A method and system for collaborative control of energy distribution of a deep-sea energy island, an electronic device and a storage medium
By introducing modular units and predictive control methods into the deep-sea energy island system, the supply-demand mismatch caused by the volatility of renewable energy has been solved, multi-objective optimized control has been achieved, and the coordinated control efficiency and system stability of energy distribution have been improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTHEAST UNIV
- Filing Date
- 2026-03-16
- Publication Date
- 2026-06-09
AI Technical Summary
Existing technologies cannot effectively solve the volatility problem of renewable energy sources such as deep-sea wind power and photovoltaic power generation, resulting in a mismatch between energy supply and demand in time and space, making it impossible to achieve coordinated control of multi-objective and multi-type energy island systems, and affecting the stability and efficiency of the system.
Multiple modular units are defined using a programmable logic controller (PLC), including wind, solar and wave power generation units, forecasting units, energy storage and power distribution units, electrolysis hydrogen production units, ammonia and alcohol storage units, and a central controller. By monitoring and forecasting data in real time, the action commands of each unit are optimized and controlled to achieve multi-objective optimization control.
It has enhanced the grid connection and absorption capacity of deep-sea energy islands, maximized the efficiency of coordinated control of energy distribution, and ensured the stability and efficient operation of the system.
Smart Images
Figure CN122178418A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of energy distribution systems for deep-sea energy islands, and specifically to a collaborative control method, system, electronic device, and storage medium for energy distribution on deep-sea energy islands. Background Technology
[0002] Against the backdrop of "dual carbon" (carbon dioxide, carbon emissions, and carbon sequestration), my country's new energy sector has developed rapidly, but overall energy supply remains limited. With persistently strong electricity demand, challenges such as temporal and spatial mismatches between energy supply and demand have emerged. Developing deep-sea hydropower, as an effective solution, is a key direction for expanding the new energy sector, and its utilization value is increasingly prominent. Against this backdrop, various offshore energy distribution schemes have emerged, utilizing offshore wind and solar power through offshore photovoltaic power generation and other methods.
[0003] Due to the volatile nature of renewable energy output caused by weather and other factors, improving the grid integration capacity and operational efficiency of deep-sea wind and solar power requires coordinated control of various modules. This necessitates real-time monitoring and rapid response to the energy island's operational status, effectively mitigating instability caused by wind and solar fluctuations, and ensuring the security of the energy island system's internal coordinated control. Currently, most solutions only address single power balance or economic optimization, failing to achieve multi-objective and multi-type control in complex systems. Therefore, this invention proposes a coordinated control method, system, electronic equipment, and storage medium for energy distribution in deep-sea energy islands. Summary of the Invention
[0004] To address the problems mentioned above in the background technology, this invention proposes a collaborative control method, system, electronic device, and storage medium for energy distribution on a deep-sea energy island based on a programmable logic controller (PLC).
[0005] The technical solution adopted in this invention is:
[0006] A collaborative control method for multi-type energy distribution in deep-sea energy islands includes the following steps:
[0007] S1. Define multiple modular control units in the programmable control system, namely, wind, solar, and wave energy generation units, prediction units, energy storage and power distribution units, electrolysis hydrogen production units, ammonia and alcohol storage units, and a central controller. Develop coordinated control rules for each unit, specifying the action commands under different operating conditions.
[0008] S2. Acquire real-time monitored parameters and input them into the central controller, including wind, solar, and wave power generation P0, power generation equipment parameters a0, and real-time hydrogen production power P. h0 Ammonia and methanol production rate v0, battery state of charge SOC0.
[0009] S3. Using the prediction unit, based on the input weather forecast data, historical power generation data, and real-time monitoring parameters obtained in S2, obtain the power generation equipment parameters a and real-time hydrogen production power P corresponding to the system reaching optimal performance. h Ammonia and methanol production rate v, and battery state of charge (SOC).
[0010] S4. Multi-objective optimization control. This considers the electrolyzer efficiency η. d With load power P h Efficiency varies; it is higher within the 50%-80% rated power range, but decreases under excessively low or high loads. Therefore, the electrolytic cell power P is adjusted during optimization. d η must be considered simultaneously d and P h To mitigate the impact of hydrogen production, the electrolyzer power is prioritized for allocation within its high-efficiency range, as expressed by the function P. d = g1(η d ,P h ).
[0011] S5. Based on the given values a, Pd, v, and SOC of the power generation equipment parameters, electrolytic cell operating power, ammonia production rate, methanol production rate, and battery state of charge, as well as the measured values a0, Pd0, v0, and SOC of the power generation equipment parameters, electrolytic cell operating power, ammonia production rate, methanol production rate, and battery state of charge at the current moment, the difference between the given value and the measured value is taken as the deviation DV of each parameter. If adjustment is required, the parameter control module is executed; otherwise, no adjustment is performed. Finally, a control command is generated and issued to achieve the system requirements.
[0012] The present invention also provides a collaborative control system for a multi-type energy distribution system for a deep-sea energy island, including a real-time data acquisition module, a prediction module, a parameter control module, and a system output module;
[0013] The real-time data acquisition module is used to acquire parameters monitored in real time by each module, including wind, solar, and wave power generation, power generation equipment parameters, real-time hydrogen production power, ammonia and methanol production rates, battery state of charge, and corresponding electrolyzer operating power and battery charging and discharging power.
[0014] The prediction module is used to determine the power generation equipment parameters, real-time hydrogen production power, ammonia production rate, methanol production rate, and battery state of charge corresponding to the system reaching optimal performance based on real-time monitoring parameters.
[0015] The parameter control module is used to: 1. determine the deviation based on the measured and given values of the power generation equipment parameters, electrolytic cell operating power, ammonia production rate, methanol production rate, and battery charging and discharging power; 2. output control commands to implement control based on the deviation value and PID control parameters to meet system requirements;
[0016] The system output module is used to output real-time monitoring parameters and parameter control data of each unit of the system according to the needs of energy island system regulation.
[0017] The above modules can be embedded in the programmable controller in hardware form or stored in the computer memory in software form, so that the controller can call and execute the corresponding operations of the above modules.
[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described collaborative control method for a multi-type energy distribution system for a deep-sea energy island.
[0019] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described collaborative control method for a multi-type energy distribution system for a deep-sea energy island.
[0020] Compared with the prior art, the beneficial effects of the present invention are:
[0021] This invention defines multiple modules based on a programmable logic controller (PLC): a wind, solar, and wave energy generation unit, a forecasting unit, an energy storage and distribution unit, an electrolytic hydrogen production unit, an ammonia and methanol storage unit, and a central controller. It designs a collaborative control method for the distribution of multiple energy types in deep-sea energy islands, achieving visualization of energy distribution. Based on the given and measured values of power generation equipment parameters, electrolyzer operating power, ammonia and methanol production rates, and battery charging and discharging power, the method calculates the deviation between the given and measured values and controls the system to meet grid load requirements, thereby improving the grid integration and absorption capacity of deep-sea energy and maximizing the efficiency of collaborative energy distribution control. Attached Figure Description
[0022] Figure 1 This is a flowchart of the method described in this invention;
[0023] Figure 2 This is the logic control diagram of the method described in this invention. Detailed Implementation
[0024] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.
[0025] Figure 1 This is a flowchart of the collaborative control method for multi-type energy distribution in deep-sea energy islands provided by the present invention. Figure 2 This is a control logic diagram. Taking a system with 450MW of offshore wind power, 50MW of offshore photovoltaic power, and an additional 2MW of wave energy power as an example, the process is as follows:
[0026] S1: This example system includes: wind, solar and wave power generation units (450MW offshore wind power, 50MW offshore photovoltaic power, and an additional 2MW wave power generation); a prediction unit (using the Transformer prediction algorithm); an energy storage and distribution unit (60MW / 120MWh); an electrolysis hydrogen production unit; an ammonia and alcohol storage unit; and a central controller.
[0027] Furthermore, coordinated control rules are formulated for each unit, clarifying the action instructions under different operating conditions. This invention divides the system operating conditions into start-up / shutdown conditions and power setting conditions. The specific control rules for each unit are as follows:
[0028] (a) Wind, solar, and wave energy generation units. Start-up and shutdown: When the power generation exceeds the rated start-up power for 3 minutes, the start-up procedure is executed, and the power is increased at a constant rate of 0.3 MW / min; when the power generation is lower than the rated shutdown power for 5 minutes, or when equipment malfunctions and an emergency shutdown order is received, the shutdown action is executed, and the circuit breaker is tripped. Power setting: Based on the wind, solar, and wave energy generation power P when the system reaches optimal performance as output by the prediction module, the power generation equipment parameter a is adjusted. When, the power is increased; when At that time, the power is reduced.
[0029] (b) Prediction Unit. Using the Transformer model, based on the input weather forecast data, historical power generation data and real-time monitoring parameters, the system outputs the predicted power generation results of wind power, photovoltaic and wave energy for future periods, and performs optimization decisions to solve for the optimal control scheme of each unit.
[0030] (c) Energy Storage and Power Distribution Unit. Batteries are used as the energy storage and power distribution method. Start-up and shutdown: The battery management system controls battery charging and discharging. During startup, the contactor closes, entering standby mode; during shutdown, the power drops to 0, the contactor opens, and the unit enters sleep mode. Power setting: Based on the given state of charge (SOC) and charging / discharging power P... elec Execution. When P elec0 When P > 0 and SOC0 < 90%, charging begins; when P elec0 Discharge occurs when |P| < 0 and SOC0 > 20%; elec0 If the power consumption is less than 0.1MW, or the state of charge (SOC) exceeds the limit, charging and discharging will be stopped.
[0031] (d) Electrolysis Hydrogen Production Unit. Start-up and Shutdown: During startup, the system preheats and pressurizes, increasing power to the minimum operating power; during shutdown, power drops to 0, the system vents hydrogen, and the pressure decreases to atmospheric pressure. Power Setting: Based on the hydrogen production power P... h With the operating power P of the electrolytic cell d To regulate and control P d0 = Pd .
[0032] (e) Ammonia and Methanol Storage Unit. Start-up and Shutdown: During startup, adjust the temperature and pressure to the rated values and introduce a suitable ratio of N2 (CO2) and H2 (H2 to N2 ratio is 3:1, H2 to CO2 ratio is 3:1); during shutdown, cut off the gas supply and reduce the temperature and pressure. Power Setting: Adjust the N2 (CO2) inlet valve according to the feed rate of ammonia and methanol v, thereby controlling the ratio of N2 (CO2) to H2.
[0033] (f) Central controller. Determines the corresponding operating conditions of each unit and issues commands.
[0034] S2: Obtain the parameters monitored in real time, corresponding to Figure 2 The data acquisition module. After system startup, the central controller collects the following parameters in real time through the data acquisition module: wind, solar, and wave power generation P0, power generation equipment parameter a0, and real-time hydrogen production power P. h0 Ammonia and methanol production rate v0, battery state of charge SOC0.
[0035] S3: Using a prediction unit employing the Transformer algorithm, corresponding to Figure 2 The prediction module takes weather forecast data, historical power generation data, and real-time monitoring parameters as input to obtain the power generation equipment parameters 'a' and real-time hydrogen production power 'P' corresponding to the system reaching optimal performance. h Ammonia and methanol production rate v, and battery state of charge (SOC).
[0036] Furthermore, step S3 includes:
[0037] S31: Collect real-time monitoring data on wind, solar, and wave power generation P0, power generation equipment parameters a0, and real-time hydrogen production power P. h0 The data includes ammonia and methanol production rates (v0), battery state of charge (SOC0), weather forecast data, and historical power generation data, all of which are normalized. Input vector The construction is as follows:
[0038]
[0039]
[0040] A data point is recorded every 15 minutes. The historical power generation data is 288 characters long, corresponding to the past 72 hours; the weather forecast data is 192 characters long, corresponding to the next 48 hours; and the time feature data is 96 characters long, corresponding to the next 24 hours. P_wind_hist is the historical wind power data, P_pv_hist is the historical photovoltaic data, P_wave_hist is the historical wave energy data, V_wind_pred is the wind speed forecast, I_solar_pred is the solar radiation forecast, and H_wave_pred is the wave height forecast.
[0041] S32: Obtain the predicted power generation values of wind, solar, and wave energy for the next 24 hours, one point every 15 minutes. The predicted result vector Y(t) is constructed as follows:
[0042]
[0043]
[0044]
[0045] This process involves the Transformer model learning the complex nonlinear relationship between historical data and weather forecasts to establish a relationship from input to output, denoted by F:
[0046]
[0047] S33: Based on the power generation prediction results, and with the goal of optimizing system performance and maximizing energy utilization, establish an optimization model to solve for the optimal operating setpoints of each unit. Output the given power generation equipment parameter a and hydrogen production power P. h Ammonia and methanol production rate v, and battery state of charge (SOC).
[0048] The optimization process needs to meet the following constraints:
[0049] (a) Power balance: P + P elec = P d + Other loads
[0050] (b) Equipment operating limits: The parameters of each piece of equipment must not exceed their rated range.
[0051] (c) Battery State of Energy Limits: 20% < SOC < 90%
[0052] A model predictive control (MPC) framework is adopted, and rolling optimization is performed every 15 minutes. The optimization problem is solved using the sequential quadratic programming (SQP) algorithm, which can obtain the optimal solution within 1 minute, meeting the real-time control requirements.
[0053] S4: Multi-objective optimization control. This considers the electrolyzer efficiency η.d With load power P h Efficiency varies; it is higher within the 50%-80% rated power range, but decreases under excessively low or high loads. Therefore, the electrolytic cell power P is adjusted during optimization. d η must be considered simultaneously d and P h To mitigate the impact of hydrogen production, the electrolyzer power is prioritized for allocation within its high-efficiency range, as expressed by the function P. d = g1(η d ,P h )
[0054] S5: Control loop execution, corresponding to Figure 2 Parameter control module. Based on the given values a and P of the power generation equipment parameters, electrolyzer operating power, ammonia production rate, methanol production rate, and battery state of charge. d The values of v, SOC, and the measured values of current power generation equipment parameters, electrolyzer operating power, ammonia production rate, methanol production rate, and battery state of charge a0, P d0 The parameters v0 and SOC are obtained by taking the difference between the given value and the measured value as the deviation DV of each parameter. If adjustment is required, the parameter control module is executed; otherwise, no adjustment is performed. Finally, control commands are generated and issued to achieve the system requirements.
[0055] The present invention also provides a collaborative control system for a multi-type energy distribution system for a deep-sea energy island, including a real-time data acquisition module, a prediction module, a parameter control module, and a system output module;
[0056] The real-time data acquisition module is used to acquire parameters monitored in real time by each module, including wind, solar, and wave power generation, power generation equipment parameters, real-time hydrogen production power, ammonia and methanol production rates, battery state of charge, and corresponding electrolyzer operating power and battery charging and discharging power.
[0057] The prediction module is used to determine the power generation equipment parameters, real-time hydrogen production power, ammonia production rate, methanol production rate, and battery state of charge corresponding to the system reaching optimal performance based on real-time monitoring parameters.
[0058] The parameter control module is used to: 1. determine the deviation based on the measured and set values of the power generation equipment parameters, electrolyzer operating power, ammonia production rate, methanol production rate, and battery charging / discharging power; 2. output control commands to implement control based on the deviation value and PID control parameters to meet system requirements.
[0059] The system output module is used to output real-time monitoring parameters and parameter control data of each unit of the system according to the needs of energy island system regulation.
[0060] The above modules can be embedded in the programmable controller in hardware form or stored in the computer memory in software form, so that the controller can call and execute the corresponding operations of the above modules.
[0061] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described collaborative control method for a multi-type energy distribution system for a deep-sea energy island.
[0062] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described collaborative control method for a multi-type energy distribution system for a deep-sea energy island.
[0063] The method and specific implementation of the present invention have been described in detail above. Of course, in addition to the examples described above, the present invention may have other embodiments, and all technical solutions formed by equivalent substitution or equivalent transformation fall within the scope of protection of the present invention.
Claims
1. A collaborative control method for multi-type energy distribution in deep-sea energy islands, characterized in that, Includes the following steps: S1. Define multiple modular control units in the programmable control system, namely wind, solar and wave energy generation unit, prediction unit, energy storage and power distribution unit, electrolysis hydrogen production unit, ammonia and alcohol storage unit and central controller; and formulate collaborative control rules for each unit to clarify the action instructions under different operating conditions. S2. Acquire real-time monitored parameters and input them into the central controller, including wind, solar, and wave power generation P0, power generation equipment parameters a0, and real-time hydrogen production power P. h0 Ammonia and methanol production rate v0; battery state of charge SOC0; S3. Using the prediction unit, based on the input weather forecast data, historical power generation data, and real-time monitoring parameters obtained in S2, obtain the power generation equipment parameters a and real-time hydrogen production power P corresponding to the system reaching optimal performance. h Ammonia and methanol production rate v; battery state of charge (SOC); S4. Multi-objective optimization control, taking into account the electrolyzer efficiency η d With load power P h Efficiency varies; it is high within 50%-80% of rated power, but decreases under excessively low or high loads. Therefore, the electrolytic cell power P is optimized accordingly. d η must be considered simultaneously d and P h To mitigate the impact of hydrogen production, the electrolyzer power is prioritized for allocation within its high-efficiency range, as expressed by the function P. d = g1(η d , P h ); S5. Based on the given values a, Pd, v, and SOC of the power generation equipment parameters, electrolytic cell operating power, ammonia production rate, methanol production rate, and battery state of charge, as well as the measured values a0, Pd0, v0, and SOC of the power generation equipment parameters, electrolytic cell operating power, ammonia production rate, methanol production rate, and battery state of charge at the current moment, the difference between the given value and the measured value is taken as the deviation DV of each parameter. If adjustment is required, the parameter control module is executed; otherwise, no adjustment is performed. Finally, a control command is generated and issued to achieve the system requirements.
2. The collaborative control method for multi-type energy distribution in deep-sea energy islands according to claim 1, characterized in that, The system operating conditions are divided into start-up / shutdown conditions and power setting conditions. The specific control rules for each unit are as follows: (a) Wind, solar, and wave power generation units; Start-up and shutdown: When the power generation is greater than the rated start-up power for 3 minutes, the start-up procedure is executed, and the power is increased at a constant rate of 0.3 MW / min; When the power generation is lower than the rated shutdown power for 5 minutes, or when the equipment malfunctions and an emergency shutdown order is received, the shutdown action is executed, and the circuit breaker is tripped; Power setting: Based on the wind, solar, and wave power generation P when the system reaches optimal performance as output by the prediction module, the power generation equipment parameter a is adjusted. When, the power is increased; when At that time, the power is reduced; (b) Prediction Unit: Using the Transformer model, based on the input weather forecast data, historical power generation data and real-time monitoring parameters, it outputs the prediction results of wind power, photovoltaic and wave power generation for future periods, and makes optimization decisions to solve the optimal control scheme for each unit; (c) Energy storage and power distribution unit; using batteries as the energy storage and power distribution method; start-up and shutdown: the battery management system controls the charging and discharging of the batteries; When starting, the contactor closes, entering standby mode; when stopping, the power drops to 0, the contactor opens, and the system enters sleep mode; power setting: based on the given state of charge (SOC) and charging / discharging power (P). elec Execute; when P elec0 When P > 0 and SOC0 < 90%, charging begins; when P elec0 Discharge occurs when |P| < 0 and SOC0 > 20%; elec0 If the power consumption is less than 0.1MW, or the state of charge (SOC) exceeds the limit, charging and discharging will be stopped. (d) Electrolysis hydrogen production unit; Start-up and shutdown: During startup, the system is preheated and pressurized, increasing the power to the minimum operating power; during shutdown, the power drops to 0, the system vents hydrogen, and the pressure drops to atmospheric pressure; Power setting: Based on the hydrogen production power P... h With the operating power P of the electrolytic cell d To regulate and control P d0 = P d ; (e) Ammonia and methanol storage unit; Start-up and shutdown: When starting, adjust the temperature and pressure to the rated values and introduce N2 (CO2) and H2; when shutting down, cut off the gas and reduce the temperature and pressure; power setting: adjust the N2 (CO2) inlet valve according to the feed rate v of ammonia and methanol, thereby controlling the ratio of N2 (CO2) and H2; (f) Central controller; determines the corresponding operating conditions of each unit and completes the instruction issuance.
3. The collaborative control method for multi-type energy distribution in deep-sea energy islands according to claim 1, characterized in that, Step S3 specifically includes: S31: Collect real-time monitoring data on wind, solar, and wave power generation P0, power generation equipment parameters a0, and real-time hydrogen production power P. h0 The data includes ammonia and methanol production rates (v0), battery state of charge (SOC0), weather forecast data, and historical power generation data, which are then normalized. The input vector... The construction is as follows: A data point is recorded every 15 minutes. The historical power generation data is 288 characters long, corresponding to the past 72 hours; the weather forecast data is 192 characters long, corresponding to the next 48 hours; the time feature data is 96 characters long, corresponding to the next 24 hours; P_wind_hist is the historical wind power data, P_pv_hist is the historical photovoltaic data, P_wave_hist is the historical wave energy data, V_wind_pred is the wind speed forecast, I_solar_pred is the sunshine forecast, and H_wave_pred is the wave height forecast. S32: Obtain the predicted power generation values of wind, solar, and wave energy for the next 24 hours, one point every 15 minutes; the predicted result vector Y(t) is constructed as follows: This process involves the Transformer model learning the complex nonlinear relationship between historical data and weather forecasts to establish a relationship from input to output, denoted by F: S33: Based on the power generation prediction results, with the goal of optimizing system performance and maximizing energy utilization, establish an optimization model to solve for the optimal operating setpoints of each unit; output the given power generation equipment parameter a and hydrogen production power P. h Ammonia and methanol production rate v, and battery state of charge (SOC).
4. The collaborative control method for multi-type energy distribution in deep-sea energy islands according to claim 3, characterized in that, The optimization process in step S33 satisfies the following constraints: (a) Power balance: P + P elec = P d + Other loads; (b) Equipment operating limits: The parameters of each piece of equipment must not exceed their rated range; (c) Battery state of energy limit: 20% < SOC < 90%; The Model Predictive Control (MPC) framework is adopted, and the optimization is performed every 15 minutes. The optimization problem is solved by the Sequential Quadratic Programming (SQP) algorithm, and the optimal solution is obtained within 1 minute, which meets the real-time control requirements.
5. A collaborative control system for a multi-type energy distribution system in a deep-sea energy island, characterized in that, It includes a real-time data acquisition module, a prediction module, a parameter control module, and a system output module; The real-time data acquisition module is used to acquire the parameters monitored by each module in real time, including wind, solar and wave power generation, power generation equipment parameters, real-time hydrogen production power, ammonia and methanol production rate, battery state of charge and the corresponding electrolyzer operating power and battery charging and discharging power. The prediction module is used to determine the power generation equipment parameters, real-time hydrogen production power, ammonia production rate, methanol production rate, and battery state of charge corresponding to the system reaching optimal performance based on real-time monitoring parameters. The parameter control module is used to:
1. determine the deviation based on the measured and given values of the power generation equipment parameters, electrolytic cell operating power, ammonia production rate, methanol production rate, and battery charging and discharging power; 2. Based on the deviation value and PID control parameters, output control commands to implement control and achieve system requirements; The system output module is used to output real-time monitoring parameters and parameter control data of each unit of the system according to the control needs of the energy island system.
6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method of claim 1.
7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps described above for implementing the method of claim 1.