through edge computing terminals, and combined with MPC algorithm, the load curve is dynamically adjusted to achieve source-load interaction optimization while meeting user comfort or travel needs;

[0087] The Energy Management Master System (EMS) transforms physical energy systems into computable digital optimization problems and achieves minute-level refined management of energy carbon footprint through a closed loop of "prediction-optimization-execution-verification".

[0088] The system architecture of this invention has good scalability and compatibility, supporting flexible access to various types of new energy equipment such as photovoltaic, wind power, and electric vehicle charging piles. It adapts to the future energy structure adjustment and scale expansion needs of the park. The system makes full use of edge computing terminals to achieve real-time monitoring and rapid response of flexible loads, ensuring user comfort and energy flexibility. Through intelligent diagnostic algorithms such as string performance ratio and photovoltaic current and voltage characteristic anomaly detection, the system can provide early warning of photovoltaic equipment failures or shading problems, improving operation and maintenance management efficiency and reducing operation and maintenance costs. In addition, the system supports minute-level carbon footprint tracking and real-time data visualization, providing data support for park green management, energy conservation and carbon reduction, and carbon trading decisions, further helping enterprises achieve green, low-carbon, and high-quality development goals.

[0089] The energy management control system includes:

[0090] The renewable energy power generation prediction module uses an LSTM neural network model, takes meteorological data (irradiance, wind speed, temperature) as input, and outputs the predicted power generation value for the next 24 hours.

[0091] Among them, the calculation of energy management string capacity and number of components

[0092] The number of components N in a single string is determined by the inverter input voltage range and component parameters.

[0093] ;

[0094] In the formula:

[0095] Maximum input voltage of the inverter (e.g., 1000V)

[0096] Safety margin (usually 10%)

[0097] Open-circuit voltage of the component at the lowest ambient temperature (to be corrected for temperature coefficient):

[0098] ;

[0099] Open-circuit voltage temperature coefficient (e.g., -0.3% / ℃);

[0100] Standard test temperature (25℃).

[0101] String power matching:

[0102] Total power of the string: Inverter tolerances must be met:

[0103]

[0104] Component nominal maximum power (e.g., 550W):

[0105] String mismatch loss factor (usually taken as 0.95~0.98).

[0106] The energy management control system aims to minimize carbon emissions and operating costs.

[0107] ;

[0108] in, Purchase electricity at time t. For grid electricity price, Let be the carbon emissions at time t. These are the weighting coefficients;

[0109] Actual power generation Irradiance ,temperature Influence:

[0110] ;

[0111] Power temperature coefficient (e.g., -0.4% / ℃);

[0112] Standard test irradiance (1000W / m²).

[0113] String efficiency monitoring:

[0114] String performance ratio Used for fault diagnosis:

[0115] ;

[0116] in, The theoretical power (calculated from actual power generation), if This will trigger an alarm.

[0117] The energy storage unit charging and discharging strategy satisfies

[0118] ;

[0119] In the formula, For charge / discharge efficiency, For energy storage capacity

[0120] Based on the above, the formula for diagnosing photovoltaic string faults is summarized.

[0121] Detection of abnormal string current-voltage characteristics:

[0122] Under normal operating conditions, the string operating point should be close to the maximum power point (MPP). If it deviates from this point, a fault may exist.

[0123] ;

[0124] MPP current predicted by irradiance and temperature:

[0125] Actual measured current;

[0126] Shadow Occlusion Positioning Formula

[0127] If some components in the string are blocked, its output voltage It will rise abnormally:

[0128] ;

[0129] Number of components that are obscured;

[0130] The operating voltage of a normal component.

[0131] Application example:

[0132] If the parameters of a photovoltaic string in a zero-carbon industrial park are as follows:

[0133] Components: ;

[0134] environment: ;

[0135] Calculation steps:

[0136] I. Number of strings ;

[0137] II. Real-time Power .

[0138] The hydrogen storage unit generates hydrogen through water electrolysis. satisfy:

[0139] ;

[0140] In the formula, This is the priority coefficient for hydrogen production. For load demand.

[0141] Among them, the adjustment amount of the flexible load satisfy:

[0142] ;

[0143] In the formula For temperature control load adjustment coefficient For the set temperature and the actual temperature.

[0144] In the islanded mode, power balance operation must be satisfied:

[0145] ;

[0146] The carbon emissions are calculated as follows:

[0147] ;

[0148] In the formula, The carbon emission factor for energy unit j. Its output power.

[0149] The green electricity trading within the park utilizes smart contracts, and the transaction price... Dynamically updated:

[0150] .

[0151] In the formula, Let be the demand and supply at time t, respectively. This represents the price sensitivity coefficient.

[0152] The grid connection / islanding switching logic of the islanding mode is as follows:

[0153] If a grid voltage deviation is detected or frequency deviation ,exist Switch to island mode;

[0154] Based on the above, the basic framework for MPC rolling optimization is as follows:

[0155] MPC uses rolling time-domain optimization to solve for the optimal control sequence for the future step (predictive time domain) in each control cycle, executing only the control input for the first step, and then re-optimizing in the next cycle.

[0156] The general form of an optimization problem:

[0157] ;

[0158] System status (e.g., energy storage SOC, load power);

[0159] Control inputs (such as energy storage charging and discharging power, hydrogen production commands);

[0160] Control time domain (usually) ).

[0161] The innovative MPC rolling optimization framework enables an intelligent upgrade of energy management. This system employs a multi-objective optimization algorithm to dynamically solve for the optimal control sequence in the prediction time domain, perfectly coordinating key functions such as carbon emission calculation, green electricity trading pricing, and islanding mode switching. Among these, carbon emission factor weighted calculation ensures environmental benefits, smart contract dynamic pricing enhances economic returns, and millisecond-level islanding switching guarantees power supply security. The MPC framework achieves closed-loop management of "prediction-optimization-execution" in the control time domain by updating system status (such as energy storage SOC) and control inputs (such as hydrogen production commands) in real time. This transforms the passive response of traditional energy systems into proactive optimization. This advanced control strategy not only breaks through the technical bottleneck of renewable energy consumption but also achieves minute-level refined management of carbon footprint through digital empowerment, providing a replicable and scalable benchmark solution for the construction of zero-carbon parks.

[0162] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0163] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A zero-carbon distributed energy management system for industrial parks based on microgrid technology, characterized in that: The system includes: distributed renewable energy generation units, multiple types of energy storage units, flexible and adjustable load units, and an energy management control system; The distributed renewable energy generation unit, the multi-type energy storage unit, and the flexible adjustable load unit are all connected to the energy management main control system for signal transmission. The distributed renewable energy generation unit is used to supply power to the park and to provide power generation data and meteorological data for power generation prediction to the energy management main control system; The multi-type energy storage unit includes an electrochemical energy storage subunit and a hydrogen energy storage subunit; the electrochemical energy storage subunit is used to perform charging and discharging power regulation under the control of the energy management main control system, and update the state of charge according to the charging and discharging efficiency; the hydrogen energy storage subunit is used to produce hydrogen by electrolyzing water under the control of the energy management main control system, and the hydrogen production power is determined according to the hydrogen production priority coefficient and load demand. The flexible adjustable load unit relies on the load aggregation control platform to collect load data in real time through the edge computing terminal and upload it to the energy management main control system. Under the premise of meeting user comfort or travel needs, the load curve is dynamically adjusted in combination with the MPC algorithm to achieve source-load interaction optimization. The flexible adjustable load unit includes temperature-controlled load, and the energy management main control system determines the adjustment amount of the temperature-controlled load based on the set temperature and the actual temperature of the temperature-controlled load. The energy management control system is used for energy prediction, optimized scheduling, and real-time control based on the power generation data, energy storage status, and load data, specifically including: Based on the meteorological data, an LSTM neural network model is used to output the predicted power generation value for the next 24 hours. An optimization target is constructed using the purchased electricity volume, grid electricity price, and carbon emissions. Based on the predicted power generation value, the state of charge of the electrochemical energy storage sub-unit, and the load data of the flexible adjustable load unit, the purchased electricity volume, the charging and discharging power of the electrochemical energy storage sub-unit, the hydrogen production power of the hydrogen energy storage sub-unit, and the adjustment amount of the temperature-controlled load are solved, and corresponding control commands are issued. Monitor grid voltage and frequency. When the voltage or frequency deviates from the preset range, switch to island mode. In island mode, the sum of distributed renewable energy generation power and energy storage discharge power is equal to the sum of the total load power demand of the park and the energy storage charging power.

2. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: The energy management control system includes: The renewable energy power generation forecasting module, based on an LSTM neural network model, processes input meteorological data and outputs a predicted power generation value for the next 24 hours. ； In the formula, These represent the irradiance, wind speed, and ambient temperature at time t, respectively. Among them, the calculation of energy management string capacity and component number is as follows: The number N of components in a single string is determined by the inverter input voltage range and component parameters: ； In the formula: Maximum input voltage of the inverter Safety margin Open-circuit voltage of the component at the lowest ambient temperature: ； In the formula, Temperature coefficient of open-circuit voltage Standard test temperature String power matching; Total power of the string: Inverter tolerances must be met: ； In the formula, The component's nominal maximum power , string mismatch loss coefficient.

3. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: The energy management control system aims to minimize carbon emissions and operating costs, with the objective function being: ； in, Purchase electricity at time t. For grid electricity price, Let be the carbon emissions at time t. These are the weighting coefficients; Actual power generation Irradiation ,temperature Influence: ； Power temperature coefficient; Standard test irradiance; String efficiency monitoring: String performance ratio Used for fault diagnosis: 100%； in, For theoretical power, if This will trigger an alarm.

4. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: The charging and discharging strategies of the various types of energy storage units satisfy the following: ； In the formula, For charging or discharging efficiency, ; Based on the above, the following formula for diagnosing photovoltaic string faults can be summarized: Detection of abnormal string current-voltage characteristics: Under normal operating conditions, the string operating point should be close to the maximum power point; if it deviates, a fault may exist. ； In the formula, MPP current predicted by irradiance and temperature Actual measured current Shadow occlusion positioning formula: If some components in the string are blocked, its output voltage It will rise abnormally: ； In the formula, Number of components that are obscured : The operating voltage of a normal component.

5. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: The hydrogen energy storage subunit in the multi-type energy storage unit generates hydrogen through water electrolysis. satisfy: ； In the formula, This is the priority coefficient for hydrogen production. For load demand.

6. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: Adjustment amount of flexible load unit satisfy: ； In the formula The adjustment coefficient of temperature control load i , For the set temperature and the actual temperature.

7. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: The energy management control system monitors the grid voltage and frequency. When the voltage or frequency deviates from the preset range, the system switches to islanding mode within a millisecond response time. In islanding mode, the following power balance must be met: ； In the formula, : The actual power generation of the distributed renewable energy generation unit at time t The discharge power of the energy storage unit at time t. The total power demand of the flexible load unit in the park at time t. : The charging power of the energy storage unit at time t.

8. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 3, characterized in that: The carbon emissions are calculated as follows: ； In the formula, The carbon emission factor for energy unit j. Its output power.

9. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 1, characterized in that: In the green electricity trading module, the green electricity trading price within the park The transaction price is dynamically updated using smart contracts and meets the following requirements: ； In the formula, Let be the demand and supply at time t, respectively. This represents the price sensitivity coefficient.

10. The zero-carbon distributed energy management system for industrial parks based on microgrid technology according to claim 7, characterized in that: The grid connection or islanding switching logic for the islanding mode is as follows: If a grid voltage deviation is detected or frequency deviation At the preset time Switch to island mode; Based on the above, the basic framework of MPC rolling optimization is written: MPC solves the optimal control sequence in the future prediction time domain in each control cycle through rolling time-domain optimization, only executes the first step of control quantity, and re-optimizes in the next cycle; The general form of an optimization problem: ； In the formula: System status; : Control input; : Control the time domain.

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