Multi-energy energy management method and ship

Abstract

The present invention discloses a multi-energy energy management method and ship. The method is applied to a multi-energy power system including fuel cells, battery packs and photovoltaic power generation. An energy management optimization model based on model predictive control is established, including a prediction model, rolling optimization and reference trajectory. The energy management strategy is optimized in real time through model predictive control (MPC). According to actual energy demand and availability, the conversion and distribution between different energy sources are intelligently managed to achieve a stable supply of power output. The present invention integrates hydrogen fuel cells, lithium batteries and photovoltaic power generation technologies to construct a hybrid power platform with complementary multi-energy sources. The energy management strategy is optimized in real time through model predictive control (MPC) to ensure efficient and stable use of energy under various navigation conditions.

Description

Technical Field

[0001] The present invention relates to the field of new energy and hybrid power systems for ships, and in particular to a multi-energy energy management method and a ship. Background Art

[0002] Traditional ships rely primarily on fossil fuels, which has led to serious environmental pollution. To address this issue, researchers are exploring and applying new energy technologies, including hydrogen fuel cells, lithium batteries, and photovoltaic power generation. These new energy technologies are expected to reduce ships' reliance on fossil fuels and, consequently, reduce environmental pollution.

[0003] Hydrogen fuel cell technology is a clean energy technology that directly converts the chemical energy of hydrogen and oxygen into electricity. It is pollution-free, noise-free, and highly efficient. This technology has broad application prospects in automotive energy, aerospace energy, and energy storage systems. Technological advances have significantly reduced costs and improved energy efficiency, driving global energy transformation and sustainable development.

[0004] Lithium battery technology is a clean energy technology based on the movement of lithium ions between positive and negative electrodes to achieve charge and discharge. It offers advantages such as high energy density and long lifespan. Currently, demand for lithium batteries continues to expand. In the future, technological innovation will drive higher energy density and faster charging speeds for lithium batteries. Furthermore, green and low-carbon development trends will become the industry's development trend, promoting the sustainable development of the lithium battery industry.

[0005] Photovoltaic technology utilizes the photovoltaic effect at semiconductor interfaces to convert sunlight directly into electricity. In recent years, photovoltaic technology has experienced rapid development worldwide, with significant technological advancements. High-efficiency N-type cells and large silicon wafers are driving cost reductions and efficiency improvements. Combined with key technologies such as high-efficiency cells, intelligent operations and maintenance, and energy storage, these technologies are accelerating global energy transformation and sustainable development.

[0006] However, current new energy vessels often rely on a single energy source, such as batteries or fuel cells. This can lead to unstable power output under fluctuating energy demand, compromising the vessel's normal operation and safety. While some new energy vessels have attempted to integrate multiple energy sources, existing power systems often lack comprehensive energy management, impacting navigation efficiency and stability. In particular, systems combining fuel cells, photovoltaics, and batteries face difficulties in accurately predicting energy demand and optimizing energy distribution, leading to energy waste and unstable output. Summary of the Invention

[0007] The purpose of the present invention is to provide a multi-energy energy management method and ship, combining hydrogen fuel cells, lithium batteries and photovoltaic power generation technologies to build a multi-energy complementary hybrid platform, and optimize the energy management strategy in real time through model predictive control (MPC) to ensure efficient and stable use of energy under various navigation conditions.

[0008] To achieve the above objectives, according to a first aspect of the present invention, a multi-energy energy management method is provided, which is applied to a multi-energy power system including fuel cells, battery packs, and photovoltaic power generation. The method comprises: establishing an energy management optimization model based on model predictive control, including a prediction model, a rolling optimization, and a reference trajectory;

[0009] Predictive Models: Definition is the state variable of the model predictive control, u is the control variable, For measurement input, The measured output is as follows:

[0010]

[0011]

[0012]

[0013]

[0014] Where: is the state of charge of the battery pack; M e yes k The equivalent hydrogen consumption corresponding to the total power consumption of the multi-energy power system in this stage; Provide load power for photovoltaic power generation; is the battery pack power; is the load power demand;

[0015] The state space after linearization and discretization is as follows:

[0016]

[0017]

[0018] Where, and They are x 、 u and y The form after linearization and discretization; and are all coefficient matrices, and the following equation exists:

[0019]

[0020]

[0021]

[0022] Among them, according to the fuel cell output power The corresponding hydrogen consumption per unit time , fitted as a linear function, the equation of the straight line obtained by fitting is:

[0023]

[0024] Where: and They are the two parameters of the linear function respectively; For battery pack energy;

[0025] At the same time, the energy balance equation of the multi-energy power system, the state-of-charge equation of the battery pack, and the power distribution equation of photovoltaic power generation are established; among them, the energy balance equation of the multi-energy power system describes the relationship between the load power demand and the output power of the fuel cell, battery pack, and photovoltaic power generation; the state-of-charge equation of the battery pack describes the relationship between the state of charge of the battery pack and the output power of the battery pack; and the power distribution equation of photovoltaic power generation describes the power distribution strategy of photovoltaic power generation;

[0026] Rolling optimization: Establish an indicator function and determine the fuel cell output power constraint and battery pack SOC constraint. The indicator function is defined as the minimum difference between the measured output value of the predictive control process and the reference trajectory.

[0027] Reference trajectory: The multi-energy power system has the lowest equivalent hydrogen consumption, as shown in the following formula:

[0028]

[0029] The optimal reference power of the battery pack in the prediction time domain is obtained by solving the above equation. The SOC trajectory of the battery pack during the charge and discharge process is obtained based on the battery pack's state of charge equation. This SOC trajectory is used as a reference trajectory for the rolling optimization of the prediction model.

[0030] In summary, the output power of fuel cells, battery packs, and photovoltaic power generation is determined through the energy management optimization model based on model predictive control.

[0031] In the above scheme, the energy balance equation of the multi-energy power system is as follows:

[0032]

[0033] In the formula, the sum of the power provided by the fuel cell, battery pack and photovoltaic power generation to the load meets the current load power demand.

[0034] In the above scheme, the state of charge equation of the battery pack is as follows:

[0035]

[0036] Where, Indicates the charge and discharge efficiency of the battery pack, It is k The time step of the phase.

[0037] In the above scheme, the power allocation strategy for photovoltaic power generation is as follows:

[0038] Adopting priority-based adaptive power allocation control, photovoltaic power generation is allocated according to the priority of different demands, and the allocation ratio of each demand is adjusted in real time according to the operating status of the system. The demands are divided into system voltage regulation demand, battery pack charging demand and load power demand, with the priority of the three decreasing in descending order.

[0039] System voltage regulation requirements have the highest priority, as follows:

[0040]

[0041] Where, The voltage stabilization power of photovoltaic power generation; The power required for system voltage stabilization; The voltage stabilization control coefficient is set based on the system design and operating experience to ensure that the voltage stabilization demand is met first. is the current voltage of the system; is the system reference voltage;

[0042] If there is surplus photovoltaic power and the battery charging conditions allow, it will be used to charge the battery pack:

[0043]

[0044] Where, The charging power of photovoltaic power; is the output power of photovoltaic power generation; The maximum charging power of the battery pack;

[0045] After ensuring the voltage stabilization power requirement, the remaining photovoltaic power is also used to meet the power requirements of the load:

[0046]

[0047] According to the system status and the fluctuation of photovoltaic power generation, the power distribution ratio of various requirements is automatically adjusted, that is, a weight coefficient is used To determine the power allocation ratio for various requirements, the weights are adjusted according to the real-time system status:

[0048]

[0049] Where, Indicates the photovoltaic power allocated to various needs; is the weight coefficient of various demands;

[0050] Different requirements dynamically adjust weight coefficients according to their priorities and needs : When the voltage regulation demand is high, the weight assigned to voltage regulation Increase; when the battery SOC is low and needs to be charged, the weight assigned to battery charging Increase; when the load power is insufficient, the weight assigned to the load output will improve;

[0051] The total power distribution must satisfy:

[0052]

[0053] That is, the sum of the photovoltaic power generation power allocated to various needs is equal to the output power of photovoltaic power generation.

[0054] In the above scheme, the minimized indicator function J Described as the sum of the difference between the output and the reference term and the weighted norm of the control input term in each stage:

[0055]

[0056] Where: is the state weight matrix; is the input penalty matrix; To measure the output, Indicates that in stage k For the future k + i Predicted measurement outputs of the phase; is the reference trajectory; is the control variable matrix, as shown below: U No. The stages are:

[0057]

[0058] Where: is the prediction length of the control variable;

[0059] During the optimization process, the following constraints are set.

[0060] Fuel cell output power constraints:

[0061]

[0062] Where, and The fuel cell power output must be within a specified range to avoid overloading or reducing efficiency.

[0063] Battery pack SOC constraint:

[0064]

[0065] Where, and are the minimum charge and maximum charge of the battery respectively. For battery k The state of charge of the battery pack must be kept within a safe range to extend its service life.

[0066] In the above scheme, the energy management optimization model based on model predictive control also includes feedback correction;

[0067] Feedback correction is used to correct the output of the prediction model in the rolling optimization process before optimizing at the next moment. The feedback correction formula is as follows:

[0068]

[0069] Where: is the optimized output matrix; Output error vector for the prediction model; is the error coefficient matrix.

[0070] In the above solution, the method further includes:

[0071] The fuel cell is modeled, and its output voltage and output power are:

[0072]

[0073]

[0074] Where: is the fuel cell monomer output voltage; is the open circuit voltage; is the activation voltage; is the ohmic voltage loss; Output power for the fuel cell; is the fuel cell current; the calculation method is as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Where: A is the Tafel slope; is the exchange current; is the scaling factor; is the response time; is the ohmic internal resistance of the fuel cell; is the electromotive force of the fuel cell; is the voltage constant under rated operating conditions; is the standard electromotive force; is the number of moving electrons; is the Faraday constant; ; is the partial pressure of hydrogen; is the partial pressure of oxygen; ideal gas constant; is Planck's constant; is an exponential term, which represents the effect of free energy change on the reaction rate; is the Gibbs free energy change, which represents the free energy change in the reaction; is the operating temperature; is the charge transfer coefficient;

[0082] Among them, the standard electromotive force of the fuel cell The calculation method is as follows:

[0083]

[0084] The relationship between gas pressure and the conversion rate of substances involved in the reaction:

[0085]

[0086]

[0087] Where, and are the conversion rates of hydrogen and oxygen, respectively; and are the supply pressures of hydrogen and oxygen respectively; and are the proportions of hydrogen and oxygen in fuel and air respectively;

[0088] Reactant conversion rate:

[0089]

[0090]

[0091] Where: is the fuel flow rate; is the air flow rate.

[0092] In the above solution, the method further includes:

[0093] Modeling of photovoltaic power generation, the output power of photovoltaic power generation :

[0094]

[0095] Where, is the overall efficiency of photovoltaic power generation; is the solar panel area; is the sunlight intensity;

[0096]

[0097]

[0098] Where: is the actual temperature of the solar panel; is the absolute temperature; Normal operating temperature of solar panels; is the temperature coefficient Reference efficiency of solar panels is the reference temperature of the solar panel The efficiency of maximum power point tracking for photovoltaic power generation;

[0099] Use log-normal distribution to randomly generate hourly light intensity data , the expression of lognormal distribution is as follows:

[0100]

[0101] Where, are the standard deviation and mean of solar radiation intensity data respectively; is the solar radiation intensity.

[0102] In the above solution, the method further includes:

[0103] Modeling the battery pack, the power of the battery cell and energy It is calculated by the following two formulas:

[0104]

[0105]

[0106] Where, 、 and They are the charge and discharge current, nominal voltage and rated capacity of the battery cell respectively;

[0107] The battery state of charge is determined using the ampere-hour integration method, which is calculated as follows:

[0108]

[0109]

[0110] Where, is the state of charge of the battery cell at time t; is the state of charge of the battery pack at time t; is the initial state of charge of the battery cell; ± represents the battery being charged or discharged; The charging and discharging efficiency of the battery cell; is the DC / DC conversion efficiency;

[0111] In summary, since the battery pack is composed of battery cells connected in series and parallel, some parameters of the battery pack are calculated using the following formula:

[0112]

[0113] Where, and are the number of batteries connected in series and parallel respectively; is the rated capacity of the battery pack; is the nominal voltage of the battery pack; The charging and discharging current of the battery pack; is the battery pack power; For battery energy.

[0114] According to a second aspect of the present invention, there is provided a ship, the ship adopting any one of the multi-energy energy management methods described above, wherein the multi-energy power system includes a fuel cell, a battery pack, and a photovoltaic power generation system;

[0115] The fuel cell system includes a hydrogen-to-methanol system, a methanol storage tank, a methanol reforming hydrogen production system, and a hydrogen fuel cell. The hydrogen-to-methanol system uses hydrogen generated by offshore wind farms and carbon dioxide from offshore carbon sequestration to synthesize methanol, achieving efficient storage and transportation of hydrogen energy. The methanol storage tank is used to store methanol. The methanol reforming hydrogen production system is connected to the methanol storage tank to reform methanol to produce hydrogen. The hydrogen fuel cell is connected to the methanol reforming hydrogen production system to use hydrogen to generate electricity and transmit it to the power grid.

[0116] Photovoltaic power generation converts solar energy into electrical energy, which is connected to the battery pack and the power grid to stabilize the system voltage and store the electrical energy in the battery pack;

[0117] The battery pack is connected to the power grid for transmitting electric energy to the power grid; the power grid is connected to the load.

[0118] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0119] (1) Ensuring safe storage and transportation of hydrogen: This invention converts hydrogen into methanol, utilizing methanol’s high energy density and liquid storage advantages to simplify the hydrogen storage and transportation process and improve conversion efficiency. This solves the problems of traditional hydrogen storage methods with numerous safety hazards and high technical requirements.

[0120] (2) Achieving efficient navigation of green ships: The present invention uses a methanol energy system integrated on a barge to synthesize methanol from the surplus hydrogen generated by an offshore wind farm, thereby achieving efficient storage and transportation of hydrogen energy. Methanol, as a high-energy-density liquid fuel, is converted into electricity through a reforming hydrogen fuel cell system, improving the environmental performance of the ship and ensuring the endurance requirements for long-distance navigation. This solves the problem of traditional ships relying on fossil fuels to cause serious environmental pollution, and at the same time overcomes the problem that existing new energy solutions such as electric systems are difficult to meet the needs of long-distance navigation due to the limited energy density of batteries.

[0121] (3) Optimizing the power system energy management strategy: This paper proposes a new type of new energy power system that integrates multiple energy sources and optimizes the energy management strategy in real time through model predictive control (MPC) to ensure efficient and stable energy use under various navigation conditions. This solves the problem that existing new energy ship power systems, which lack comprehensive energy management, affect navigation efficiency and stability. In particular, systems combining fuel cells, photovoltaics, and batteries, have problems such as energy waste and unstable output due to the inability to accurately predict energy demand and optimize energy distribution.

[0122] (4) Improving the stability of the power system of new energy ships: The present invention uses methanol as a key energy carrier through a multi-energy storage system integrated on the barge, combined with medium-voltage DC networking, photovoltaic power generation, lithium batteries, and hydrogen-to-methanol and methanol reforming hydrogen fuel cell technologies to build a multi-energy complementary hybrid platform. This integrated solution can intelligently manage the conversion and distribution between different energy sources based on actual energy demand and availability, thereby achieving a stable supply of power output. This solution effectively addresses the limitations of current new energy ships that are overly dependent on a single energy source, such as batteries or fuel cells, and solves the problem that such dependence can easily cause unstable power output when energy demand fluctuates, thereby affecting the normal operation and safety of the ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 This is a schematic diagram of the structure of the integrated energy system of light-hydrogen-methanol-lithium battery;

[0124] Figure 2 This is the network topology diagram of the ship power system based on light-lithium battery-hydrogen fuel cell;

[0125] Figure 3 Schematic diagram of the PEMFC system structure and working principle;

[0126] Figure 4 is the equivalent circuit diagram of the fuel cell;

[0127] Figure 5 This is a fuel cell simulation diagram;

[0128] Figure 6 This is the working principle diagram of photovoltaic power generation;

[0129] Figure 7 Three-view drawing of a barge integrating multiple energy sources;

[0130] Figure 8 This is a structural diagram of a barge that integrates multiple energy sources. DETAILED DESCRIPTION

[0131] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0132] The present invention aims to provide a new type of multi-energy integration and energy management system for ships and a new type of new energy supply and distribution management mode for ships. The energy supply process combines multiple energy storage forms such as methanol hydrogen storage, hydrogen fuel cells, photovoltaics, and lithium battery packs. Among them, methanol hydrogen storage is to convert hydrogen into methanol through a chemical reaction, and then achieve high-efficiency energy storage by transporting methanol, and then convert it into hydrogen and carbon dioxide when used. This technology has the advantages of being green, having a wide range of raw material sources, being easy to store and transport, and having relatively low energy consumption and costs. It is currently widely used in the fields of energy, chemical industry, transportation, etc., providing efficient and clean hydrogen supply for fuel cell systems, chemical production, and new energy energy reserves.

[0133] The allocation management model relies on the Model Predictive Control (MPC) model, an advanced control strategy based on model prediction and rolling optimization. It constructs a dynamic model of the system to predict future states and then solves an optimization problem within each control cycle to find the optimal control input based on the predicted results. MPC models, with their ability to handle complex system dynamics, constraints, and real-time feedback correction, are widely used in industrial control, robotic navigation, autonomous driving, and other fields. With increasing computing power and advanced optimization algorithms, their application is expected to expand further in the future.

[0134] The present invention aims to solve the following key technical problems existing in the application of existing new energy power systems in the field of ships:

[0135] (1) Challenges in hydrogen storage and transportation: Traditional hydrogen storage methods have many safety risks and high technical requirements. This invention simplifies the hydrogen storage and transportation process and improves conversion efficiency by converting hydrogen into methanol, taking advantage of methanol's high energy density and liquid storage properties.

[0136] (2) The balance between environmental protection and endurance: Traditional ships rely on fossil fuels, causing serious environmental pollution, while existing new energy solutions such as electric systems are limited by battery energy density and cannot meet the needs of long-distance navigation. The present invention uses a methanol energy system integrated on a barge to synthesize methanol using surplus hydrogen from offshore wind farms, thereby achieving efficient storage and transportation of hydrogen energy. Methanol, as a liquid fuel with high energy density, is converted into electrical energy through a reforming hydrogen fuel cell system, which not only improves the environmental performance of the ship, but also ensures the endurance requirements of long-distance navigation, providing a green and efficient energy solution for the shipping industry.

[0137] (3) Energy management issues in new energy power systems: Existing new energy ship power systems often lack comprehensive energy management, which affects navigation efficiency and stability. In particular, systems combining fuel cells, photovoltaics, and batteries cannot accurately predict energy demand and optimize energy distribution, resulting in energy waste and unstable output. To address this challenge, this paper proposes a new type of new energy power system that integrates multiple energy sources and optimizes energy management strategies in real time through model predictive control (MPC), ensuring efficient and stable energy use under various navigation conditions.

[0138] (4) Stability issues caused by reliance on a single energy source in the power system of new energy ships: Current new energy ships mostly rely on a single energy source such as batteries or fuel cells, which can easily lead to unstable power output under fluctuating energy demand, affecting the normal operation and safety of the ship. To this end, this technology uses a multi-energy storage system integrated on a barge, uses methanol as a key energy carrier, and combines medium-voltage DC networking, photovoltaic power generation, lithium batteries, and hydrogen-to-methanol and methanol reforming hydrogen fuel cell technologies to build a multi-energy complementary hybrid platform. This integrated solution can intelligently manage the conversion and distribution between different energy sources based on actual energy demand and availability, thereby achieving a stable supply of power output.

[0139] First, the present invention provides a multi-energy power system. Figure 1 、 Figure 7 and Figure 8 As shown, the barge integrates DC networking technology and a multi-faceted energy storage system. The hybrid platform includes a medium-voltage DC network, photovoltaic power generation, lithium batteries, a hydrogen-to-methanol system, methanol storage tanks, a methanol reforming fuel cell system, and loads. The lithium battery and fuel cell system provide high-efficiency and high-energy-density electricity storage and generation capabilities. Due to methanol's high energy density and its storage and transportation in liquid form, it offers a higher hydrogen storage density than high-pressure or low-temperature liquid hydrogen storage methods, and its transportation costs and difficulty are lower. The hydrogen-to-methanol system effectively utilizes surplus hydrogen generated by offshore wind farms and offshore stored CO2 to produce methanol, which is then stored in dedicated storage tanks onboard. During the power generation process, the methanol reforming system converts the hydrogen in the methanol back into hydrogen gas, which is then converted into electricity by the fuel cell, addressing the difficulties in transporting and storing hydrogen. The hydrogen fuel cell and lithium batteries power the barge's operational and self-propelled modes.

[0140] In order to make the ship more environmentally friendly and have better endurance, the present invention proposes the following Figure 2The network topology of the power system is shown in Figure 1. During navigation, after receiving sunlight, the photovoltaic panels distribute energy according to system requirements. Photovoltaic power is prioritized for system voltage regulation, lithium battery charging, loads, and propulsion output. The lithium battery pack acts as an energy storage device, storing the electricity generated by the solar cells and serving as a power source for the entire ship when required. The power distribution equipment primarily consists of an energy management device, various operating indicators such as an AC voltmeter, and an inverter.

[0141] (1) Fuel cell system

[0142] The working principle of PEMFC is relatively complex. Figure 3 Current research on fuel cell models focuses on dimensionality, time domain, and mechanistic models. Dimensionally, they can be categorized into one-, two-, and three-dimensional models. In the time domain, they can be further divided into steady-state and dynamic models. Mechanistically, they can be further divided into mechanistic and empirical models. Because data models cannot fully represent the structure and operating principles of PEMFC systems, this paper employs a modeling approach based on semi-empirical models. This approach combines mechanistic and data modeling to establish a PEMFC system model using a relatively small number of data samples.

[0143] The present invention adopts a dynamic stack model in the research process of fuel cell modeling. The equivalent circuit of the fuel cell is as follows Figure 4 As shown. Its output voltage and output power are:

[0144]

[0145]

[0146] Where: is the fuel cell monomer output voltage; is the open circuit voltage; is the activation voltage; is the ohmic voltage loss; is the fuel cell current (A). The calculation method is as follows:

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] Where: A is the Tafel slope; is the exchange current; is the scaling factor; is the response time; is the ohmic internal resistance of the fuel cell; is the electromotive force of the fuel cell; is the voltage constant under rated operating conditions; is the standard electromotive force; is the number of moving electrons; is the Faraday constant ; is the partial pressure of hydrogen (MPa); is the partial pressure of oxygen (MPa); is the ideal gas constant Planck constant is an exponential term, which represents the effect of free energy change on the reaction rate; is the Gibbs free energy change, which represents the free energy change in the reaction; is the operating temperature (K); is the charge transfer coefficient.

[0154] Among them, the standard electromotive force of the fuel cell The calculation method is as follows:

[0155]

[0156] The relationship between gas pressure and the conversion rate of substances involved in the reaction:

[0157]

[0158]

[0159] Where, and are the conversion rates of hydrogen and oxygen, respectively; and The supply pressures of hydrogen and oxygen respectively and are the proportions of hydrogen and oxygen in fuel and air respectively.

[0160] Reactant conversion rate:

[0161]

[0162]

[0163] Where: is the fuel flow rate Air flow rate . Established in MATLAB / Simulink simulation platform Figure 5 Model shown.

[0164] (2) Photovoltaic power generation system

[0165] The actual power generation capacity of a photovoltaic power generation system is not only affected by meteorological conditions, but also by factors such as component shading and battery mismatch. Figure 6 shown.

[0166] Photovoltaic power generation system output power as follows:

[0167]

[0168] Where, is the overall efficiency of the photovoltaic system; is the solar panel area; is the solar radiation intensity, in units of , and the geographical location and weather conditions fluctuate over time.

[0169]

[0170]

[0171] Where: is the actual temperature of the solar panel; is the absolute temperature; is the normal operating temperature of the solar panel, and its value is is the temperature coefficient, take is the reference efficiency of the solar panel, take is the reference temperature of the solar panel, which is is the efficiency of the maximum power point tracking of the photovoltaic system, which is taken as 1.

[0172] Considering the randomness of the output power of the photovoltaic system, this embodiment adopts a lognormal distribution to randomly generate hourly light intensity data. The expression of the lognormal distribution is as follows:

[0173]

[0174] Where, are the standard deviation and mean of solar radiation intensity data respectively; is the solar radiation intensity.

[0175] (3) Lithium battery system

[0176] Lithium-ion batteries consist of a negative electrode, separator, positive electrode, and electrolyte. During charging, lithium ions continuously release from the positive electrode active material, migrate to the negative electrode, and then react at the negative electrode interface before becoming embedded in the negative electrode material. The lithium ion migration during discharge is the opposite of the charging process. This directional movement of lithium ions generates an electric current within the battery. Considering the scale of application and environmental performance, this project selected lithium iron phosphate batteries as one of the components of the composite energy storage system.

[0177] Battery system expansion is primarily achieved through the series and parallel connection of battery cells. Therefore, modeling lithium battery cells is crucial. This section first numerically models the charge and discharge behavior of battery cells, and then develops a battery pack charge and discharge model based on the series and parallel connection.

[0178] Battery power and energy It can be calculated by the following two formulas:

[0179]

[0180]

[0181] Where, 、 and They are the charge and discharge current, nominal voltage and rated capacity of the battery cell, in units of A, V and Ah respectively.

[0182] As one of the key state parameters of lithium batteries, state of charge (SOC) can be used to indicate the remaining capacity of the battery at any given moment. It plays an important role in ship capacity configuration, energy management, preventing overcharge and over-discharge, and ensuring safety and longevity during use. Currently, common battery SOC estimation methods can be roughly divided into four categories: characterization parameter-based methods, ampere-hour integration methods, model-based methods, and data-driven methods. Among them, the ampere-hour integration method has been widely used due to its simplicity and ease of use. Its calculation method is as follows:

[0183]

[0184]

[0185] Where, is the state of charge of the battery cell at time t; is the state of charge of the battery pack at time t; is the initial state of charge of the battery cell; ± represents the battery being charged or discharged; The charging and discharging efficiency of the battery cell; is the DC / DC conversion efficiency.

[0186] In summary, since the battery pack is composed of battery cells connected in series and parallel, some parameters of the battery pack can be calculated using the following formula:

[0187]

[0188] Where, and are the number of batteries connected in series and parallel respectively; is the rated capacity of the battery pack, Ah; is the nominal voltage of the battery pack, V; is the battery pack charge and discharge current, A; is the battery pack power, W; is the battery pack energy, Wh.

[0189] Secondly, the present invention proposes an energy management optimization model based on model predictive control (MPC).

[0190] The energy management strategy developed for electric-photovoltaic hybrid ships with hydrogen fuel cells and lithium batteries as the main power sources is based on the following main ideas: while ensuring the stable operation of the electric-photovoltaic hybrid ship system, the lithium battery unit and fuel cell system are operated within their high efficiency range as much as possible, and the energy storage battery pack and photovoltaic power generation are used to supplement the insufficient power demand or absorb the excess photovoltaic power generation power to keep the battery pack at a high SOC value; in addition, when the battery pack is charged and discharged, the charge and discharge current must be maintained within the maximum limit to avoid overcharging and over-discharging, thereby extending the service life of the battery pack.

[0191] Because multi-energy parallel hybrid systems involve multiple distributed power sources, solving their real-time equivalent optimal hydrogen energy consumption is a key issue. While the hybrid system is operating, the state of charge (SOC) of the lithium-ion battery pack can be measured. Therefore, based on the current SOC and the reference power calculated using the FDP algorithm, the battery state equation can be used to calculate the reference SOC trajectory within the prediction domain. This is then used to achieve final power allocation based on MPC.

[0192] Model predictive control (MPC) offers high flexibility and no strict requirements on the form of the prediction model. It can easily account for complex optimal control problems with various constraints, multiple variables, and uncertainties, and exhibits excellent robustness and interference tolerance. MPC-based energy management strategies primarily consist of four components: a prediction model, rolling optimization, feedback correction, and a reference trajectory.

[0193] During navigation, the system generates electricity through the coordinated operation of photovoltaic power generation, fuel cells, and lithium batteries. The photovoltaic system provides partial power to the ship during the day, primarily for voltage stabilization, with the remaining energy stored in the lithium batteries. The fuel cells and lithium batteries provide continuous power for navigation and load consumption, with any excess energy stored in the lithium batteries. When power demand is high, the lithium batteries and fuel cells combine to provide energy, ensuring efficient output.

[0194] (1) Model Overview

[0195] The model manages three main energy sources:

[0196] Fuel cell: The main energy source, giving priority to meeting load demands.

[0197] Lithium battery energy storage system: can provide power for the system and store excess electricity.

[0198] Photovoltaic power generation system: mainly used for voltage stabilization, and excess electricity can be stored and supplied for navigation and load output.

[0199] (2) Prediction model

[0200] MPC dynamically adjusts the entire system by predicting future load demand and energy input. At each control stage k, based on the current state and past data, the future state is predicted and controlled accordingly.

[0201] Predicting future responses at k stages based on the system's historical information and future inputs is a crucial step in MPC. Given that the MPC algorithm performs calculations in stages, the hybrid system needs to be linearized and discretized. The following definitions are given: is the state variable of the model predictive control system; u is the control variable; For measurement input; is the measurement output.

[0202]

[0203]

[0204]

[0205]

[0206] Where: Is the state of charge of the lithium battery pack; M e yes k The equivalent hydrogen consumption corresponding to the total power consumption of the hybrid system in this stage; Provide load power for photovoltaic power generation; is the battery pack power; is the load power demand.

[0207] Since the MPC algorithm is calculated in stages, the system needs to be linearized and discretized. The state space form of the hybrid system after linearization and discretization is as follows:

[0208]

[0209]

[0210] Where: and are all coefficient matrices, and the following equation exists:

[0211]

[0212]

[0213]

[0214] in, and The specific matrix form is adjusted according to the actual dynamic characteristics of the system. The corresponding hydrogen consumption per unit time , can be fitted into a linear function, and the equation of the straight line obtained by fitting is:

[0215]

[0216] Where: and They are the two parameters of the linear function.

[0217] The energy balance equation of the system describes the relationship between load demand and energy output. The energy balance equation is as follows:

[0218]

[0219] Where: The sum of the power of fuel cells, photovoltaic power generation and lithium batteries must meet the current load demand.

[0220] The state of charge of a lithium battery is expressed by the following equation:

[0221]

[0222] Where: Indicates the charge and discharge efficiency of lithium batteries. It is k The time step of the phase.

[0223] To optimize the distribution of PV power within the vessel, priority-based adaptive power allocation control is employed. PV power is allocated based on the priority of different loads, and the allocation ratio for each load is adjusted in real time based on the system's operating status. Loads can be categorized as system voltage regulation (high priority), battery charging (medium priority), and power output (low priority).

[0224] System voltage stabilization has a higher priority, and the system voltage stability is the first priority. System voltage stabilization power requirements:

[0225]

[0226] Where, is the regulated power of photovoltaic; The power required for system voltage stabilization; The voltage stabilization control coefficient is set based on the system design and operating experience to ensure that the voltage stabilization demand is met first. is the current voltage of the system; is the system reference voltage.

[0227] If there is surplus photovoltaic power and battery charging conditions permit, it will be used to charge the battery:

[0228]

[0229] Where, The charging power of photovoltaic power; It is the maximum charging power of the battery. Charging is limited by the battery SOC.

[0230] After ensuring the voltage stabilization power demand, the remaining photovoltaic power is used for power output:

[0231]

[0232] To make power distribution more efficient, an adaptive power allocation algorithm is introduced. This algorithm automatically adjusts the power allocation ratio of each component by monitoring the system status (battery SOC, voltage deviation, power demand, etc.) and photovoltaic power generation fluctuations in real time.

[0233] Adaptive power allocation can use a weight-based adjustment formula. Assume we use a weight coefficient To determine the power distribution ratio of each load, the weight is adjusted according to the real-time system status:

[0234]

[0235] Where, Indicates the photovoltaic power distributed to the load; is the load weight coefficient.

[0236] Different loads are dynamically adjusted according to their priorities and needs When the voltage regulation demand is high, the weight assigned to voltage regulation is Increase; when the battery SOC is low and needs to be charged, the weight assigned to battery charging Increase; when the load power is insufficient, the weight assigned to the power output Will improve.

[0237] The total power distribution must satisfy:

[0238]

[0239] (3) Rolling optimization

[0240] The present invention converts the index function of MPC into J It is defined as the minimum difference between the measured output value of the process and the reference trajectory, achieving the lowest equivalent fuel consumption of the multi-energy hybrid system. J It can be described as the sum of the difference between the output item and the reference item in each stage and the weighted norm of the control input item:

[0241]

[0242] Where: is the state weight matrix; is the input penalty matrix; is the measurement output; is the reference trajectory; is the control variable matrix, as shown below: U No. The stages are:

[0243]

[0244] Where: is the prediction length of the control variable.

[0245] During the optimization process, the following constraints must be observed.

[0246] Fuel cell output power constraints:

[0247]

[0248] The power output of the fuel cell needs to be within a specified range to avoid overloading the fuel cell or reducing its efficiency.

[0249] Lithium battery SOC constraints:

[0250]

[0251] The state of charge of lithium batteries must be kept within a safe range to extend their service life.

[0252] (4) Feedback correction

[0253] It is difficult to establish a device model in a multi-energy hybrid system. In order to reduce the impact of errors on the optimization results, the output of the prediction model (the measured output obtained by rolling optimization) is fed back and corrected through the feedback correction link before the optimization at the next moment. The feedback correction formula is as follows:

[0254]

[0255] Where: is the optimized output matrix; Output error vector for the prediction model; is the error coefficient matrix.

[0256] (5) Reference trajectory

[0257] The energy management strategy of the present invention is a real-time optimization strategy. The reference trajectory is obtained by solving the prediction time domain based on the FDP algorithm and the ECMS optimization algorithm. The reference trajectory provides a dynamic adjustment target for the lithium battery charging and discharging strategy.

[0258] Since the multi-energy ship hybrid system studied has ship hydrogen fuel cells, photovoltaic power generation groups and lithium batteries, that is, the hybrid system consumes hydrogen energy and electricity energy, the optimization goal is to minimize the equivalent fuel consumption of the hybrid system, as shown in the following formula:

[0259]

[0260] Solving the above equation yields the optimal reference power for the lithium battery within the prediction domain. Based on the established lithium battery pack SOC prediction model, the SOC trajectory during the charge and discharge process is then derived. This SOC trajectory serves as a reference trajectory for rolling optimization of the prediction model. The reference trajectory is dynamically adjusted as the system state changes, ensuring the battery always maintains optimal operating conditions. Furthermore, this adjustment prevents over-discharge or over-charging of the lithium battery.

[0261] The calculation of the reference trajectory is performed simultaneously with the previous process and is used for rolling optimization. In this rolling optimization, the optimal power of photovoltaic power generation, lithium batteries, and hydrogen fuel cells can be solved based on the optimization objective, various constraints, and indicator functions.

[0262] In summary, this invention addresses the volatility of renewable energy and ensures a stable supply of electricity by integrating photovoltaic power generation, lithium batteries, hydrogen fuel cells, and methanol reforming systems into the ship's power system. Hydrogen-to-methanol technology effectively addresses the challenges of hydrogen storage and transportation. By preparing methanol and storing it onboard, hydrogen energy can be conveniently stored and converted. Furthermore, a model predictive control (MPC) strategy optimizes energy management in the multi-energy system, ensuring that the fuel cells and lithium batteries operate within their high-efficiency range, extending equipment life and improving system efficiency.

[0263] Furthermore, compressed hydrogen storage systems can replace methanol storage tanks and methanol reforming systems, directly storing hydrogen and converting it into electricity via fuel cells. While this increases hydrogen storage costs and transportation difficulties, it reduces the methanol preparation and reforming process. Supercapacitors can replace lithium batteries to provide short-term high power needs. Supercapacitors have fast charge and discharge speeds and are suitable for use in situations with large load fluctuations, but their lower energy density prevents them from completely replacing lithium batteries.

[0264] The fuel cell system and energy management system are at the core, ensuring sustained and efficient power. Lithium batteries or other energy storage devices are essential for storing excess power and balancing loads. A photovoltaic power generation system is optional for supplementing energy and stabilizing voltage, particularly suitable for energy support during long voyages.

[0265] Finally, the present invention also provides a ship, which adopts any one of the multi-energy energy management methods described above. Of course, the multi-energy power system and multi-energy energy management method of the present invention can be used in other equipment.

[0266] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0267] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, or two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0268] It will be easily understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-energy energy management method, applied to a multi-energy power system including fuel cells, battery packs and photovoltaic power generation, characterized in that: The method comprises: establishing an energy management optimization model based on model predictive control, including a prediction model, a rolling optimization and a reference trajectory; Predictive Models: Definition is the state variable of the model predictive control, u is the control variable, For measurement input, The measured output is as follows: Where: is the state of charge of the battery pack; M e yes k The equivalent hydrogen consumption corresponding to the total power consumption of the multi-energy power system in this stage; Provide load power for photovoltaic power generation; is the battery pack power; is the load power demand; The state space after linearization and discretization is as follows: Where, and They are x 、 u and y The form after linearization and discretization; and are all coefficient matrices, and the following equation exists: Among them, according to the fuel cell output power The corresponding hydrogen consumption per unit time , fitted as a linear function, the equation of the straight line obtained by fitting is: Where: and They are the two parameters of the linear function respectively; For battery pack energy; At the same time, the energy balance equation of the multi-energy power system, the state-of-charge equation of the battery pack, and the power distribution equation of photovoltaic power generation are established; among them, the energy balance equation of the multi-energy power system describes the relationship between the load power demand and the output power of the fuel cell, battery pack, and photovoltaic power generation; the state-of-charge equation of the battery pack describes the relationship between the state of charge of the battery pack and the output power of the battery pack; and the power distribution equation of photovoltaic power generation describes the power distribution strategy of photovoltaic power generation; Rolling optimization: Establish an indicator function and determine the fuel cell output power constraint and battery pack SOC constraint. The indicator function is defined as the minimum difference between the measured output value of the predictive control process and the reference trajectory. Reference trajectory: The multi-energy power system has the lowest equivalent hydrogen consumption, as shown in the following formula: The optimal reference power of the battery pack in the prediction time domain is obtained by solving the above equation. The SOC trajectory of the battery pack during the charge and discharge process is obtained based on the battery pack's state of charge equation. This SOC trajectory is used as a reference trajectory for the rolling optimization of the prediction model. In summary, the output power of fuel cells, battery packs, and photovoltaic power generation is determined through the energy management optimization model based on model predictive control.

2. The multi-energy energy management method according to claim 1, characterized in that: The energy balance equation of a multi-energy power system is as follows: In the formula, the sum of the power provided by the fuel cell, battery pack and photovoltaic power generation to the load meets the current load power demand.

3. The multi-energy management method according to claim 1, characterized in that: The state of charge equation of the battery pack is as follows: Where, Indicates the charge and discharge efficiency of the battery pack, It is k The time step of the phase.

4. The multi-energy energy management method according to claim 1, characterized in that: The power allocation strategy for photovoltaic power generation is as follows: Adopting priority-based adaptive power allocation control, photovoltaic power generation is allocated according to the priority of different demands, and the allocation ratio of each demand is adjusted in real time according to the operating status of the system. The demands are divided into system voltage regulation demand, battery pack charging demand and load power demand, with the priority of the three decreasing in descending order. System voltage regulation requirements have the highest priority, as follows: Where, The voltage stabilization power of photovoltaic power generation; The power required for system voltage stabilization; The voltage stabilization control coefficient is set based on the system design and operating experience to ensure that the voltage stabilization demand is met first. is the current voltage of the system; is the system reference voltage; If there is surplus photovoltaic power and the battery charging conditions allow, it will be used to charge the battery pack: Where, The charging power of photovoltaic power; is the output power of photovoltaic power generation; is the maximum charging power of the battery pack; After ensuring the voltage stabilization power requirement, the remaining photovoltaic power is also used to meet the power requirements of the load: According to the system status and the fluctuation of photovoltaic power generation, the power distribution ratio of various requirements is automatically adjusted, that is, a weight coefficient is used To determine the power allocation ratio for various requirements, the weights are adjusted according to the real-time system status: Where, Indicates the photovoltaic power allocated to various needs; is the weight coefficient of various demands; Different requirements dynamically adjust weight coefficients according to their priorities and needs : When the voltage regulation demand is high, the weight assigned to voltage regulation Increase; when the battery SOC is low and needs to be charged, the weight assigned to battery charging Increase; when the load power is insufficient, the weight assigned to the load output will improve; The total power distribution must satisfy: That is, the sum of the photovoltaic power generation power allocated to various needs is equal to the output power of photovoltaic power generation.

5. The multi-energy energy management method according to claim 1, characterized in that: Minimize the indicator function J Described as the sum of the difference between the output and the reference term and the weighted norm of the control input term in each stage: Where: is the state weight matrix; is the input penalty matrix; To measure the output, Indicates that in stage k For the future k + i Predicted measurement outputs of the phase; is the reference trajectory; is the control variable matrix, as shown below: U No. The stages are: Where: is the prediction length of the control variable; During the optimization process, the following constraints are set: Fuel cell output power constraints: Where, and The fuel cell power output must be within a specified range to avoid overloading or reducing efficiency. Battery pack SOC constraint: Where, and are the minimum charge and maximum charge of the battery respectively. For battery k The state of charge of the battery pack must be kept within a safe range to extend its service life.

6. The multi-energy energy management method according to claim 1, characterized in that: Energy management optimization model based on model predictive control, including feedback correction; Feedback correction is used to correct the output of the prediction model in the rolling optimization process before optimizing at the next moment. The feedback correction formula is as follows: Where: is the optimized output matrix; Output error vector for the prediction model; is the error coefficient matrix.

7. The multi-energy management method according to claim 1, characterized in that: The method further includes: The fuel cell is modeled, and its output voltage and output power are: Where: is the fuel cell monomer output voltage; is the open circuit voltage; is the activation voltage; is the ohmic voltage loss; Output power for the fuel cell; is the fuel cell current; the calculation method is as follows: Where: A is the Tafel slope; is the exchange current; is the scaling factor; is the response time; is the ohmic internal resistance of the fuel cell; is the electromotive force of the fuel cell; is the voltage constant under rated operating conditions; is the standard electromotive force; is the number of moving electrons; is the Faraday constant; ; is the partial pressure of hydrogen; is the partial pressure of oxygen; ideal gas constant; is Planck's constant; is an exponential term, which represents the effect of free energy change on the reaction rate; is the Gibbs free energy change, which represents the free energy change in the reaction; is the operating temperature; is the charge transfer coefficient; Among them, the standard electromotive force of the fuel cell The calculation method is as follows: The relationship between gas pressure and the conversion rate of substances involved in the reaction: Where, and are the conversion rates of hydrogen and oxygen, respectively; and are the supply pressures of hydrogen and oxygen respectively; and are the proportions of hydrogen and oxygen in fuel and air respectively; Reactant conversion rate: Where: is the fuel flow rate; is the air flow rate.

8. The multi-energy management method according to claim 1, characterized in that: The method further includes: Modeling of photovoltaic power generation, the output power of photovoltaic power generation : Where, is the overall efficiency of photovoltaic power generation; is the solar panel area; is the sunlight intensity; Where: is the actual temperature of the solar panel; is the absolute temperature; Normal operating temperature of solar panels; is the temperature coefficient Reference efficiency of solar panels is the reference temperature of the solar panel The efficiency of maximum power point tracking for photovoltaic power generation; Use log-normal distribution to randomly generate hourly light intensity data , the expression of lognormal distribution is as follows: Where, are the standard deviation and mean of solar radiation intensity data respectively; is the solar radiation intensity.

9. The multi-energy management method according to claim 1, characterized in that: The method further includes: Modeling the battery pack, the power of the battery cell and energy It is calculated by the following two formulas: Where, 、 and They are the charge and discharge current, nominal voltage and rated capacity of the battery cell respectively; The battery state of charge is determined using the ampere-hour integration method, which is calculated as follows: Where, is the state of charge of the battery cell at time t; is the state of charge of the battery pack at time t; is the initial state of charge of the battery cell; ± represents the battery being charged or discharged; The charging and discharging efficiency of the battery cell; is the DC / DC conversion efficiency; In summary, since the battery pack is composed of battery cells connected in series and parallel, some parameters of the battery pack are calculated using the following formula: Where, and are the number of batteries connected in series and parallel respectively; is the rated capacity of the battery pack; is the nominal voltage of the battery pack; The charging and discharging current of the battery pack; is the battery pack power; For battery energy.

10. A ship, characterized in that: The ship adopts the multi-energy energy management method according to any one of claims 1 to 9, wherein the multi-energy power system includes a fuel cell, a battery pack and photovoltaic power generation; The fuel cell system includes a hydrogen-to-methanol system, a methanol storage tank, a methanol reforming hydrogen production system, and a hydrogen fuel cell. The hydrogen-to-methanol system uses hydrogen generated by offshore wind farms and carbon dioxide from offshore carbon sequestration to synthesize methanol, achieving efficient storage and transportation of hydrogen energy. The methanol storage tank is used to store methanol; the methanol reforming hydrogen production system is connected to the methanol storage tank and is used to reform methanol to produce hydrogen; the hydrogen fuel cell is connected to the methanol reforming hydrogen production system and is used to use hydrogen to generate electricity and transmit it to the power grid; Photovoltaic power generation converts solar energy into electrical energy, which is connected to the battery pack and the power grid to stabilize the system voltage and store the electrical energy in the battery pack; The battery pack is connected to the power grid for transmitting electric energy to the power grid; the power grid is connected to the load.

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