Double-layer real-time energy management optimization method and system of ship hybrid power system

Abstract

The invention discloses a double-layer real-time energy management optimization method and system for a ship hybrid power system, and the system comprises an execution layer energy management module which comprises a distributed optimal power distribution module and a distributed cooperative control module, and is used for the control of a fuel cell and a cell energy storage system; the decision-making layer energy management module is used for realizing energy coordination optimization between the fuel cell and the cell energy storage system; and the controller module is used for sending a control signal and receiving feedback information. According to the method, the optimal increment cost and the optimal power of each fuel cell are solved through the fuel dispersion optimal power distribution strategy of the first execution layer according to the equal increment cost criterion, so that the low-cost fuel cell generates more power, the high-cost fuel cell generates less power, and the long-term operation performance is consistent; through battery distributed cooperative control of the second execution layer, dynamic balance of the state of charge is realized, power is distributed according to the capacity proportion, and bus voltage is accurately recovered; and adaptive equal hydrogen consumption minimization is coordinated and optimized through a decision-making layer.

Description

Technical Field

[0001] This invention relates to an energy management optimization method and system, and more particularly to a two-layer real-time energy management optimization method and system for a marine hybrid power system. Background Technology

[0002] The hybrid power system (HPS) of a hydrogen fuel cell vehicle (FCV) typically consists of multiple fuel cell systems (FCS) and multiple battery storage systems (BSS) to meet classification society requirements and ensure power supply for critical loads. Such systems improve stability and safety by integrating different power sources.

[0003] However, existing hybrid power systems face significant challenges in energy management: different types of power sources have different operating characteristics, modes, control objectives, and health states, which places higher demands on the controllability of energy flow between power sources. Furthermore, ship navigation conditions are complex and variable, propulsion load power is difficult to predict accurately, and the output power of each power source varies greatly. Under extreme conditions such as rapid braking and emergency collision avoidance, energy transmission patterns are complex, directly affecting the system's economy, power endurance, and reliability.

[0004] To address the aforementioned issues, existing technologies have proposed various Energy Management Strategies (EMS), which can be mainly categorized into three types: Rule-Based Strategies (RBS), Optimization-Based Strategies (OBS), and Artificial Intelligence-Based Strategies (AIDS). However, these methods all have significant shortcomings: Rule-based strategies, while easy to implement, rely on expert experience for rule setting, resulting in poor output flexibility and unsatisfactory multi-objective optimization effects; Optimization-based strategies, while capable of global or transient optimization, either rely on complete navigation information, making real-time application difficult, or suffer from suboptimal results due to a lack of a global perspective; Artificial Intelligence-based strategies, while capable of autonomous learning, are affected by network structure and reward functions, require extensive data training, and their actual effectiveness remains to be verified.

[0005] Furthermore, existing energy management strategies are mostly designed for simple systems consisting of a single fuel cell system and a battery storage system, and are difficult to apply directly to complex marine hybrid power systems consisting of multiple fuel cell systems and multiple battery storage systems. In particular, systematic and coordinated solutions have not yet been formed in terms of achieving performance consistency, power distribution economy, SOC balance and system efficiency improvement. Summary of the Invention

[0006] Purpose of the invention: This invention proposes a two-layer real-time energy management optimization method and system for marine hybrid power systems, which realizes hierarchical multi-objective optimization and minimizes the total hydrogen consumption of the system while ensuring global reliability.

[0007] Technical solution: This invention includes the following steps:

[0008] S1. The second execution layer collects local status information on the terminal voltage and state of charge of each battery cell;

[0009] S2. The second execution layer uses the average consensus algorithm to enable adjacent battery cells to exchange local state information, collaboratively calculate the updated global average state of charge estimate, and send the value to the decision layer.

[0010] S3. The decision-making level defines the total equivalent hydrogen consumption based on the improved strategy of minimizing equivalent hydrogen consumption;

[0011] S4. Total equivalent hydrogen consumption Equivalent to the output power of a multi-energy storage battery system ;

[0012] S5. Based on the power balance principle of the hybrid power system, the decision-making level further derives the total power reference value of the multi-fuel cell system;

[0013] S6. Based on the fuel cell efficiency-power curve, the decision-making layer divides the multi-fuel cell system into high-efficiency, medium-efficiency, and low-efficiency ranges. By using a segmented output power optimization function, the total power reference command of the multi-fuel cell system is mapped and processed, forcing the multi-fuel cell system to operate in the high-efficiency range as much as possible. The total reference power of the multi-fuel cell system after efficiency optimization is generated and sent to the first execution layer.

[0014] S7. The first execution layer receives the decision layer's objective function for minimizing the total power generation cost of the multi-fuel cell system and establishes it.

[0015] S8. Total reference power of multi-fuel cell systems provided by decision-making energy management strategies As a constraint, construct the augmented Lagrangian function;

[0016] S9. Apply the incremental rate criterion to define incremental cost;

[0017] S10. Combine the incremental cost equations for each fuel cell with the total power constraint to calculate the optimal incremental cost. ;

[0018] S11. Solve for the optimal power of each fuel cell. And distributed to each fuel cell;

[0019] S12. After receiving the instantaneous optimal output power command from the decision layer, the second execution layer controls the voltage change of each battery through the voltage reference command and rationally allocates the output power of each battery.

[0020] The formula for calculating the updated global average state of charge estimate is as follows:

[0021]

[0022] in, This is the globally average charge estimate after local updating of the battery. The state of charge of the battery is measured locally. For consistency gain, This is the global average state of charge estimate for neighboring batteries. This is the local global average charge estimate for the battery.

[0023] The total equivalent hydrogen consumption is:

[0024]

[0025] in, For total equivalent hydrogen consumption, For the instantaneous hydrogen consumption of a multi-fuel cell system, The instantaneous equivalent hydrogen consumption of a multi-energy storage battery system. As a correction factor;

[0026]

[0027] in, This is the SOC balance coefficient. This is the updated global average charge estimate obtained from the second execution layer. This represents the upper limit of the permissible state of charge. This represents the lower limit of the permissible state of charge.

[0028] Instantaneous hydrogen consumption of multi-fuel cell systems Calculated using the following formula:

[0029]

[0030] For the first Hydrogen consumption of a fuel cell For the first The output power of a fuel cell; These are the fitting coefficients;

[0031] Instantaneous equivalent hydrogen consumption of multi-energy storage battery system Calculated using the following formula:

[0032]

[0033] For the output power of multi-energy storage battery systems, To improve the discharge and charging efficiency of multi-energy storage battery systems, The average discharge and charge efficiency of a multi-energy storage battery system. This represents the average hydrogen consumption rate of multiple fuel cells.

[0034] Specifically, S4 is:

[0035]

[0036] By using the decision-making layer to solve for the minimum total equivalent hydrogen consumption, at this point, The solution set is the instantaneous optimal output power of the multi-energy storage battery system. The decision layer then issues the instantaneous optimal output power to the second execution layer.

[0037] The total power reference value of the multi-fuel cell system is:

[0038]

[0039] in, This is a reference value for the total power of a multi-fuel cell system. For total load demand, This represents the instantaneous optimal output power of a multi-energy storage battery system.

[0040] The segmented output power optimization function is as follows:

[0041]

[0042] in, For the total reference power of the multi-fuel cell system, matrix The boundary values ​​were determined by fitting three intervals: high efficiency, medium efficiency, and low efficiency.

[0043] The objective function for the total power generation cost of the multi-fuel cell system is as follows:

[0044]

[0045] in, For the power generation cost of each fuel cell system, Its output power, , , This is the cost coefficient.

[0046] The optimal power of each fuel cell for:

[0047]

[0048]

[0049] in, This represents the optimal incremental cost.

[0050] The voltage reference command is defined as follows:

[0051]

[0052] in, Rated bus voltage, For voltage compensation, ;in, This refers to the PI control coefficient. This is the comprehensive error term.

[0053] A two-layer real-time energy management optimization system for a marine hybrid power system, comprising:

[0054] Execution layer energy management module: includes a distributed optimal power allocation module and a distributed collaborative control module, which are used for the control of fuel cells and battery energy storage systems, respectively;

[0055] Decision-making level energy management module: used to achieve energy coordination and optimization between fuel cells and battery energy storage systems;

[0056] Controller module: The controller module is connected to the execution layer energy management module and the decision layer energy management module, and is used to send control signals and receive feedback information.

[0057] Beneficial effects: The present invention has the following advantages:

[0058] (1) By using the optimal power allocation strategy of fuel distribution in the first execution layer, the optimal incremental cost and the optimal power of each fuel cell are solved according to the equal incremental cost criterion, so that the low-cost fuel cell generates more power and the high-cost fuel cell generates less power, so that the long-term operating performance is consistent and the degradation is delayed.

[0059] (2) Through the distributed collaborative control of batteries in the second execution layer, dynamic balance of state of charge, power distribution according to capacity ratio and precise recovery of bus voltage are achieved.

[0060] (3) Through energy coordination optimization at the decision-making level, in order to optimize hydrogen consumption and system efficiency globally, an adaptive hydrogen consumption minimization design is carried out. Based on the improved equivalent consumption minimization strategy and efficiency range optimization strategy, the power allocation of multiple fuel cells and multiple battery energy storage systems is dynamically adjusted to minimize hydrogen consumption and improve system efficiency. Attached Figure Description

[0061] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0062] The invention will now be further described with reference to the accompanying drawings.

[0063] Example 1

[0064] like Figure 1 As shown, the two-layer real-time energy management optimization method for a ship hybrid power system in this embodiment includes the following steps:

[0065] S1. The second execution layer collects local status information on the terminal voltage and state of charge (remaining charge) of each battery unit.

[0066] S2. The second execution layer uses a distributed communication network based on the average consensus algorithm to enable adjacent battery cells to exchange local state information, collaboratively calculate the updated global average state of charge estimate, and send the value to the decision layer.

[0067]

[0068] in, This is the globally average charge estimate after local updating of the battery. The state of charge of the battery is measured locally. For consistency gain, This is the global average state of charge estimate for neighboring batteries. This is the local global average charge estimate for the battery.

[0069] S3. The decision-making level defines the total equivalent hydrogen consumption based on the improved strategy of minimizing equivalent hydrogen consumption:

[0070]

[0071] in, For total equivalent hydrogen consumption, For the instantaneous hydrogen consumption of a multi-fuel cell system, The instantaneous equivalent hydrogen consumption of a multi-energy storage battery system (converting the battery's charging and discharging power into equivalent hydrogen consumption). This is a correction factor used to adjust the weight of equivalent hydrogen consumption based on the battery's average state of charge, guiding the state of charge back to the target value.

[0072]

[0073] in, The SOC balance coefficient is a key adjustment parameter, adaptively adjusted by a sigmoid-like function. This is the updated global average charge estimate obtained from the second execution layer. This represents the upper limit of the permissible state of charge. This represents the lower limit of the permissible state of charge.

[0074] Instantaneous hydrogen consumption of multi-fuel cell systems Calculated using the following formula:

[0075]

[0076] For the first Hydrogen consumption of a fuel cell For the first The output power of a fuel cell. Fit coefficients. These coefficients differ for FCS in different health states.

[0077] Instantaneous equivalent hydrogen consumption of multi-energy storage battery system Calculated using the following formula:

[0078]

[0079] This represents the output power of a multi-energy storage battery system (positive for discharging and negative for charging). To improve the discharge and charging efficiency of multi-energy storage battery systems, The average discharge and charge efficiency of a multi-energy storage battery system. The average hydrogen consumption rate of multiple fuel cells can be understood as "how much hydrogen needs to be burned to generate one kilowatt-hour of electricity," and is a bridging coefficient connecting electricity and hydrogen.

[0080] S4. Total equivalent hydrogen consumption Equivalent to the output power of a multi-energy storage battery system The expression:

[0081]

[0082] By using the decision-making layer to solve for the minimum total equivalent hydrogen consumption, at this point, The solution set is the instantaneous optimal output power of the multi-energy storage battery system. The decision layer then issues the instantaneous optimal output power to the second execution layer.

[0083] S5. Based on the power balance principle of hybrid power systems, the decision-making level further derives the total power reference value for multi-fuel cell systems:

[0084]

[0085] in, This is a reference value for the total power of a multi-fuel cell system. For total load demand, This represents the instantaneous optimal output power of a multi-energy storage battery system.

[0086] S6. Based on the fuel cell efficiency-power curve, the decision-making layer divides the multi-fuel cell system into high-efficiency, medium-efficiency, and low-efficiency ranges. Through a piecewise output power optimization function, the total power reference command of the multi-fuel cell system is mapped, forcing the system to operate as close to the high-efficiency range as possible. This generates the efficiency-optimized total reference power for the multi-fuel cell system and sends it to the first execution layer. The piecewise output power optimization function is as follows:

[0087]

[0088] in, For the total reference power of the multi-fuel cell system, matrix By fitting the boundaries of three intervals—high efficiency, medium efficiency, and low efficiency—the system ensures that the multi-fuel cell system preferentially operates in the high efficiency interval.

[0089] S7 The first execution layer receives the decision layer's objective of minimizing the total power generation cost of the multi-fuel cell system. First, it establishes the objective function for the total power generation cost of the multi-fuel cell system, as follows:

[0090]

[0091] in, For the power generation cost of each fuel cell system, Its output power, , , Cost coefficient

[0092] S8. Total reference power of multi-fuel cell systems provided by decision-making energy management strategies As a constraint, construct the augmented Lagrangian function:

[0093]

[0094] right and Take the partial derivatives separately and set them to zero:

[0095]

[0096] S9. Apply the equal incremental rate criterion to define incremental cost. for:

[0097]

[0098] When the incremental costs of all fuel cell systems are equal, the total power generation cost is minimized.

[0099]

[0100] S10. Combine the incremental cost equations for each fuel cell with the total power constraint to calculate the optimal incremental cost. :

[0101]

[0102] S11. Solve for the optimal power of each fuel cell. And distributed to each fuel cell:

[0103]

[0104] S12. After receiving the instantaneous optimal output power command from the decision layer, the second execution layer designs a droop-free cooperative control law. This law controls the voltage changes of each battery through a voltage reference command, thereby achieving a reasonable allocation of output power among the batteries. The voltage reference command can be defined as follows:

[0105]

[0106] in, This is the rated bus voltage (global constant). For voltage compensation, ;in, The PI control coefficient ensures a fast and oscillating dynamic response. This is a comprehensive error term, determined by the voltage error term and the state-of-charge error term of each battery.

[0107] Example 2

[0108] The two-layer real-time energy management optimization system for the marine hybrid power system in this embodiment includes:

[0109] Execution layer energy management module: includes a distributed optimal power allocation module and a distributed collaborative control module, which are used for the control of fuel cells and battery energy storage systems, respectively;

[0110] Decision-making level energy management module: used to achieve energy coordination and optimization between fuel cells and battery energy storage systems.

[0111] Controller module: The controller module is connected to the execution layer energy management module and the decision layer energy management module, and is used to send control signals and receive feedback information.

Claims

1. A two-layer real-time energy management optimization method for a marine hybrid power system, characterized in that, Includes the following steps: S1. The second execution layer collects local status information on the terminal voltage and state of charge of each battery cell; S2. The second execution layer uses the average consensus algorithm to enable adjacent battery cells to exchange local state information, collaboratively calculate the updated global average state of charge estimate, and send the value to the decision layer. S3. The decision-making level defines the total equivalent hydrogen consumption based on the improved strategy of minimizing equivalent hydrogen consumption; S4. Total equivalent hydrogen consumption Equivalent to the output power of a multi-energy storage battery system ; S5. Based on the power balance principle of the hybrid power system, the decision-making level further derives the total power reference value of the multi-fuel cell system; S6. Based on the fuel cell efficiency-power curve, the decision-making layer divides the multi-fuel cell system into high-efficiency, medium-efficiency, and low-efficiency ranges. By using a segmented output power optimization function, the total power reference command of the multi-fuel cell system is mapped and processed, forcing the multi-fuel cell system to operate in the high-efficiency range as much as possible. The total reference power of the multi-fuel cell system after efficiency optimization is generated and sent to the first execution layer. S7. The first execution layer receives the decision layer's objective function for minimizing the total power generation cost of the multi-fuel cell system and establishes it. S8. Total reference power of multi-fuel cell systems provided by decision-making energy management strategies As a constraint, construct the augmented Lagrangian function; S9. Apply the incremental rate criterion to define incremental cost; S10. Combine the incremental cost equations for each fuel cell with the total power constraint to calculate the optimal incremental cost. ; S11. Solve for the optimal power of each fuel cell. And distributed to each fuel cell; S12. After receiving the instantaneous optimal output power command from the decision layer, the second execution layer controls the voltage change of each battery through the voltage reference command and rationally allocates the output power of each battery.

2. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 1, characterized in that, The formula for calculating the updated global average state of charge estimate is as follows: in, This is the globally average charge estimate after local updating of the battery. The state of charge of the battery is measured locally. For consistency gain, This is the global average state of charge estimate for neighboring batteries. This is the local global average charge estimate for the battery.

3. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 1, characterized in that, The total equivalent hydrogen consumption is: in, For total equivalent hydrogen consumption, For the instantaneous hydrogen consumption of a multi-fuel cell system, The instantaneous equivalent hydrogen consumption of a multi-energy storage battery system. As a correction factor; in, This is the SOC balance coefficient. This is the updated global average charge estimate obtained from the second execution layer. This represents the upper limit of the permissible state of charge. This represents the lower limit of the permissible state of charge. Instantaneous hydrogen consumption of multi-fuel cell systems Calculated using the following formula: For the first Hydrogen consumption of a fuel cell For the first The output power of a fuel cell; These are the fitting coefficients; Instantaneous equivalent hydrogen consumption of multi-energy storage battery system Calculated using the following formula: For the output power of multi-energy storage battery systems, To improve the discharge and charging efficiency of multi-energy storage battery systems, The average discharge and charge efficiency of a multi-energy storage battery system. This represents the average hydrogen consumption rate of multiple fuel cells.

4. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 3, characterized in that, Specifically, S4 is: By using the decision-making layer to solve for the minimum total equivalent hydrogen consumption, at this point, The solution set is the instantaneous optimal output power of the multi-energy storage battery system. The decision layer then issues the instantaneous optimal output power to the second execution layer.

5. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 4, characterized in that, The total power reference value of the multi-fuel cell system is: in, This is a reference value for the total power of a multi-fuel cell system. For total load demand, This represents the instantaneous optimal output power of a multi-energy storage battery system.

6. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 5, characterized in that, The segmented output power optimization function is as follows: in, For the total reference power of the multi-fuel cell system, matrix The boundary values ​​were determined by fitting three intervals: high efficiency, medium efficiency, and low efficiency.

7. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 6, characterized in that, The objective function for the total power generation cost of the multi-fuel cell system is as follows: in, For the power generation cost of each fuel cell system, Its output power, , , This is the cost coefficient.

8. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 7, characterized in that, The optimal power of each fuel cell for: in, This represents the optimal incremental cost.

9. The two-layer real-time energy management optimization method for a marine hybrid power system according to claim 1, characterized in that, The voltage reference command is defined as follows: in, Rated bus voltage, For voltage compensation, ;in, This refers to the PI control coefficient. This is the comprehensive error term.

10. A two-layer real-time energy management optimization system for a marine hybrid power system, the system being used to implement the two-layer real-time energy management optimization method for a marine hybrid power system as described in any one of claims 1 to 9, characterized in that, include: Execution layer energy management module: includes a distributed optimal power allocation module and a distributed collaborative control module, which are used for the control of fuel cells and battery energy storage systems, respectively; Decision-making level energy management module: used to achieve energy coordination and optimization between fuel cells and battery energy storage systems; Controller module: The controller module is connected to the execution layer energy management module and the decision layer energy management module, and is used to send control signals and receive feedback information.

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