Energy management optimization method and system based on ship microgrid

Abstract

The invention relates to the technical field of ship power system energy management, and particularly discloses an energy management optimization method and system based on a ship microgrid, and the system comprises a photovoltaic power generation unit, an energy storage unit, and a diesel generator unit. The photovoltaic power generation unit is provided with a maximum power point tracking controller to achieve the maximum utilization rate of solar energy, the energy storage unit comprises a storage battery and a bidirectional power converter, the charge state is monitored in real time, a safety threshold value is set, and the diesel generator unit serves as a standby power supply. Based on an improved logic threshold control mechanism, a photovoltaic power generation unit is used as a main energy source and works at a maximum power point; the energy storage battery is a secondary buffer unit, a charge state safety threshold value is set, dynamic charging and discharging are carried out, and when photovoltaic output is surplus and the storage battery does not reach the upper limit, surplus electric energy is stored in the storage battery.

Description

Technical Field

[0001] This invention relates to the field of energy management technology for ship electrical systems, and in particular to an energy management optimization method and system based on ship microgrids. Background Technology

[0002] In the process of green transformation of the global shipping industry, ship energy management faces multiple challenges. The significant increase in international fuel sulfur content standards has directly led to an increase in ship operating costs. The limitations of traditional fuel power are becoming increasingly apparent. At the same time, pure electric ships are limited by battery technology, and their range is difficult to meet the needs of ocean voyages.

[0003] Ships operate under complex conditions, with significant power fluctuations during takeoff and reversing. Diesel engines have insufficient response speed, and lithium batteries also face numerous limitations in charging and discharging. Traditional energy management strategies struggle to balance efficiency and reliability. Fuel costs constitute a substantial proportion of total ship operating costs, and traditional rule-based energy management methods have obvious drawbacks. For instance, diesel engines experience reduced fuel efficiency under low loads, and improper charging and discharging of lithium batteries accelerates capacity decay, thereby increasing total lifecycle costs.

[0004] Existing ship energy management strategies each have their shortcomings: rule-based strategies have strong real-time performance but limited optimization capabilities; instantaneous optimization strategies only pursue the optimal at a single moment and cannot guarantee the global optimal; global optimization strategies are theoretically optimal, but have large computational load and poor real-time performance; and adaptive control strategies are not adaptable enough to the marine environment.

[0005] Therefore, there is an urgent need to develop an energy management optimization method and system based on ship microgrids. Summary of the Invention

[0006] The purpose of this invention is to provide an energy management optimization method and system based on ship microgrids to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an energy management optimization system based on a ship microgrid, wherein the energy management optimization system of the ship microgrid includes a photovoltaic power generation unit, an energy storage unit, and a diesel generator unit;

[0008] The photovoltaic power generation unit is equipped with a maximum power point tracking controller to achieve maximum solar energy utilization. The energy storage unit includes a battery and a bidirectional power converter, which monitors the state of charge in real time and sets a safety threshold. The diesel generator unit serves as a backup power source.

[0009] The energy management optimization system for the ship microgrid uses a hierarchical control architecture, which includes:

[0010] The local control layer is mainly responsible for photovoltaic maximum power tracking and suppressing power fluctuations.

[0011] The equipment-level control layer is mainly responsible for optimizing grid-connected power quality and promptly eliminating voltage and frequency fluctuations caused by local control layer and grid-connected load switching operations.

[0012] The system-level control layer achieves economical system operation through intelligent optimization algorithms. Based on real-time monitoring data and the predicted results of load and renewable energy power generation, a distributed power source collaborative scheduling model is constructed.

[0013] Preferably, the local control layer employs an improved logic threshold strategy for real-time power scheduling, which includes:

[0014] The safe operating range of the battery's state of charge (SOC) is set to 20% to 90%.

[0015] When the photovoltaic power exceeds the load demand and the SOC is less than 90%, the photovoltaic system outputs full power, and the excess electricity is stored in the battery through the bidirectional converter.

[0016] When the photovoltaic power exceeds the load demand and the SOC is ≥ 90%, the photovoltaic system outputs full power, and the surplus power is fed into the grid through the inverter.

[0017] When photovoltaic power is insufficient and SOC > 20%, the battery supplements the power supply gap through a bidirectional converter.

[0018] When the photovoltaic power is insufficient and the SOC is ≤20%, the diesel generator is started to supply power, the photovoltaic output is given priority to the load, and the charging of the battery is suspended.

[0019] Preferably, the system-level control layer integrates a multi-objective improved particle swarm optimization algorithm, which optimizes for both economic efficiency and environmental friendliness, and includes:

[0020] The inertia weight decreases non-linearly with the number of iterations, starting at 0.9 and decreasing to 0.4 at the end of the iteration. This ensures global search capability in the early stage and improves convergence speed in the later stage.

[0021] The learning factor is dynamically adjusted based on the fitness ratio of the individual optimal solution to the group optimal solution. The optimization objective is to minimize the total system cost, including operation and maintenance costs and environmental governance costs.

[0022] Preferably, the economic benefits include battery maintenance costs, diesel generator fuel costs, and grid interconnection costs;

[0023] The environmental friendliness includes the cost of treating pollutant emissions from diesel generators and the indirect cost of carbon emissions from the power grid.

[0024] Preferably, the bidirectional power converter is controlled by switching the charging and discharging modes according to the polarity of the power reference value and dynamically adjusting the duty cycle of the switching signal through a proportional-integral controller.

[0025] Preferably, the implementation steps of the multi-objective improved particle swarm optimization algorithm are as follows:

[0026] S1: Initialize the position and velocity parameters of the particle swarm;

[0027] S2: Dynamically adjust inertia weights and learning factors;

[0028] S3: Calculate the fitness value for multiple objectives, including economic efficiency and environmental friendliness;

[0029] S4: By using non-dominated sorting and crowding calculation, retain the Pareto optimal solution set, and output the scheduling scheme after iterating to the maximum number of times.

[0030] Preferably, the operational constraints of the multi-objective improved particle swarm optimization algorithm include:

[0031] Power supply and demand balance constraints, battery charging and discharging power limits and SOC constraints, diesel generator output power ramp rate limits, and upper limit of power exchange between microgrid and external power grid.

[0032] An energy management optimization method based on ship microgrids, the optimization method comprising the following steps:

[0033] Step 1: The photovoltaic unit operates in real time using the perturbation-observation method, and the output power is fed back to the local control layer in real time;

[0034] Step 2: Simultaneously monitor the battery's state of charge and dynamically switch the charging and discharging modes through a bidirectional power converter to control the state of charge within the range of 20% to 90%.

[0035] Step 3: Real-time power allocation is achieved based on an improved logic threshold strategy. According to the priority of "photovoltaic priority, battery buffer and diesel generator backup", the photovoltaic output prioritizes meeting the load demand, the surplus power is stored or connected to the grid according to the state of charge, and the insufficient part is supplemented by the battery.

[0036] Step 4: With a 24-hour cycle, the system-level control layer initiates a multi-objective improved particle swarm optimization algorithm. Through nonlinear decreasing inertia weights and dynamic learning factors, it comprehensively balances operation and maintenance costs and environmental protection costs, outputs a scheduling plan, and guides the operation plan of each unit the next day. The operation and maintenance costs include battery maintenance, diesel fuel and grid interaction expenses, while the environmental protection costs include pollutant treatment costs.

[0037] Step 5: Each step operates collaboratively through a hierarchical control architecture: the local control layer executes steps 1 to 3, the system-level control layer completes optimization through step 4, and the layers coordinate power balance and targets through high-speed data interaction.

[0038] Step 6: When the system detects that the diesel generator has started, it automatically adjusts the energy flow: the photovoltaic output is given priority to supply the load directly, and the excess power is distributed to other power-consuming units of the ship's microgrid through the grid connection switch, and charging of the storage battery is suspended; the diesel generator starts when the state of charge is ≤20% and the photovoltaic output is insufficient.

[0039] Step 7: During the operation of the diesel generator, continuously monitor the recovery of the battery's state of charge. Once the state of charge recovers to more than 20%, the system immediately switches to the "photovoltaic + battery" combined power supply mode and automatically disconnects the diesel generator.

[0040] Step 8: The system integrates a fault self-diagnosis module to diagnose the operating status of the optical unit, energy storage unit, and diesel generator unit in real time. If a fault is detected, the backup plan is immediately activated, redundant power supplies are enabled, power distribution strategies are adjusted, and operators are notified through audible and visual alarms.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] 1. This invention, based on an improved logic threshold control mechanism, uses a photovoltaic (PV) power generation unit as the primary energy source, ensuring it operates at its maximum power point. An energy storage battery serves as a secondary buffer unit, with a set state-of-charge (SOC) safety threshold for dynamic charging and discharging. When PV output is excessive and the battery charge is below its upper limit, excess energy is stored in the battery. When PV output is insufficient and the battery charge is above its lower limit, the battery provides supplemental power. A diesel generator serves as a backup power source, starting only when neither PV nor battery power can meet the load demand, and operating at a fixed power output. This strategy reduces diesel generator utilization and maintains stable DC bus voltage through priority allocation and SOC protection mechanisms.

[0043] 2. This invention employs a multi-objective improved particle swarm optimization algorithm with a 24-hour global scheduling cycle. The algorithm dynamically adjusts the inertia weight and optimizes the learning factor based on the adaptive relationship between the individual and group optimal solutions. The optimization objective is to minimize the total system cost. The constraints include power balance, energy storage charging and discharging limits, diesel engine climbing ability, and grid interaction capacity.

[0044] 3. In this invention, the real-time strategy ensures the maximum utilization of clean energy and equipment safety, and protects the battery life through the SOC threshold; the periodic strategy reduces long-term operating costs and environmental burden through multi-objective optimization. Together, they constitute an efficient energy management framework for ship microgrids, supporting the IMO 2050 emission reduction target. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the improved logic threshold energy management control of the present invention;

[0047] Figure 2 This is a flowchart of the multi-objective improved particle swarm optimization algorithm of the present invention;

[0048] Figure 3 This is a conventional PSO diagram of the present invention;

[0049] Figure 4 This is the improved PSO diagram of the present invention;

[0050] Figure 5 This is a comparison of the results of different runs of the traditional PSO and the improved PSO of the present invention. Detailed Implementation

[0051] The following will refer to the appendices in the embodiments of the present invention. Figure 1-5 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: An energy management optimization system based on a ship microgrid. The system includes a photovoltaic power generation unit, an energy storage unit, and a diesel generator unit. The photovoltaic power generation unit is equipped with a maximum power point tracking controller to maximize solar energy utilization. The energy storage unit includes batteries and a bidirectional power converter, capable of real-time monitoring of state of charge and setting safety thresholds. The diesel generator unit serves as a backup power source.

[0053] The energy management optimization system for ship microgrids adopts a hierarchical control architecture, specifically including:

[0054] Local control layer: mainly responsible for photovoltaic maximum power tracking and suppressing power fluctuations.

[0055] Device-level control layer: mainly optimizes grid-connected power quality. Specifically, it monitors voltage fluctuation thresholds and frequency fluctuation thresholds in real time. When the thresholds are exceeded, a PI compensation algorithm is activated to suppress fluctuations. Virtual impedance regulation technology is used to suppress harmonic distortion and ensure that the total harmonic distortion rate is ≤5%. The device-level control layer interacts with the local control layer through a high-speed CAN bus. Its response priority is higher than that of the local control layer but lower than that of the system-level emergency control signal.

[0056] System-level control layer: The system achieves economical operation by using intelligent optimization algorithms. Based on real-time monitoring data and the predicted results of load and renewable energy power generation, a distributed power source collaborative scheduling model is constructed.

[0057] The simulation experiment was conducted using a ship microgrid model built with Matlab / Simulink. The system parameters are as follows: rated power of photovoltaic unit 100kW, maximum power point tracking efficiency 98%;

[0058] The energy storage unit is a 200kWh lead-acid battery with a charge / discharge efficiency of 90%.

[0059] The diesel generator has a rated power of 150kW and a fuel consumption rate of 220g / kWh.

[0060] The load includes pulse load and stable load, where the pulse load fluctuates at 50kW / 10s during the start-up phase and the stable load is constant at 80kW during the cruise phase.

[0061] The simulation covered sunny, cloudy, nighttime, and load change scenarios. The photovoltaic output was 100kW on sunny days, 30kW on cloudy days, and 0kW at night.

[0062] The load fluctuation range is ±30% of the rated load, the fluctuation interval is 30s, and each fluctuation lasts for 5s.

[0063] In Example 2, the local control layer employs an improved logic threshold strategy for real-time power scheduling. This strategy specifically includes the following:

[0064] The safe operating range of the battery's state of charge (SOC) is set to 20% to 90%.

[0065] When the photovoltaic power exceeds the load demand and the SOC is less than 90%, the photovoltaic system outputs full power, and the excess electricity is stored in the battery through the bidirectional converter.

[0066] When the photovoltaic power exceeds the load demand and the SOC is ≥ 90%, the photovoltaic system outputs full power, and the surplus power is fed into the grid through the inverter.

[0067] When photovoltaic power is insufficient and SOC > 20%, the battery supplements the power supply gap through a bidirectional converter.

[0068] When the photovoltaic power is insufficient and the SOC is ≤20%, the diesel generator is started to supply power, the photovoltaic output is given priority to the load, and the charging of the battery is suspended.

[0069] It also includes the following measures: After the diesel generator starts supplying power, the system will monitor the speed, oil temperature, oil pressure and output voltage in real time, and monitor the output power. Power adjustment will be implemented according to the load fluctuation range.

[0070] When the load change rate is ≤10% of the rated power / minute, the sample is taken every 5 seconds, and the power adjustment step size does not exceed 5% of the rated power;

[0071] When the load change rate is greater than 10%, shorten the sampling period to 2 seconds and limit the adjustment step size to within 3% of the rated power to ensure that the output power deviation from the load demand is ≤3%.

[0072] The system also manages the charging and discharging process of the battery in detail to avoid damage to the battery from overcharging and discharging, and to extend the battery's service life.

[0073] The system-level control layer integrates a multi-objective improved particle swarm optimization algorithm. This algorithm prioritizes economic efficiency and environmental friendliness, specifically including:

[0074] The inertia weight decreases non-linearly with the number of iterations, with an initial value of 0.9 and a value of 0.4 at the end of the iteration. This ensures global search capability in the early stage and improves convergence speed in the later stage.

[0075] The learning factors are dynamically adjusted based on the fitness ratio between the individual optimal solution and the group optimal solution: when the ratio is ≤0.6, the learning factors c1 = 2.5 and c2 = 1.5; when 0.6 < ratio < 1.2, the learning factors c1 = c2 = 2.0; when the ratio is ≥1.2, the learning factors c1 = 1.5 and c2 = 2.5.

[0076] The optimization objective is to minimize the total system cost, including operation and maintenance costs and environmental governance costs.

[0077] Economic efficiency includes battery maintenance costs, diesel generator fuel costs, and grid interconnection costs; environmental efficiency includes diesel generator pollutant emission treatment costs and indirect costs of grid carbon emissions.

[0078] During the optimization process, the algorithm comprehensively considers the current photovoltaic power generation status, battery condition, load demand, and external environmental factors to achieve the best balance between economy and environmental protection. By continuously adjusting the operating status of the diesel generator, the charging and discharging strategy of the battery, and the grid interaction plan, the system ensures that while meeting load demand, it minimizes operation and maintenance costs, environmental governance costs, pollutant emission treatment costs, and indirect costs of grid carbon emissions. Furthermore, the algorithm has self-learning capabilities, continuously optimizing the control strategy based on historical data and real-time feedback to improve the overall performance and stability of the system.

[0079] The bidirectional power converter control method is as follows: the charging and discharging modes are switched according to the polarity of the power reference value; the duty cycle of the switching signal is dynamically adjusted by the proportional-integral controller, the proportional coefficient is set to 0.8, the integral time constant is set to 0.05s, the dynamic response time is ≤0.1s under rated power conditions, and the power tracking error is ≤5%.

[0080] The implementation steps of the multi-objective improved particle swarm optimization algorithm are as follows:

[0081] S1: Initialize the position and velocity parameters of the particle swarm. The position parameter initialization range is -5 to 5, corresponding to the power scheduling coefficient, covering the range of -50% to 50% of the rated power. The velocity parameter initialization range is -2 to 2. The population size is set to 30, and the maximum number of iterations is 50.

[0082] S2: Dynamically adjust inertia weights and learning factors;

[0083] S3: Calculate the multi-objective fitness value covering economic efficiency and environmental friendliness: The formula for calculating economic efficiency is:

[0084] C total =C bat +C diesel +C grid ;

[0085] Where C bat The battery maintenance cost is calculated by multiplying 0.01 by the number of charge-discharge cycles and then by the rated capacity, in kWh.

[0086] C diesel The cost of diesel fuel is calculated as fuel consumption rate (g / kWh), multiplied by electricity generation (kWh), and then multiplied by the oil price (yuan / g).

[0087] C grid This is the grid interaction cost, calculated by multiplying the amount of electricity purchased or sold in kWh by the electricity price in yuan / kWh.

[0088] The formula for calculating environmental friendliness is:

[0089] Etotal =E diesel +E grid ;

[0090] Where E diesel The cost of diesel emissions is calculated by multiplying the amount of pollutants emitted (g / kWh) by the treatment cost (yuan / g) and then by the amount of electricity generated (kWh).

[0091] E grid The indirect cost of carbon emissions from the power grid is calculated using the power grid emission factor, expressed in gCO2 / kWh. This is multiplied by the amount of electricity exchanged, also in kWh, and then multiplied by the carbon price, expressed in yuan / gCO2.

[0092] S4: By using non-dominated sorting and crowding calculation, the Pareto optimal solution set is preserved: The crowding distance calculation formula is:

[0093]

[0094] Where, d i Represents the i-th object, This represents summing over m from 1 to m, where m represents the number of related categories and dimensions. and These represent the maximum and minimum frequency values ​​in class m, used to determine the frequency range. and It refers to the frequency values ​​at different times, in different states, or at different locations.

[0095] Solutions ranking in the top 30% by crowding distance are retained. The convergence criterion for iteration is to stop early if the rate of change of the optimal fitness value is ≤0.5% over five consecutive iterations; otherwise, the algorithm iterates to a maximum of 50 iterations before outputting the scheduling scheme. During iteration, for each particle, its velocity and position parameters are updated based on its historical and global optimal positions to explore a better solution space. To enhance the algorithm's local search capability, a mutation operation is introduced to fine-tune the positions of some particles, avoiding getting trapped in local optima. Considering the actual operational constraints of the ship's microgrid, such as the start-stop limit of the diesel generator and the charging / discharging depth limit of the battery, the algorithm's output is verified and adjusted to ensure the feasibility and practicality of the scheduling scheme. Finally, the algorithm outputs the most economically cost-effective scheduling scheme while meeting load requirements, battery state limitations, and environmental considerations, providing a scientific basis for energy management of ship microgrids.

[0096] The algorithm's operational constraints include: a power supply-demand balance constraint of P. pv +p bat +p diesel +P grid +p loadEach power deviation shall not exceed 2% of the rated power.

[0097] The charging and discharging power limits and SOC constraints for lead-acid batteries are as follows: charging and discharging power is 0.2C to 0.5C, SOC safety range is 20% to 90%, and single charge / discharge depth is ≤80%. The diesel generator output power ramp rate is limited to ≤5% of rated power / second, and the minimum operating power is ≥30% of rated power. The upper limit of power exchange between the microgrid and the external grid is 20% of the system's rated power in grid-connected mode; no grid interaction occurs in islanded mode. The battery SOC range is set within 10% to 95%. During energy management, to improve fault tolerance, a certain margin is provided, with a threshold set at 20% to 90%. The energy management strategy is as follows:

[0098] The photovoltaic power generation capacity is greater than the load requirement and the state of charge (SOC) is less than 90%.

[0099] The photovoltaic (PV) power exceeds the load requirement, and the PV system maintains its maximum power level. Since the battery is determined to be in normal operating or over-discharge condition, which does not affect energy flow, the excess output power from the PV system flows into the battery for storage, improving energy utilization. The battery module enable signal is high. The diesel generator connection switch is disconnected, and it does not participate in power supply.

[0100] The photovoltaic power generation capacity is greater than the load requirement and the SOC is greater than 90%.

[0101] Photovoltaic power generation continues to maintain maximum power output. At this time, the battery SOC is >90%. To avoid the impact of overcharging, the battery module enable signal goes low and stops working. Power beyond what the load needs flows into the grid, and the diesel generator still does not participate in the energy flow.

[0102] The photovoltaic power generation capacity is less than the load requirement and the state of charge (SOC) is greater than 20%.

[0103] If the maximum power output of the photovoltaic system is insufficient to supply the load, the battery is determined to be in normal working or overcharged state, which does not affect its output power. Therefore, the insufficient power is supplied by the battery. The battery module outputs a high-level enable signal to supply power to the load, and the diesel generator switch is turned off and does not participate in power supply.

[0104] The photovoltaic power generation capacity is less than the load requirement and the state of charge (SOC) is less than 20%.

[0105] The photovoltaic system's maximum output power is insufficient to supply the load, and the SOC detection is less than 20%. At this point, the battery is determined to be over-discharged and unable to output power to the load. Therefore, the diesel generator, acting as a backup power source, is switched on to supply power to the load. The battery module's enable signal is high, the three-phase inverter disconnects its AC connection, and the photovoltaic output power flows into the battery for storage. Simultaneously, to prevent excess energy from the diesel generator from flowing into the grid and reducing system energy efficiency, the grid connection switch is disconnected.

[0106] Example 3: An energy management optimization system based on a ship microgrid, the optimization method includes the following steps:

[0107] Step 1: The photovoltaic unit tracks the maximum power point in real time using the perturbation observation method to ensure maximum utilization of solar energy resources, and the output power is fed back to the local control layer in real time.

[0108] Step 2: Simultaneously monitor the battery's state of charge and dynamically switch the charging and discharging modes through a bidirectional power converter to strictly control the state of charge within a safe range of 20% to 90%, avoiding lifespan degradation caused by overcharging and over-discharging.

[0109] Step 3: Real-time power allocation is achieved based on an improved logic threshold strategy, following the priority of "photovoltaic priority, battery buffer and diesel generator backup": photovoltaic output is given priority to meet load demand, surplus power is stored or connected to the grid according to the state of charge, and the insufficient part is dynamically supplemented by the battery to ensure stable power supply to the load.

[0110] Step 4: With a 24-hour cycle, the system-level control layer initiates a multi-objective improved particle swarm optimization algorithm. Through nonlinear decreasing inertia weights and dynamic learning factors, it comprehensively balances operation and maintenance costs and environmental protection costs, outputs the Pareto optimal scheduling scheme, and guides the operation plan of each unit the next day. The operation and maintenance costs include battery maintenance, diesel fuel and grid interaction expenses, while the environmental protection costs include pollutant treatment costs.

[0111] Step 5: Each step operates collaboratively through a hierarchical control architecture: the local control layer executes steps 1 to 3 to achieve real-time response, the system-level control layer completes global optimization through step 4, and the layers coordinate short-term power balance and long-term economic and environmental goals through high-speed data interaction.

[0112] Step 6: When the system detects the diesel generator starting, it automatically adjusts the energy flow: photovoltaic output is prioritized to directly supply the load, and excess power is distributed to other power-consuming units of the ship's microgrid through the grid connection switch. Charging of the storage battery is suspended to prioritize power supply to the load. The diesel generator starts when the state of charge is ≤20% and the photovoltaic output is insufficient.

[0113] Step 7: During the operation of the diesel generator, continuously monitor the recovery of the battery's state of charge. Once the state of charge recovers to more than 20% and the photovoltaic output can partially support the load, the system immediately switches to the "photovoltaic + battery" combined power supply mode, automatically disconnecting the diesel generator to reduce fuel consumption and pollutant emissions.

[0114] Step 8: The system integrates a fault self-diagnosis module to diagnose the operating status of the photovoltaic unit, energy storage unit, and diesel generator unit in real time. If a fault is detected, the backup plan is immediately activated: if the main battery pack fault lasts for more than 5 seconds, the 50kWh backup battery pack is activated; the power distribution strategy is adjusted to prioritize the power supply of navigation equipment and communication system, and non-critical loads are reduced by 20%; the audible and visual alarm adopts a three-level classification, with a first-level fault alarm frequency of 2Hz and a second-level fault alarm frequency of 1Hz, and the alarm continues until the fault is cleared.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy management optimization system based on a ship microgrid, characterized in that: The energy management optimization system of the ship microgrid includes a photovoltaic power generation unit, an energy storage unit, and a diesel generator unit; The photovoltaic power generation unit is equipped with a maximum power point tracking controller to achieve maximum solar energy utilization. The energy storage unit includes a battery and a bidirectional power converter, which monitors the state of charge in real time and sets a safety threshold. The diesel generator unit serves as a backup power source. The energy management optimization system for the ship microgrid uses a hierarchical control architecture, which includes: The local control layer is mainly responsible for photovoltaic maximum power tracking and suppressing power fluctuations. The equipment-level control layer is mainly responsible for optimizing grid-connected power quality and promptly eliminating voltage and frequency fluctuations caused by local control layer and grid-connected load switching operations. The system-level control layer achieves economical system operation through intelligent optimization algorithms. Based on real-time monitoring data and the predicted results of load and renewable energy power generation, a distributed power source collaborative scheduling model is constructed.

2. The energy management optimization system based on a ship microgrid according to claim 1, characterized in that: The local control layer employs an improved logical threshold strategy for real-time power scheduling, which includes: The safe operating range of the battery's state of charge (SOC) is set to 20% to 90%. When the photovoltaic power exceeds the load demand and the SOC is less than 90%, the photovoltaic system outputs full power, and the excess electricity is stored in the battery through the bidirectional converter. When the photovoltaic power exceeds the load demand and the SOC is ≥ 90%, the photovoltaic system outputs full power, and the surplus power is fed into the grid through the inverter. When photovoltaic power is insufficient and SOC > 20%, the battery supplements the power supply gap through a bidirectional converter. When the photovoltaic power is insufficient and the SOC is ≤20%, the diesel generator is started to supply power, the photovoltaic output is given priority to the load, and charging of the battery is suspended.

3. The energy management optimization system based on a ship microgrid according to claim 1, characterized in that: The system-level control layer integrates a multi-objective improved particle swarm optimization algorithm, which optimizes for both economic efficiency and environmental friendliness, and includes: The inertia weight decreases non-linearly with the number of iterations, starting at 0.9 and decreasing to 0.4 at the end of the iteration. This ensures global search capability in the early stage and improves convergence speed in the later stage. The learning factor is dynamically adjusted based on the fitness ratio of the individual optimal solution to the group optimal solution. The optimization objective is to minimize the total system cost, including operation and maintenance costs and environmental governance costs.

4. The energy management optimization system based on a ship microgrid according to claim 3, characterized in that: The economic benefits include battery maintenance costs, diesel generator fuel costs, and grid connection costs. The environmental friendliness includes the cost of treating pollutant emissions from diesel generators and the indirect cost of carbon emissions from the power grid.

5. The energy management optimization system based on a ship microgrid according to claim 1, characterized in that: The bidirectional power converter is controlled by switching the charging and discharging modes according to the polarity of the power reference value and dynamically adjusting the duty cycle of the switching signal through a proportional-integral controller.

6. The energy management optimization system based on a ship microgrid according to claim 3, characterized in that: The implementation steps of the multi-objective improved particle swarm optimization algorithm are as follows: S1: Initialize the position and velocity parameters of the particle swarm; S2: Dynamically adjust inertia weights and learning factors; S3: Calculate the fitness value for multiple objectives, including economic efficiency and environmental friendliness; S4: By using non-dominated sorting and crowding calculation, retain the Pareto optimal solution set, and output the scheduling scheme after iterating to the maximum number of times.

7. The energy management optimization system based on a ship microgrid according to claim 3, characterized in that: The operational constraints of the multi-objective improved particle swarm optimization algorithm include: Power supply and demand balance constraints, battery charging and discharging power limits and SOC constraints, diesel generator output power ramp rate limits, and upper limit of power exchange between microgrid and external power grid.

8. An energy management optimization method based on a ship microgrid, and an energy management optimization system based on a ship microgrid according to any one of claims 1-7, characterized in that: The optimization method includes the following steps: Step 1: The photovoltaic unit operates in real time using the perturbation-observation method, and the output power is fed back to the local control layer in real time; Step 2: Simultaneously monitor the battery's state of charge and dynamically switch the charging and discharging modes through a bidirectional power converter to control the state of charge within the range of 20% to 90%. Step 3: Real-time power allocation is achieved based on an improved logic threshold strategy. According to the priority of "photovoltaic priority, battery buffer and diesel generator backup", the photovoltaic output prioritizes meeting the load demand, the surplus power is stored in the state of charge or connected to the grid, and the insufficient part is supplemented by the battery. Step 4: With a 24-hour cycle, the system-level control layer starts the multi-objective improved particle swarm optimization algorithm. Through nonlinear decreasing inertia weight and dynamic learning factor optimization, it comprehensively weighs the operation and maintenance costs and environmental protection costs, outputs the scheduling plan, and guides the operation plan of each unit the next day. The operation and maintenance costs include battery maintenance, diesel fuel and grid interaction expenses, and the environmental protection costs include pollutant treatment costs. Step 5: Each step operates collaboratively through a hierarchical control architecture: the local control layer executes steps 1 to 3, the system-level control layer completes optimization through step 4, and the layers coordinate power balance and targets through high-speed data interaction. Step 6: When the system detects that the diesel generator has started, it automatically adjusts the energy flow: the photovoltaic output is given priority to directly supply the load, and the excess power is distributed to other power-consuming units of the ship's microgrid through the grid connection switch, and charging of the storage battery is suspended; the starting conditions for the diesel generator are that the state of charge is ≤20% and the photovoltaic output is insufficient. Step 7: During the operation of the diesel generator, continuously monitor the recovery of the battery's state of charge. Once the state of charge recovers to more than 20%, the system immediately switches to the "photovoltaic + battery" combined power supply mode and automatically disconnects the diesel generator. Step 8: The system integrates a fault self-diagnosis module to diagnose the operating status of the optical unit, energy storage unit, and diesel generator unit in real time. If a fault is detected, the backup plan is immediately activated, redundant power supplies are enabled, power distribution strategies are adjusted, and operators are notified through audible and visual alarms.

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