Multi-energy energy management method and ship

Abstract

The invention discloses a multi-energy energy management method and a ship, the method is applied to a multi-energy power system comprising a fuel cell, a battery pack and photovoltaic power generation, an energy management optimization model based on model prediction control is established, and the energy management optimization model comprises a prediction model, rolling optimization and a reference trajectory. An energy management strategy is optimized in real time through model predictive control (MPC), conversion and distribution among different energy sources are intelligently managed according to actual energy source requirements and availability, and therefore stable supply of power output is achieved. A multi-energy complementary hybrid power platform is constructed by integrating a hydrogen fuel cell, a lithium battery and a photovoltaic power generation technology, an energy management strategy is optimized in real time through model predictive control (MPC), and efficient and stable use of energy under various navigation conditions is ensured.

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 management method and a ship. Background Art

[0003] Hydrogen fuel cell technology is a clean energy technology that directly converts the chemical energy of hydrogen and oxygen into electrical energy. It has the characteristics of no pollution, no noise and high efficiency. This technology has broad application prospects in the fields of automotive energy, aerospace energy and energy storage systems. With the advancement of technology, its cost has been greatly reduced and energy efficiency has been improved, which has promoted 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 charging and discharging. It has advantages such as high energy density and long life. At present, the demand for lithium batteries continues to expand. In the future, technological innovation will promote the improvement of lithium battery energy density and faster charging speed. At the same time, green and low-carbon will become the development trend of the industry, promoting the sustainable development of the lithium battery industry.

[0005] Photovoltaic technology is a technology that uses the photovoltaic effect of semiconductor interfaces to directly convert light energy into electrical energy. In recent years, photovoltaic technology has developed rapidly around the world, with significant technological progress. N-type high-efficiency batteries and large silicon wafer technology have driven cost reduction and efficiency improvement. Combined with key technologies such as high-efficiency batteries, intelligent operation and maintenance, and energy storage, it is accelerating the global energy transformation and sustainable development.

[0006] However, most of the current new energy ships 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. Although some new energy ships try to integrate multiple energy sources, the existing new energy ship power systems that integrate multiple energy sources often affect navigation efficiency and stability due to the lack of comprehensive energy management. In particular, those systems that combine fuel cells, photovoltaics and batteries have difficulty in accurately predicting energy demand and optimizing energy distribution, resulting in 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 technology to build a multi-energy complementary hybrid power 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 object, according to a first aspect of the present invention, there is provided a multi-energy energy management method, which is applied to a multi-energy power system including a fuel cell, a battery pack and a photovoltaic power generation, the method comprising: establishing an energy management optimization model based on model predictive control, including a prediction model, a rolling optimization and a reference trajectory;

[0009] Prediction model: Define x as the state variable of the model predictive control, u as the control variable, v as the measurement input, and y as the measurement output, as shown below:

[0010]

[0011] u=P LIB,pk #(twenty four)

[0012] v=P ref #(25)

[0013]

[0014] Where: SOC LIB,pk is the state of charge of the battery pack; M e is the equivalent hydrogen consumption corresponding to the total power consumption of the multi-energy power system in stage k; P pv_prop Provides load power for photovoltaic power generation; P LIB,pk is the battery pack power; P ref is the load power demand;

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

[0016] x(k+1)=A(k)x(k)+B u (k)u(k)+B v (k)#(27)

[0017] y(k)=C(k)x(k)#(28)

[0018] In the formula, x(k), u(k) and y(k) are the linearized and discretized forms of x, u and y respectively; A(k), B u (k), B v (k) and C(k) are both coefficient matrices, and the following equation exists:

[0019] A(k)=C(k)#(29)

[0020]

[0021] Among them, according to the fuel cell output power P fc The corresponding hydrogen consumption per unit time The fitting is a linear function, and the equation of the straight line is obtained after fitting:

[0022]

[0023] Where: m1 and m2 are the two parameters of the linear function; E LIB,pk For battery pack energy;

[0024] At the same time, the energy balance equation of the multi-energy power system, the charge state equation of the battery pack, and the power allocation 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 charge state equation of the battery pack describes the relationship between the charge state of the battery pack and the output power of the battery pack, and the power allocation equation of photovoltaic power generation describes the power allocation strategy of photovoltaic power generation;

[0025] Rolling optimization: Establish an index function and determine the fuel cell output power constraint and battery pack SOC constraint; the index function is defined as the minimum difference between the measured output value of the predictive control process and the reference trajectory;

[0026] Reference trajectory: The equivalent hydrogen consumption of the multi-energy power system is the lowest, as shown in formula (45):

[0027]

[0028] The optimal reference power of the battery pack in the prediction time domain is obtained by solving equation (45), and the SOC trajectory of the battery pack during the charging and discharging process is obtained based on the state of charge equation of the battery pack. The SOC trajectory is used as a reference trajectory for the rolling optimization of the prediction model.

[0029] 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.

[0030] In the above scheme, the energy balance equation of the multi-energy power system is as follows:

[0031] P ref (k) = P fc (k)+P pv_prop (k)+P LIB,pk (k)#(33)

[0032] 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.

[0033] In the above scheme, the charge state equation of the battery pack is as follows:

[0034] SOC LIB,pk (k+1)=SOC LIB,pk (k)+η bat ·PLIB,pk (k) Δt k #(34)

[0035] Where η bat Indicates the charge and discharge efficiency of the battery pack, Δt k is the time step of the kth phase.

[0036] In the above scheme, the power allocation strategy of photovoltaic power generation is as follows:

[0037] Adopting priority-based adaptive power allocation control, the power of 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 stabilization demand, battery pack charging demand and load power demand, and the priorities of the three are reduced in turn;

[0038] The system voltage regulation requirement has the highest priority, as follows:

[0039] P pv_stab =P stab =K stab ·(V ref -V sys )#(35)

[0040] Where P pv_stab is the regulated power of photovoltaic power generation; P stab The power required for system voltage regulation; K stab V is the voltage control coefficient, which is set according to the system design and operation experience to ensure that the voltage regulation demand is met first; V sys is the current voltage of the system; V ref is the system reference voltage;

[0041] If there is surplus power from photovoltaic power generation and the battery pack charging conditions allow, it will be used to charge the battery pack:

[0042] P pv_bat =min(P pv -P stab -P pv_prop ,P bat_max )#(36)

[0043] Where P pv_bat is the photovoltaic charging power; P pv is the output power of photovoltaic power generation; P bat_max is the maximum charging power of the battery pack;

[0044] After ensuring the voltage stabilization power requirement, the remaining photovoltaic power is also used to meet the power requirements of the load:

[0045] P pv_prop =min(P pv-P stab ,P ref -P fc -P LIB,pk )#(37)

[0046] According to the system status and the fluctuation of photovoltaic power generation, the power allocation ratio of various requirements is automatically adjusted, that is, a weight coefficient ω is used i To determine the power allocation ratio for various requirements, the weights are adjusted according to the real-time system status:

[0047] P load,i =ω i ·P pv #(38)

[0048] Where P load,i Represents the photovoltaic power allocated to various needs; ω i is the weight coefficient of various demands;

[0049] Different requirements dynamically adjust the weight coefficient ω according to their priority and needs i : When the voltage regulation demand is high, the weight assigned to voltage regulation ω stab Increase; when the battery SOC is low and needs to be charged, the weight ω assigned to battery charging bat Increase; when the load power provided is insufficient, the weight ω assigned to the load output prop will improve;

[0050] The total power distribution must satisfy:

[0051]

[0052] That is, the sum of the photovoltaic power generation allocated to various needs is equal to the output power of photovoltaic power generation.

[0053] In the above scheme, the minimized index function J is 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:

[0054]

[0055] Where: Q is the state weight matrix; R is the input penalty matrix; y is the measurement output, y(k+i|k) represents the predicted measurement output at stage k for the future k+i stage; y ref is the reference trajectory; U is the control variable matrix, as shown in formula (41), the i-th stage of U is:

[0056]

[0057] Where: N is the prediction length of the control variable;

[0058] During the optimization process, the following constraints are set.

[0059] Fuel cell output power constraints:

[0060] P fc_min ≤P fc (t)≤P fc_max #(42)

[0061] Where P fc_min and P fc_max The minimum and maximum values ​​of the fuel cell output power are respectively; the power output of the fuel cell needs to be within the specified range to avoid overloading or reducing the efficiency of the fuel cell;

[0062] Battery pack SOC constraint:

[0063] SOC min ≤SOC LIB (k)≤SOC max #(43)

[0064] In the formula, SOC min and SOC max They are the minimum charge and maximum charge of the battery, SOC LIB (k) is the charge of the battery at stage k; the charge state of the battery pack must be kept within a safe range to extend its service life.

[0065] In the above scheme, the energy management optimization model based on model predictive control also includes feedback correction;

[0066] Feedback correction is used to correct the output of the prediction model in the rolling optimization, and then the optimization is performed at the next moment; the feedback correction formula is as follows:

[0067] y corrected =y+λ er e er #(44)

[0068] Where: y corrected is the optimized output matrix; e er Output error vector for prediction model; λ er is the error coefficient matrix.

[0069] In the above scheme, the method further comprises:

[0070] The fuel cell is modeled, and its output voltage and output power are:

[0071] V fc =E oc -V act -V ohm #(1)

[0072] P fc =V fc ·i fc #(2)

[0073] Where: V fc is the fuel cell monomer output voltage; E oc is the open circuit voltage; V act is the activation voltage; V ohm is the ohmic voltage loss; P fc is the output power of the fuel cell; i fc is the fuel cell current; the calculation method is as follows:

[0074]

[0075] V ohm =r ohm ·i fc #(4)

[0076] V fc =Er ohm ·i fc #(5)

[0077] E=K C ·E n #(6)

[0078]

[0079] Where: A is the Tafel slope; i0 is the exchange current; s is the scaling factor; T d is the response time; r ohm is the ohmic internal resistance of the fuel cell; E is the electromotive force of the fuel cell; K c is the voltage constant under rated working conditions; E n is the standard electromotive force; z is the number of moving electrons; F is the Faraday constant; k is the reaction rate constant; is the partial pressure of hydrogen; is the partial pressure of oxygen; R is the ideal gas constant; h is Planck's constant; is an exponential term, which indicates the effect of free energy change on the reaction rate; ΔG is the Gibbs free energy change, which indicates the free energy change in the reaction; T is the working temperature; α is the charge transfer coefficient;

[0080] Among them, the standard electromotive force E of the fuel cell n The calculation method is as follows:

[0081]

[0082] The relationship between gas pressure and the conversion rate of substances involved in the reaction:

[0083]

[0084]

[0085] In the formula, Uf H2 and are the conversion rates of hydrogen and oxygen respectively; P fuel and P air are the supply pressures of hydrogen and oxygen respectively; x and y are the proportions of hydrogen and oxygen in the fuel and air respectively;

[0086] Reactant conversion rate:

[0087]

[0088] Where: V fuel is the fuel flow rate; V air is the air flow rate.

[0089] In the above scheme, the method further comprises:

[0090] Modeling photovoltaic power generation, the output power P of photovoltaic power generation pv :

[0091] P pv =η·S·G#(14)

[0092] In the formula, η is the overall efficiency of photovoltaic power generation; S is the area of ​​the solar panel; G is the intensity of sunlight;

[0093]

[0094] η=η * ·η MPPT ·[1-(β·T c -T ref )]#(16)

[0095] Where: T c is the actual temperature of the solar panel; T a is the absolute temperature; T * is the normal operating temperature of the solar panel; β is the temperature coefficient; η * is the reference efficiency of the solar panel; T ref is the reference temperature of the solar panel; η MPPT The efficiency of maximum power point tracking for photovoltaic power generation;

[0096] The hourly light intensity data F(x,u,σ) is randomly generated using the lognormal distribution. The expression of the lognormal distribution is as follows:

[0097]

[0098] Where u and σ are the standard deviation and mean value of solar radiation intensity data respectively; x is the solar radiation intensity.

[0099] In the above scheme, the method further comprises:

[0100] Model the battery pack, the power P of the battery cell LIB With energy E LIB Calculated by formula (18) and formula (19):

[0101] P LIB =I LIB ·U LIB #(18)

[0102] E LIB =C LIB ·U LIB #(19)

[0103] In the formula, I LIB , U LIB and C LIB They are the charge and discharge current, nominal voltage and rated capacity of the battery cell respectively;

[0104] The battery state of charge is determined using the ampere-hour integration method, which is calculated as follows:

[0105]

[0106] In the formula, SOC LIB (t) is the state of charge of the battery cell at time t; SOC LIB,pk (t) is the state of charge of the battery pack at time t; SOC LIB0 is the initial charge state of the battery cell; ± represents the battery being charged or discharged; η LIB is the battery monomer charge and discharge efficiency; η inv is the DC / DC conversion efficiency;

[0107] In summary, since the battery pack is composed of battery cells connected in series and parallel, some parameters of the battery pack are calculated by formula (22):

[0108]

[0109] Where N LIB,s With N LIB,p are the number of batteries connected in series and parallel; C LIB,pk is the rated capacity of the battery pack; U LIB,pk is the nominal voltage of the battery pack; I LIB,pk P is the battery pack charge and discharge current; LIB,pk is the battery pack power; R LIB,pk For battery energy.

[0110] According to a second aspect of the present invention, there is provided a ship, the ship adopts any one of the above-mentioned multi-energy energy management methods, the multi-energy power system comprising a fuel cell, a battery pack and a photovoltaic power generation;

[0111] The fuel cell 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 in offshore carbon sequestration to synthesize methanol to achieve 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;

[0112] Photovoltaic power generation converts solar energy into electrical energy, which is connected to the battery pack and the power grid for system voltage stabilization and stores the electrical energy in the battery pack;

[0113] The battery pack is connected to the power grid to transmit electric energy to the power grid; the power grid is connected to the load.

[0114] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0115] (1) Ensure safe storage and transportation of hydrogen energy: The present invention converts hydrogen into methanol, taking advantage of methanol’s high energy density and liquid storage, thus simplifying the storage and transportation process of hydrogen energy and improving conversion efficiency. This solves the problem of many safety hazards and high technical requirements in traditional hydrogen storage methods.

[0116] (2) Realize efficient navigation of green ships: The present invention uses a methanol energy system integrated on a barge to synthesize methanol using the surplus hydrogen generated by an offshore wind farm, thereby realizing 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, thereby 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 long-distance navigation requirements due to battery energy density limitations.

[0117] (3) Optimizing the energy management strategy of the power system: The present invention 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 use of energy under various navigation conditions. This solves the problem that the existing new energy ship power system affects navigation efficiency and stability due to the lack of comprehensive energy management, especially the system combining fuel cells, photovoltaics and batteries, which leads to energy waste and unstable output due to the inability to accurately predict energy demand and optimize energy distribution.

[0118] (4) Improving the stability of the power system of new energy ships: The present invention 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 according to 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 when energy demand fluctuates, this dependence can easily cause unstable power output, thereby affecting the normal operation and safety of the ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] Figure 1 This is a schematic diagram of the structure of the integrated energy system of light-hydrogen-methanol-lithium battery;

[0120] Figure 2 This is the network topology diagram of the ship power system based on light-lithium battery-hydrogen fuel cell;

[0121] Figure 3 It is a schematic diagram of the PEMFC system structure and working principle;

[0122] Figure 4 is the equivalent circuit diagram of the fuel cell;

[0123] Figure 5 This is a fuel cell simulation diagram;

[0124] Figure 6 This is the working principle diagram of photovoltaic power generation;

[0125] Figure 7 Three-view drawing of a barge integrating multiple energy sources;

[0126] Figure 8 This is a structural diagram of a barge that integrates multiple energy sources. DETAILED DESCRIPTION

[0127] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain 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 can be combined with each other as long as they do not conflict with each other.

[0128] The present invention aims to provide a new type of ship multi-energy integration and energy management system and a new type of ship new energy energy supply and distribution management mode. The energy supply process combines various 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 chemical reactions, and then achieve efficient 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 source of raw materials, 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., and provides efficient and clean hydrogen supply for fuel cell systems, chemical production, and new energy energy reserves.

[0129] The allocation management mode relies on the model predictive control (MPC) model. The MPC model is an advanced control strategy based on model prediction and rolling optimization. It predicts the future state by building a system dynamic model and solves the optimization problem in each control cycle based on the prediction results to find the optimal control input. The MPC model is widely used in industrial control, robot navigation, autonomous driving and other fields because of its ability to handle complex system dynamics, constraints and real-time feedback correction. With the improvement of computing power and the development of optimization algorithms, its application scope is expected to be further expanded in the future.

[0130] 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:

[0131] (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 the conversion efficiency by converting hydrogen into methanol and taking advantage of methanol’s high energy density and liquid storage.

[0132] (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 are difficult to meet long-distance navigation needs. The present invention uses a methanol energy system integrated with 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.

[0133] (3) Energy management issues of new energy power systems: Existing new energy ship power systems often affect navigation efficiency and stability due to the lack of comprehensive energy management. 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 meet this challenge, the present invention 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) to ensure efficient and stable use of energy under various navigation conditions.

[0134] (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 barge-integrated multi-energy storage system, 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 technology 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.

[0135] First, the present invention provides a multi-energy power system. Figure 1 , Figure 7 and Figure 8As shown, the barge integrates DC networking technology and multi-energy storage system, and the hybrid platform includes: medium-voltage DC networking, photovoltaic power generation, lithium battery, hydrogen-to-methanol system, methanol storage tank, methanol reforming hydrogen fuel cell system, load and other parts. The lithium battery and fuel cell system provide the system with high-efficiency and high-energy-density power storage and generation capabilities. Due to the high energy density of methanol and its storage and transportation in liquid form, it has a higher hydrogen storage energy density than high-pressure or low-temperature liquid hydrogen storage methods, and the transportation cost and difficulty of hydrogen storage are also lower. The barge can effectively use the surplus hydrogen energy generated by offshore wind farms and the offshore carbon dioxide to produce methanol through the hydrogen-to-methanol system, and store the methanol in a dedicated storage tank on board. In the power generation process, the hydrogen energy in methanol is converted back into hydrogen through the methanol reforming hydrogen production system, and then the hydrogen is converted into electrical energy through the fuel cell, solving the problem of difficult transportation and storage of hydrogen. The hydrogen fuel cell and lithium battery provide power for the operation mode and self-propelled mode of the barge.

[0136] In order to make the ship more environmentally friendly and have better endurance, the present invention proposes the following Figure 2 The network topology of the power system shown in the figure. During the voyage of the ship, after receiving sunlight, the photovoltaic modules distribute energy according to the system requirements, and the photovoltaic power generation power is used for system voltage stabilization, lithium battery charging, load and propulsion output according to priority. As an energy storage device, the lithium battery pack stores the electric energy generated by the solar cell on the one hand, and serves as the energy source of the entire ship under specific needs on the other hand. The power distribution equipment is mainly composed of energy management devices, AC voltmeters and other operating indicator instruments and inverters.

[0137] (1) Fuel cell system

[0138] The working principle of PEMFC is relatively complex. Figure 3 As shown. The current research on fuel cell models is reflected in the dimensions, time domain and mechanism models. In terms of dimensions, it can be divided into one-dimensional, two-dimensional and three-dimensional models, while in the time domain it can be divided into steady-state models and dynamic models, and in terms of mechanism it can be divided into mechanism models and empirical models. Since the data model cannot fully display the structure and working principle of the PEMFC system, the present invention adopts a modeling method based on a semi-empirical model, that is, the mechanism model is combined with the data modeling to establish a model of the PEMFC system through fewer data samples.

[0139] The present invention adopts a dynamic stack model in the process of research on fuel cell modeling. The equivalent circuit of the fuel cell is as follows: Figure 4 As shown. Its output voltage and output power are:

[0140] V fc =E oc -V act -V ohm #(1)

[0141] P fc =V fc ·i fc #(2)

[0142] Where: V fc E is the output voltage of the fuel cell monomer; oc is the open circuit voltage; V act is the activation voltage; V ohm is the ohmic voltage loss; i fc is the fuel cell current (A). The calculation method is as follows:

[0143]

[0144] V ohm =r ohm ·i fc #(4)

[0145] V fc =Er ohm ·i fc #(5)

[0146] E=K C ·E n #(6)

[0147]

[0148] Where: A is the Tafel slope; i0 is the exchange current; s is the scaling factor; T d is the response time; r ohm is the ohmic internal resistance of the fuel cell; E is the electromotive force of the fuel cell; K c is the voltage constant under rated working conditions; E n is the standard electromotive force; z is the number of moving electrons; F is the Faraday constant 96485C / mol; k is the reaction rate constant; P H2 is the partial pressure of hydrogen (MPa); is the partial pressure of oxygen (MPa); R is the ideal gas constant 8.3145 J / (molK); h is Planck's constant 6.626×10 -34 J.s; is an exponential term, which indicates the effect of free energy change on reaction rate; ΔG is the Gibbs free energy change, which indicates the free energy change in the reaction; T is the working temperature (K); α is the charge transfer coefficient.

[0149] Among them, the standard electromotive force E of the fuel cell n The calculation method is as follows:

[0150]

[0151] The relationship between gas pressure and the conversion rate of substances involved in the reaction:

[0152]

[0153]

[0154] In the formula, Uf H2 and are the conversion rates of hydrogen and oxygen respectively; P fuel and P air are the supply pressures of hydrogen and oxygen (MPa), respectively; x and y are the proportions of hydrogen and oxygen in the fuel and air, respectively.

[0155] Reactant conversion rate:

[0156]

[0157] Where: V fuel is the fuel flow rate (L / min); V air Air flow rate (L / min). Established in MATLAB / Simulink simulation platform Figure 5 Model shown.

[0158] (2) Photovoltaic power generation system

[0159] 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.

[0160] Photovoltaic power generation system output power P pv as follows:

[0161] P pv =η·S·G#(14)

[0162] Where η is the overall efficiency of the photovoltaic system; S is the area of ​​the solar panel; G is the solar intensity, in w / m 2 , and the geographical location and weather conditions fluctuate over time.

[0163]

[0164] η=η * ·η MPPT ·[1-(β·T c -T ref )]#(16)

[0165] Where: T c is the actual temperature of the solar panel; T a is the absolute temperature; T *is the normal operating temperature of the solar panel, which is 45°C; β is the temperature coefficient, which is 0.05; η * is the reference efficiency of the solar panel, which is 0.17; T ref is the reference temperature of the solar panel, which is 25; η MPPT is the efficiency of the maximum power point tracking of the photovoltaic system, which is taken as 1.

[0166] Considering the randomness of the output power of the photovoltaic system, this embodiment adopts a log-normal distribution method to randomly generate hourly light intensity data. The expression of the log-normal distribution is as follows:

[0167]

[0168] Where u and σ are the standard deviation and mean value of solar radiation intensity data respectively; x is the solar radiation intensity.

[0169] (3) Lithium battery system

[0170] Lithium-ion batteries are composed of negative electrodes, separators, positive electrodes, and electrolytes. When charging, lithium ions continuously escape from the positive electrode active material, migrate to the negative electrode, and react at the negative electrode interface before being embedded in the negative electrode material. The lithium ion migration during the discharge process is the opposite of the above charging process. During the directional movement of lithium ions, current is formed inside the battery. Considering the application scale and environmental performance, this project selects lithium iron phosphate batteries as one of the components of the composite energy storage system.

[0171] The expansion of the battery system scale is mainly achieved through the series and parallel connection of battery cells. Therefore, it is very important to model the lithium battery cells. This section first numerically models the charging and discharging behavior of battery cells, and then obtains the battery pack charging and discharging model based on the series and parallel connection.

[0172] Power P of battery cell LIB With energy E LIB It can be calculated by formula (18) and formula (19):

[0173] P LIB =I LIB ·U LIB #(18)

[0174] E LIB =C LIB ·U LIB #(19)

[0175] In the formula, I LIB , U LIB and C LIB They are the charge and discharge current, nominal voltage and rated capacity of the battery cell, in units of A, V and Ah respectively.

[0176] As one of the key status parameters of lithium batteries, the state of charge can be used to indicate the remaining capacity of the battery at any moment. It plays an important role in the capacity configuration of the entire ship, energy management, preventing overcharge and over-discharge, and ensuring safety and long life during use. Currently, common battery state of charge 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:

[0177]

[0178] In the formula, SOC LIB (t) is the state of charge of the battery cell at time t; SOC LiB,pk (t) is the state of charge of the battery pack at time t; SOC LIB0 is the initial charge state of the battery cell; ± represents the battery being charged or discharged; η LIB is the battery monomer charge and discharge efficiency; η inv is the DC / DC conversion efficiency.

[0179] 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 by formula (22):

[0180]

[0181] Where N LIB,s With N LIB,p are the number of batteries connected in series and parallel; C LIB,pk is the rated capacity of the battery pack, Ah; U LIB,pk is the nominal voltage of the battery pack, V; I LIB,pk is the battery pack charge and discharge current, A; P LIB,pk is the battery pack power, W; E LIB,pk is the battery pack energy, Wh.

[0182] Secondly, the present invention proposes an energy management optimization model based on model predictive control (MPC).

[0183] Taking the electric-photovoltaic hybrid ship with hydrogen fuel cells and lithium batteries as the main power source as the object, the main idea of ​​the energy management strategy developed is as follows: under the condition of ensuring the stable operation of the electric-photovoltaic hybrid ship system, the lithium battery unit and the fuel cell system are operated in 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 range to avoid overcharging and over-discharging, thereby extending the service life of the battery pack.

[0184] Since there are multiple distributed power sources in the multi-energy parallel hybrid system, solving its real-time equivalent optimal hydrogen energy consumption is a key issue. When the hybrid system is running, the SOC of the lithium battery pack can be measured. Therefore, the reference trajectory of the lithium battery SOC in the prediction domain can be calculated based on the lithium battery state equation according to the current lithium battery SOC and the lithium battery reference power solved based on the FDP algorithm, and then the final power distribution can be achieved based on MPC.

[0185] Model predictive control has high flexibility and no strict requirements on the form of the prediction model. It can easily consider various constraints, multiple variables and uncertain complex optimal control problems, and has good robustness and anti-interference capabilities. The energy management strategy based on MPC mainly includes four parts: prediction model, rolling optimization, feedback correction and reference trajectory.

[0186] During the ship's voyage, the system provides electricity through the coordinated work of photovoltaic power generation, fuel cells and lithium batteries. The photovoltaic system provides part of the power for the ship during the day, mainly for voltage stabilization, and the remaining power is stored in the lithium battery. Fuel cells and lithium batteries serve as a continuous power source to provide power for ship navigation and load, and the surplus power is stored in the lithium battery. When the power demand is high, the lithium battery and fuel cell jointly supply energy to ensure efficient output.

[0187] (1) Model Overview

[0188] The model manages three main energy sources:

[0189] Fuel cell: Main energy source, prioritizes load demand.

[0190] Lithium battery energy storage system: can provide power for the system and store excess electrical energy.

[0191] Photovoltaic power generation system: mainly used for voltage stabilization, and excess electricity can be stored and supplied for navigation and load output.

[0192] (2) Prediction Model

[0193] MPC dynamically adjusts the entire system by predicting future load demand and energy input. In each control stage k, based on the current state and past data, the future state is predicted and controlled accordingly.

[0194] Predicting future responses in the kth stage based on the system's historical information and future inputs is a very important part of MPC. Since the MPC algorithm is calculated in stages, the hybrid system needs to be linearized and discretized. The following definitions are given: x is the state variable of the model predictive control system; u is the control variable; v is the measured input; y is the measured output.

[0195]

[0196] u=P LIB,pk #(twenty four)

[0197] v=P ref #(25)

[0198]

[0199] Where: SOC LIB,pk is the state of charge of the lithium battery pack; M e is the equivalent hydrogen consumption corresponding to the total power consumption of the hybrid system in stage k; P pv_prop Provides load power for photovoltaic power generation; P LIB,pk is the battery pack power; P ref is the load power demand.

[0200] 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:

[0201] x(k+1)=A(k)x(k)+B u (k)u(k)+B v (k)#(27)

[0202] y(k)=C(k)x(k)#(28)

[0203] Where: A(k), B u (k), B v (k) and C(k) are both coefficient matrices, and the following equation exists:

[0204] A(k)=C(k)#(29)

[0205]

[0206] Among them, the specific matrix forms of A(k) and C(k) are adjusted according to the actual dynamic characteristics of the system. According to the output power P of the hydrogen fuel cell fc The corresponding hydrogen consumption per unit time It can be fitted into a linear function, and the equation of the straight line obtained by fitting is:

[0207]

[0208] Where: m1 and m2 are the two parameters of the linear function.

[0209] The energy balance equation of the system describes the relationship between the load demand and the output of each energy source. The energy balance equation is as follows:

[0210] P ref (k) = P fc (k)+P pv_prop (k)+P LIB,pk (k)#(33)

[0211] Where: The sum of the power of the fuel cell, photovoltaic power generation and lithium battery must meet the current load demand.

[0212] The state of charge of a lithium battery is expressed by the following equation:

[0213] SOC LIB,pk (k+1)=SOC LIB,pk (k)+η bat ·P LIB,pk (k) Δt k #(34)

[0214] Where: η bat Indicates the charging and discharging efficiency of lithium batteries, Δt k is the time step of the kth phase.

[0215] In order to optimize the distribution of ship photovoltaic power, priority-based adaptive power distribution control is adopted. The power of photovoltaic power generation is distributed according to the priority of different loads, and the distribution ratio of each load is adjusted in real time according to the operating status of the system. The loads can be divided into system voltage regulation requirements (high priority), battery charging (medium priority), and power output requirements (low priority).

[0216] System voltage stabilization has a higher priority, and the voltage stability of the system is ensured first. System voltage stabilization power requirements:

[0217] P pv_stab =P stab =K stab ·(V ref -V sys )#(35)

[0218] Where P pv_stab is the photovoltaic voltage regulation power; P stab The power required for system voltage regulation; K stab V is the voltage control coefficient, which is set according to the system design and operation experience to ensure that the voltage regulation demand is met first; V sys is the current voltage of the system; V ref is the system reference voltage.

[0219] If there is surplus PV power and the battery charging conditions permit, it is used to charge the battery:

[0220] P pv_bat =min(P pv -P stab-P pv_prop ,P bat_max )#(36)

[0221] Where P pv_bat is the photovoltaic charging power; P bat_max It is the maximum charging power of the battery, and the charging is limited by the battery SOC.

[0222] After ensuring the voltage stabilization power demand, the remaining photovoltaic power is used for power output:

[0223] P pv_prop =min(P pv -P stab ,P ref -P fc -P LIB,pk )#(37)

[0224] In order to make power allocation more efficient, an adaptive power allocation algorithm is introduced. This algorithm automatically adjusts the power allocation ratio of each part by real-time monitoring of system status (battery SOC, voltage deviation, power demand, etc.) and photovoltaic power generation fluctuations.

[0225] Adaptive power allocation can use a weight-based adjustment formula. Assume that we use a weight coefficient ω i To determine the power distribution ratio of each load, the weight is adjusted according to the real-time system status:

[0226] P load,i =ω i ·P pv #(38)

[0227] Where P load,i Represents the photovoltaic power distributed to the load; ω i is the load weight coefficient.

[0228] Different loads are dynamically adjusted according to their priorities and needs i When the voltage regulation demand is high, the weight assigned to voltage regulation ω stab Increase; when the battery SOC is low and needs to be charged, the weight ω assigned to battery charging bat Increase; when the load power provided is insufficient, the weight ω allocated to the power output prop Will improve.

[0229] The total power distribution must satisfy:

[0230]

[0231] (3) Scrolling Optimization

[0232] The present invention defines the MPC index function J 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 power system. The minimized index function J 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:

[0233]

[0234] Where: Q is the state weight matrix; R is the input penalty matrix; y is the measured output; y ref is the reference trajectory; U is the control variable matrix, as shown in formula (12), the i-th stage of U is:

[0235]

[0236] Where: N is the prediction length of the control variable.

[0237] During the optimization process, the following constraints must be observed.

[0238] Fuel cell output power constraints:

[0239] P fc_min ≤P fc (t)≤P fc_max #(42)

[0240] The power output of the fuel cell needs to be within a specified range to avoid overloading the fuel cell or reducing its efficiency.

[0241] Lithium battery SOC constraints:

[0242] SOC min ≤SOC LIB (k)≤SOC max #(43)

[0243] The state of charge of lithium batteries must be kept within a safe range to extend their service life.

[0244] (4) Feedback correction

[0245] It is difficult to establish the equipment model in the 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 feedback-corrected through the feedback correction link before the optimization at the next moment. The feedback correction formula is as follows:

[0246] y corrected =y+λ er e er #(44)

[0247] Where: y corrected is the optimized output matrix; e erOutput error vector for prediction model; λ er is the error coefficient matrix.

[0248] (5) Reference trajectory

[0249] The energy management strategy of the present invention is a real-time optimization strategy, and 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 dynamically adjusted target for the charging and discharging strategy of the lithium battery.

[0250] Since the multi-energy ship hybrid power system studied has ship hydrogen fuel cells, photovoltaic power generation groups and lithium batteries at the same time, that is, the hybrid power system consumes hydrogen energy and electricity energy, the optimization target is designed to minimize the equivalent fuel consumption of the hybrid power system, as shown in formula (45):

[0251]

[0252] The optimal reference power of the lithium battery in the prediction domain is obtained by solving equation (45), and the SOC trajectory of the lithium battery charge and discharge process is obtained based on the established lithium battery pack SOC prediction model. The SOC trajectory will be used as a reference trajectory for the rolling optimization of the prediction model. The reference trajectory is dynamically adjusted as the system state changes to ensure that the battery always remains in the optimal working state. At the same time, the adjustment of the reference trajectory can prevent over-discharge or over-charging of the lithium battery.

[0253] The calculation of the reference trajectory is carried out simultaneously with the previous process, and the reference trajectory needs to be used for rolling optimization. In the rolling optimization, according to equation (45) and various constraints and indicator functions, the optimal power of photovoltaic power generation, lithium battery, and hydrogen fuel cell can be solved.

[0254] In summary, the present invention solves the volatility problem 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. The hydrogen-to-methanol technology effectively solves the problem of hydrogen storage and transportation. By preparing methanol and storing it on board, convenient storage and conversion of hydrogen energy is achieved. In addition, the energy management of the multi-energy system is optimized based on the model predictive control (MPC) strategy, ensuring that the fuel cells and lithium batteries work within the high-efficiency range, extending the equipment life and improving the system efficiency.

[0255] In addition, a compressed hydrogen storage system can be used to replace the methanol storage tank and methanol reforming system to directly store hydrogen and convert it into electricity through a fuel cell. Although the cost of hydrogen storage and the difficulty of transportation increase, the process of methanol preparation and reforming is reduced. Supercapacitors can be used instead of lithium batteries to provide short-term high power needs. Supercapacitors have fast charging and discharging speeds and are suitable for situations with large load fluctuations, but their energy density is low and they cannot completely replace lithium batteries.

[0256] Fuel cell system and energy management system are the core to ensure the durability and efficiency of power; lithium batteries or other energy storage devices are essential to store excess power and balance loads. Photovoltaic power generation system can be used as an optional part to supplement energy and stabilize voltage, especially for energy support during long-term sailing.

[0257] 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.

[0258] 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 the present application.

[0259] 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, and 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.

[0260] It will be easily understood by those skilled in the art that the above are only 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 protection scope 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; Prediction model: Define x as the state variable of the model predictive control, u as the control variable, v as the measurement input, and y as the measurement output, as shown below: u=P LIB,pk #(24) v=P ref #(25) Where: SOC LIB,pk is the state of charge of the battery pack; M e is the equivalent hydrogen consumption corresponding to the total power consumption of the multi-energy power system in stage k; P pv_prop Provides load power for photovoltaic power generation; P LIB,pk is the battery pack power; P ref is the load power demand; The state space form after linearization and discretization is as follows: x(k+1)=A(k)x(k)+B u (k)u(k)+B v (k)#(27) y(k)=C(k)x(k)#(28) In the formula, x(k), u(k) and y(k) are the linearized and discretized forms of x, u and y respectively; A(k), B u (k), B v (k) and C(k) are both coefficient matrices, and the following equation exists: A(k)=C(k)#(29) Among them, according to the fuel cell output power P fc The corresponding hydrogen consumption per unit time The fitting is a linear function, and the equation of the straight line is obtained after fitting: Where: m1 and m2 are the two parameters of the linear function; E LIB,pk For battery pack energy; At the same time, the energy balance equation of the multi-energy power system, the charge state equation of the battery pack, and the power allocation 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 charge state equation of the battery pack describes the relationship between the charge state of the battery pack and the output power of the battery pack, and the power allocation equation of photovoltaic power generation describes the power allocation strategy of photovoltaic power generation; Rolling optimization: Establish an index function and determine the fuel cell output power constraint and battery pack SOC constraint; the index function is defined as the minimum difference between the measured output value of the predictive control process and the reference trajectory; Reference trajectory: The equivalent hydrogen consumption of the multi-energy power system is the lowest, as shown in formula (45): The optimal reference power of the battery pack in the prediction time domain is obtained by solving equation (45), and the SOC trajectory of the battery pack during the charging and discharging process is obtained based on the state of charge equation of the battery pack. The 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 management method according to claim 1, characterized in that: The energy balance equation of the multi-energy power system is as follows: P ref (k)=P fc (k)+P pv_prop (k)+P LIB,pk (k)#(33) 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: SOCIETY LIB,pk (k+1)=SOC LIB,pk (k)+η bat ·P LIB,pk (k)·Δt k #(34) Where η bat Indicates the charge and discharge efficiency of the battery pack, Δt k is the time step of the kth phase.

4. The multi-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, the power of 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 stabilization demand, battery pack charging demand and load power demand, and the priorities of the three are reduced in turn; The system voltage regulation requirement has the highest priority, as follows: P pv_stab =P stab =K stab ·(V ref -V sys )#(35) Where P pv_stab is the regulated power of photovoltaic power generation; P stab The power required for system voltage regulation; K stab V is the voltage control coefficient, which is set according to the system design and operation experience to ensure that the voltage regulation demand is met first; V sys is the current voltage of the system; V ref is the system reference voltage; If there is surplus power from photovoltaic power generation and the battery pack charging conditions allow, it will be used to charge the battery pack: P pv_bat =min(P pv -P stab -P pv_prop ,P bat_max )#(36) Where P pv_bat is the photovoltaic charging power; P pv is the output power of photovoltaic power generation; P bat_max 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: P pv_prop =min(P pv -P stab ,P ref -P fc -P LIB,pk )#(37) According to the system status and the fluctuation of photovoltaic power generation, the power allocation ratio of various requirements is automatically adjusted, that is, a weight coefficient ω is used i To determine the power allocation ratio for various requirements, the weights are adjusted according to the real-time system status: P load,i =ω i ·P pv #(38) Where P load,i Represents the photovoltaic power allocated to various needs; ω i is the weight coefficient of various demands; Different requirements dynamically adjust the weight coefficient ω according to their priority and needs i : When the voltage regulation demand is high, the weight assigned to voltage regulation ω stab Increase; when the battery SOC is low and needs to be charged, the weight ω assigned to battery charging bat Increase; when the load power provided is insufficient, the weight ω assigned to the load output prop will improve; The total power distribution must satisfy: That is, the sum of the photovoltaic power generation allocated to various needs is equal to the output power of photovoltaic power generation.

5. The multi-energy management method according to claim 1, characterized in that: The minimized index function J is 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: Where: Q is the state weight matrix; R is the input penalty matrix; y is the measurement output, y(k+i|k) represents the predicted measurement output at stage k for the future k+i stage; y ref is the reference trajectory; U is the control variable matrix, as shown in formula (41), the i-th stage of U is: Where: N is the prediction length of the control variable; During the optimization process, the following constraints are set. Fuel cell output power constraints: P fc_min ≤P fc (t)≤P fc_max #(42) Where P fc_min and P fc_max The minimum and maximum values ​​of the fuel cell output power are respectively; the power output of the fuel cell needs to be within the specified range to avoid overloading or reducing the efficiency of the fuel cell; Battery pack SOC constraint: SOC min ≤SOC LIB (k)≤SOC max #(43) In the formula, SOC min and SOC max They are the minimum charge and maximum charge of the battery, SOC LIB (k) is the charge of the battery at stage k; the charge state 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, also including feedback correction; Feedback correction is used to correct the output of the prediction model in the rolling optimization, and then the optimization is performed at the next moment; the feedback correction formula is as follows: and corrected =y+λ er and er #(44) Where: y corrected is the optimized output matrix; e er Output error vector for prediction model; λ er 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: V fc =E oc -V act -V ohm #(1) P fc =V fc ·i fc #(2) Where: V fc is the fuel cell monomer output voltage; E oc is the open circuit voltage; V act is the activation voltage; V ohm is the ohmic voltage loss; P fc is the output power of the fuel cell; i fc is the fuel cell current; the calculation method is as follows: V ohm =r ohm ·i fc #(4) V fc =Er ohm ·i fc #(5) E=K C ·E n #(6) Where: A is the Tafel slope; i0 is the exchange current; s is the scaling factor; T d is the response time; r ohm is the ohmic internal resistance of the fuel cell; E is the electromotive force of the fuel cell; K c is the voltage constant under rated working conditions; E n is the standard electromotive force; z is the number of moving electrons; F is the Faraday constant; k is the reaction rate constant; P H2 is the partial pressure of hydrogen; P O2 is the partial pressure of oxygen; R is the ideal gas constant; h is Planck's constant; is an exponential term, which indicates the effect of free energy change on the reaction rate; ΔG is the Gibbs free energy change, which indicates the free energy change in the reaction; T is the working temperature; α is the charge transfer coefficient; Among them, the standard electromotive force E of the fuel cell n The calculation method is as follows: The relationship between gas pressure and the conversion rate of substances involved in the reaction: P H2 =91-0.1Uf H2 )x%P fuel #(10) P O2 =(1-0.1Uf O2 )y%P air #(11) Where Uf H2 With Uf O2 are the conversion rates of hydrogen and oxygen respectively; P fuel and P air are the supply pressures of hydrogen and oxygen respectively; x and y are the proportions of hydrogen and oxygen in the fuel and air respectively; Reactant conversion rate: Where: V fuel is the fuel flow rate; V air is the air flow rate.

8. The multi-energy management method according to claim 1, characterized in that: The method further includes: Modeling photovoltaic power generation, the output power P of photovoltaic power generation pv : P pv =η·S·G#(14) In the formula, η is the overall efficiency of photovoltaic power generation; S is the area of ​​the solar panel; G is the intensity of sunlight; the=the * ·or MPPT ·[1-(β·T c -T ref )]#(16) Where: T c is the actual temperature of the solar panel; T a is the absolute temperature; T * is the normal operating temperature of the solar panel; β is the temperature coefficient; η * is the reference efficiency of the solar panel; T ref is the reference temperature of the solar panel; η MPPT The efficiency of maximum power point tracking for photovoltaic power generation; The log-normal distribution method is used to randomly generate hourly light intensity data F(x,u,σ). The expression of the log-normal distribution is as follows: Where u and σ are the standard deviation and mean value of solar radiation intensity data respectively; x is the solar radiation intensity.

9. The multi-energy management method according to claim 1, characterized in that: The method further includes: Model the battery pack, the power P of the battery cell LIB With energy E LIB Calculated by formula (18) and formula (19): P LIB =I LIB ·IN LIB #(18) E LIB =C LIB ·U LIB #(19) In the formula, I LIB , U LIB and C LIB 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: In the formula, SOC LIB (t) is the state of charge of the battery cell at time t; SOC LIB,pk (t) is the state of charge of the battery pack at time t; SOC LIB0 is the initial charge state of the battery cell; ± represents the battery being charged or discharged; η LIB is the battery monomer charge and discharge efficiency; η inv 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 by formula (22): Where N LIB,s With N LIB,p are the number of batteries connected in series and parallel; C LIB,pk is the rated capacity of the battery pack; U LIB,pk is the nominal voltage of the battery pack; I LIB,pk P is the battery pack charge and discharge current; LIB,pk is the battery pack power; E LIB,pk 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 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 the hydrogen generated by offshore wind farms and the carbon dioxide in offshore carbon sequestration to synthesize methanol, realizing 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 generate electricity using hydrogen 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 for system voltage stabilization and stores the electrical energy in the battery pack; The battery pack is connected to the power grid to transmit electric energy to the power grid; the power grid is connected to the load.

Images