CN111709850B - New energy ship power system capacity optimization method considering ship roll - Google Patents
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Abstract
The invention discloses a capacity optimization method of a new energy ship power system considering ship rolling, which comprises the following steps: 1) Building a photovoltaic system output model, a diesel engine power generation cost model, an energy storage system charge-discharge model and a ship load model; 2) Correcting the photovoltaic system output model in the step 1) according to the influence of ship rolling on the photovoltaic system output; 3) Establishing a multi-target multi-constraint optimization target model of a ship power system; 4) Optimizing a traditional particle swarm algorithm, and adding the particles into the genetic algorithm for pairwise hybridization; 5) Fusing the models established and corrected in the steps 1), 2) and 3) by using the algorithm after the optimization in the step 4), and outputting optimal particles after the operation by using mathematical simulation software, wherein the optimal result of the optimal particles is the optimal capacity optimization configuration. The method is optimized in the rolling state when the ship sails, and can reduce cost and loss under the condition of ensuring the sailing safety of the ship, thereby achieving the purposes of safety and energy conservation.
Description
Technical Field
The invention belongs to the technical field of new energy ships, relates to a capacity optimization strategy of a new energy ship mixed power system, and particularly relates to an optimization strategy under the condition of rolling when a ship sails.
Background
With the increasing severity of marine environmental problems, the consumption of traditional ship power stations is continuously increased along with the rise of ship operation requirements, and the research on optimizing the ship power system based on new energy sources becomes one of the main trends of the future ship development. At present, many researches on capacity optimization of the micro-grid are carried out by a plurality of domestic and foreign scholars, and a great deal of theoretical basis is provided for development of the ship micro-grid. However, most of optimization of single-target single constraint is considered, related optimization conditions are sacrificed while target maximization is obtained, a single algorithm is easy to fall into local optimization, and objective reasons such as ship swinging and the like of a ship in navigation are not considered. Therefore, the invention provides a capacity optimization strategy for the new energy ship hybrid power system.
Disclosure of Invention
The invention aims to overcome the defects of inaccurate optimization and unobvious algorithm optimization effect of the existing micro-grid capacity optimization strategy, and provides a capacity optimization method of a new energy ship power system considering ship rolling.
The invention uses the light/firewood/storage basic model as the basis to control the light receiving area A of the solar cell panel under the condition of ship rolling pv The effect of (2) is converted into an effect on the illumination intensity Eav, and the photovoltaic output model is optimized. In the aspect of algorithm, a particle swarm algorithm is optimized by adopting a crossing factor in a genetic algorithm, and a rolling period is set as an optimization period of the algorithm, so that the optimal energy storage capacity configuration of the ship is obtained. The method optimizes the ship in a rolling state during navigation, so that the safety of the ship navigation is ensured, the running cost of a ship power system is effectively reduced, and the aims of safety and energy conservation are fulfilled while the optimization is more accurate.
Compared with an optimization method without considering the rolling state, the running cost of the ship electric power system is reduced by 8.77%; compared with the differential algorithm and the traditional particle swarm algorithm, the running cost is reduced by 10.52 percent and 1.99 percent.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a capacity optimization method of a new energy ship power system considering ship rolling comprises the following steps:
(1) According to the light receiving area A of the solar panel PV Conversion efficiency eta PVmax And solar radiance G (t) builds a photovoltaic system output model; according to the output power P of the diesel generator d Establishing a diesel engine power generation cost model by using the fuel consumption coefficient; according to the power delta P and efficiency eta of the charge and discharge of the storage battery c Establishing a charge and discharge model of the energy storage system; load models of the ship under different working conditions are built according to ship navigation data;
(2) Correcting the photovoltaic system output model in the step (1) according to the influence of ship rolling on the photovoltaic system output during ship navigation;
(3) Taking the total cost of the ship power system and the system reliability as optimization conditions, and establishing a multi-objective and multi-constraint optimization configuration model of the ship power system;
(4) Optimizing a traditional particle swarm algorithm, and adding a particle pairwise crossing process in the genetic algorithm into the traditional particle swarm algorithm;
(5) Fusing the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model established in the step (1), the photovoltaic system output model corrected in the step (2) and the optimal configuration model established in the step (3) in the optimized algorithm in the step (4), carrying out simulation by using mathematical simulation software, and outputting optimal particles, wherein the number of solar panels, the capacity of a diesel generator and the number of storage batteries output by the particles are the optimal capacity configuration of a ship power system.
Further preferably, the specific contents and methods for building the four models in the step (1) are as follows:
1) The specific process for building the photovoltaic system output model is as follows: according to the output power P of the photovoltaic power generation system PV (t) solar radiation G (t) received at t moment, photovoltaic array area A PV Maximum conversion efficiency eta of solar cell panel PVmax Conversion efficiency eta of solar panel maximum power tracking control MPPT Temperature and real-time temperature T of solar panel during normal operation C-normal And T C Factor influence, constructing a shipborne photovoltaic power generation system model;
2) The specific process for establishing the diesel engine power generation cost model is as follows: approximating a cost model of a diesel generator to its output power P d Is expressed as a quadratic function ofWherein a, b and c are fuel coefficients;
3) The specific process for establishing the charge and discharge model of the energy storage system comprises the following steps: the electricity storage quantity of the energy storage system at the time t+1 is expressed as electricity storage quantity of the energy storage system at the time t and electricity storage variation quantity at the time t, and the variation quantity is mainly influenced by the generated energy of the diesel engine and the photovoltaic power generation system at the time t and the total load required by the system;
4) The specific process for establishing the ship load model is as follows: and judging different working conditions of the ship during sailing according to the sailing data of the ship, and establishing a load change model of the ship during sailing under the different working conditions.
Further preferably, the method for correcting in the step (2) comprises the following specific contents and processes:
firstly, setting a rolling period and a frequency under extreme conditions by taking the extreme conditions during ship navigation as an analysis basis; secondly, the light receiving area A of the solar cell panel pv The change of the intensity is equivalent to the change of the intensity of illumination Eav; then the illumination intensity is maximized Eav max The illumination intensity is set to be when the included angle between the solar panel and the horizontal plane is 0 DEG, so that the minimum value of the illumination intensity is Eav max (1-cos θ); finally, the mathematical relation between the actual illumination intensity and the cosine value cos theta of the roll angle and the roll period f is deduced,the corrected illumination intensity mathematical model is converted to obtain an accurate solar radiation degree model;
further preferably, the specific process of establishing the multi-objective multi-constraint optimal configuration model of the ship electric power system in the step (3) is as follows:
1) First, the equivalent annual investment cost C of the system is calculated ACS The expense is the sum of equipment installation cost expense, maintenance cost expense, replacement cost expense, fuel cost expense and environmental protection conversion expense of each power supply; system reliability cost C for recalculation system rel The penalty function of the system load power shortage rate LPSP and the system capacity excess rate EXC is expressed as C rel ＝γ[max(0,LPSP-LPSP max )]+δ[max(0,EXC-EXC max )]Gamma and delta are penalty factors; finally, calculating equivalent annual investment cost C of system ACS And system reliability cost C of the system rel Is the objective function of the optimal configuration model.
2) According to the area of the ship and navigation parameters, the micro power source output power P of the new energy ship is respectively output i Storage battery capacity SOC t Micro-power supply installation machineNumber N i And setting constraint conditions.
Further preferably, in the step (4), the specific process of adding the particle pairwise intersection process in the genetic algorithm to the conventional particle swarm algorithm is as follows:
firstly, after the optimal value of the individual fitness and the optimal value of the global fitness are found by the traditional particle swarm algorithm, dividing the global particles into two parts; then the former part of particles replace the speed and the position of the latter part of particles, and the latter part of particles are crossed two by two to generate new child particles; and finally, comparing the fitness values of the front and rear particles, and reserving particles with high fitness values, namely the generated new child particles.
Further optimizing, the specific process of fusing the five models to be built and corrected in the step (5) in the optimized algorithm is as follows:
1) Initializing particle speed and position according to the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model established in the step (1) and the photovoltaic system output model corrected in the step (2);
2) Calculating the individual fitness value of each particle according to the objective function in the optimal configuration model established in the step (3);
3) Searching an individual fitness optimal value and a global fitness optimal value of the particles;
4) Judging whether the individual fitness optimal value of each particle is larger than the global fitness optimal value, if so, updating the global fitness optimal value into the current individual fitness optimal value of the particle, otherwise, returning to the step 3), and continuously searching the individual particles and the global fitness optimal value;
5) Optimizing the traditional particle swarm algorithm according to the step (4) to generate new daughter particles;
6) Judging whether the new child particles meet the requirement of constraint conditions in the optimal configuration model established in the step (3), if so, outputting an optimal result, otherwise, returning to the step (2), and continuously calculating the fitness value of each particle.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) According to the invention, under the actual condition of considering ship rolling, the light receiving area A of the solar cell panel pv The effect of (2) is converted into an effect on the illumination intensity Eav, and the photovoltaic output model is optimized. By taking a certain ocean vessel as an example, compared with an optimization method without considering the rolling state, the running cost of the ship power system is reduced by 8.77 percent. The optimization result is more accurate, and the light energy is utilized to a greater extent.
(2) The invention establishes the multi-target multi-constraint optimal configuration model, and simultaneously considers the safety and cost of the ship power station, namely, the cost is reduced to the greatest extent under the condition of ensuring the ship navigation safety. And (3) taking special placement conditions on the ship into consideration, and establishing a constraint model for the micro-power output power, the storage battery capacity and the installed quantity of the micro-power supplies of the new energy ship, so that the optimization result is more accurate.
(3) After the particle swarm algorithm is added into the crossing process, compared with the differential and particle swarm algorithm, the local optimizing condition is improved, and the cost of the ship power station system is reduced by 10.52 percent and 1.99 percent respectively.
Drawings
FIG. 1a is a flow chart of the method of the present invention
Figure 1b is a flow chart for output correction of a photovoltaic system,
figure 1c is a flow chart of an optimization conventional particle swarm algorithm,
figure 2 is a schematic diagram of a ship's hybrid power system,
figure 3a is a graph of the illumination intensity data per hour for an ocean vessel from chinese to the gulf of also door in an embodiment of the invention,
figure 3b is a graph of ambient temperature data per hour over a voyage,
figure 3c is a graph of the load change during voyage of a ship within a voyage,
figure 4 is a flow chart of the calculation of the load loss rate,
fig. 5a is a graph comparing the results of optimizing using the same genetic particle swarm algorithm for two different examples, i.e. whether the vessel roll is considered,
fig. 5b is a graph comparing the results of optimization in consideration of the roll of the ship using a differential evolution algorithm, a particle swarm algorithm, and a genetic particle swarm optimization algorithm, respectively.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention uses the photovoltaic system output, the diesel engine power generation cost and the energy storage system charge-discharge model as the basis, and the solar panel light receiving area A is controlled under the condition of ship rolling pv The effect of (2) is converted into an effect on the illumination intensity Eav, and the photovoltaic output model is optimized. In the aspect of algorithm, a particle swarm algorithm is optimized by adopting a crossing factor in a genetic algorithm, and a rolling period is set as an optimization period of the algorithm, so that the optimal energy storage capacity configuration of the ship is obtained. The method not only ensures the safety of ship navigation, but also effectively reduces the running cost of the ship power system, thereby achieving the purposes of safety and energy conservation.
As shown in fig. 1a, the capacity optimization method of the new energy ship power system considering ship rolling of the invention comprises the following steps:
(1) According to the light receiving area A of the solar panel PV Conversion efficiency eta PVmax And solar radiance G (t) builds a photovoltaic system output model; according to the output power P of the diesel generator d Establishing a diesel engine power generation cost model by using the fuel consumption coefficient; according to the power delta P and efficiency eta of the charge and discharge of the storage battery c Establishing a charge and discharge model of the energy storage system; load models of the ship under different working conditions are built according to ship navigation data;
(2) Correcting the photovoltaic system output model in the step (1) according to the influence of ship rolling on the photovoltaic system output during ship navigation;
(3) Taking the total cost of the ship power system and the system reliability as optimization conditions, and establishing a multi-objective and multi-constraint optimization configuration model of the ship power system;
(4) Optimizing a traditional particle swarm algorithm, and adding a particle pairwise crossing process in the genetic algorithm into the traditional particle swarm algorithm;
(5) Fusing the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model established in the step (1), the photovoltaic system output model corrected in the step (2) and the optimal configuration model established in the step (3) in the optimized algorithm in the step (4), carrying out simulation by using mathematical simulation software, and outputting optimal particles, wherein the number of solar panels, the capacity of a diesel generator and the number of storage batteries output by the particles are the optimal capacity configuration of a ship power system.
As shown in fig. 2, a schematic diagram of a ship hybrid power system is provided. The specific contents and methods for establishing the four models in the step (1) are as follows:
1) The specific process for building the photovoltaic system output model is as follows: photovoltaic power generation system output power P PV (t) solar radiation G (t) received at t moment, photovoltaic array area A PV Maximum conversion efficiency eta of solar cell panel PVmax Conversion efficiency eta of solar panel maximum power tracking control MPPT Temperature and real-time temperature T of solar panel during normal operation C-normal And T C And the like. The built shipborne photovoltaic power generation system model is as follows: p (P) PV (t)＝A PV G(t)η PVmax η MPPT [1-α(T C -T C-normal )]. Taking the data actually measured in 40 days from 1 month 1 day to 2 months 9 days in the course route region 2020 as the required environmental parameters in the expression, and taking a data diagram of the illumination intensity in 40 days as shown in FIG. 3 a; as shown in fig. 3b, a graph of air temperature data over 40 days is shown.
2) The specific process for establishing the diesel engine power generation cost model is as follows: approximating a cost model of a diesel generator to its output power P d Is expressed as a quadratic function ofWherein a, b and c are fuel coefficients;
3) The specific process for establishing the charge and discharge model of the energy storage system comprises the following steps: the electricity storage quantity of the energy storage system at the time t+1 is expressed as electricity storage quantity of the energy storage system at the time t and electricity storage variation quantity at the time t, and the variation quantity is mainly influenced by the generated energy of the diesel engine and the photovoltaic power generation system at the time t and the total load required by the system;
4) The specific process for establishing the ship load model is as follows: taking a 10-thousand ton ocean going tanker from large national to the also-door-to-the-gulf as an example, the vessel has mainly 4 load conditions, namely berthing, loading and unloading cargo, cruising, and sailing at full speed. Wherein the load is the maximum at full speed sailing, which is 1790kW; the load at dock is the minimum, 490kW. The load variation of the vessel over a course is shown in figure 3 c.
The method for correcting in the step (2) is shown in fig. 1b, and the specific contents and the process are as follows:
firstly, setting a rolling period and a frequency under extreme conditions by taking the extreme conditions during ship navigation as an analysis basis; secondly, the light receiving area A of the solar cell panel pv The change of the intensity is equivalent to the change of the intensity of illumination Eav; then the illumination intensity is maximized Eav max The illumination intensity is set to be when the included angle between the solar panel and the horizontal plane is 0 DEG, so that the minimum value of the illumination intensity is Eav max (1-cos θ); finally, the mathematical relation between the actual illumination intensity and the cosine value cos theta of the roll angle and the roll period f is deduced,the corrected illumination intensity mathematical model is converted to obtain an accurate solar radiation degree model;
the specific process for establishing the multi-target multi-constraint optimal configuration model of the ship power system in the step (3) is as follows:
1) First, the equivalent annual investment cost C of the system is calculated ACS The expense is the sum of equipment installation cost expense, maintenance cost expense, replacement cost expense, fuel cost expense and environmental protection conversion expense of each power supply; system reliability cost C for recalculation system rel The penalty function of the system load power shortage rate LPSP and the system capacity excess rate EXC is expressed as C rel ＝γ[max(0，LPSP-LPSP max )]+δ[max(0，EXC-EXC max )]Sum of gamma anddelta is a penalty factor, and a calculation flow chart of the load power failure rate is shown in figure 4; finally, calculating equivalent annual investment cost C of system ACS And system reliability cost C of the system rel Is the objective function of the optimal configuration model.
2) According to the area of the ship and navigation parameters, the micro power source output power P of the new energy ship is respectively output i Storage battery capacity SOC t Number N of micro-power supply installation i And setting constraint conditions. The specific constraint conditions are as follows: p (P) i - min ≤P i ≤P i-max 、SOC min ≤SOC t ≤SOC max 、1≤N i ≤N i - max 。
In the step (4), the particle swarm optimization is added with the genetic algorithm, as shown in fig. 1c, wherein the specific process is as follows:
firstly, after the optimal value of the individual fitness and the optimal value of the global fitness are found by the traditional particle swarm algorithm, dividing the global particles into two parts; then the former part of particles replace the speed and the position of the latter part of particles, and the latter part of particles are crossed two by two to generate new child particles; and finally, comparing the fitness values of the front and rear particles, and reserving particles with high fitness values, namely the generated new child particles.
The specific process of fusing the five models to be built and corrected in the optimized algorithm in the step (5) is as follows:
1) Initializing particle speed and position according to the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model established in the step (1) and the photovoltaic system output model corrected in the step (2);
2) Calculating the individual fitness value of each particle according to the objective function in the optimal configuration model established in the step (3);
3) Searching an individual fitness optimal value and a global fitness optimal value of the particles;
4) Judging whether the individual fitness optimal value of each particle is larger than the global fitness optimal value, if so, updating the global fitness optimal value into the current individual fitness optimal value of the particle, otherwise, returning to the step 3), and continuously searching the individual particles and the global fitness optimal value;
5) Optimizing the traditional particle swarm algorithm according to the step (4) to generate new daughter particles;
6) Judging whether the new child particles meet the requirement of constraint conditions in the optimal configuration model established in the step (3), if so, outputting an optimal result, otherwise, returning to the step (2), and continuously calculating the fitness value of each particle.
For comparison, simulation optimization was performed in mathematical simulation software using different algorithms and examples. And comparing the final optimizing results by using the same genetic particle swarm algorithm for two different calculation examples, namely whether the ship rolling condition is considered or not. Unlike the optimization cycle of 1h in example 1, the on-board photovoltaic model was introduced to take into account the roll of the ship, and one roll cycle 20s was set as one optimization cycle to take into account the variation in solar irradiance per 20 s. One voyage 960h, which involves 172800 optimization cycles, two example fitness function optimization process pairs such as that shown in fig. 5 a. Meanwhile, in order to better verify the reliability of the algorithm, a differential evolution algorithm, a particle swarm algorithm and a genetic particle swarm optimization algorithm are respectively used for optimizing and simulating an objective function under the condition of considering the rolling of the ship. Setting the particle population scale as 100, the algorithm iteration number as 50 and the maximum inertia weight omega max =0.9, minimum inertial weight ω min =0.4. Setting crossover probability C in genetic particle swarm optimization algorithm r =0.8. The fitness function optimizing process of the three algorithms is shown in fig. 5 b.
As shown in table 1, since example 2 has 172800 optimization cycles in consideration of the ship rolling condition, the early convergence speed is slower than that of example 1, but the optimization results show that the running cost of the power station of example 2 is reduced by 8.77% compared with that of example 1, and the optimization effect is remarkable. In algorithm comparison, the earlier stage of the particle swarm algorithm is slightly faster than the genetic particle swarm algorithm in convergence speed, the optimized value is iterated for about 10 times to reach stability, but the optimized result obtained in the later stage is inferior to the genetic particle swarm, and the global optimal value cannot be found; the difference algorithm is inferior to the two algorithms in terms of convergence speed and optimization result. The running cost of the power station is respectively reduced by 10.52 percent and 1.99 percent by using the genetic particle swarm algorithm and the particle swarm algorithm.
Table 1: comparison table of calculation example optimization results
| Calculation example/algorithm | Example 1 | EXAMPLE 2 | DE | PSO | GAPSO |
| Number of solar panels/block | 2000 | 2000 | 2000 | 2000 | 2000 |
| Accumulator/battery | 133 | 176 | 128 | 155 | 176 |
| Diesel engine output total energy (kW h) | 1550310 | 1210091 | 1601072 | 1370541 | 1210091 |
| Total cost/ten thousand yuan/voyage of hybrid power station system | 606.4 | 551.8 | 616.7 | 563 | 551.8 |
The foregoing description is only of the preferred embodiments of the invention. Of course, the invention is capable of other various embodiments and its several details are capable of modification in various, equivalent arrangements and changes, all of which are within the purview of one skilled in the art and capable of modification in accordance with the invention without departing from the spirit and intended scope of the invention as defined in the appended claims.
Claims (4)
1. The capacity optimization method of the new energy ship power system considering ship rolling is characterized by comprising the following steps of:
(1) According to the light receiving area A of the solar panel PV Conversion efficiency eta PVmax And solar radiance G (t) builds a photovoltaic system output model; according to the output power P of the diesel generator d Establishing a diesel engine power generation cost model by using the fuel consumption coefficient; according to the power delta P and efficiency eta of the charge and discharge of the storage battery c Establishing a charge and discharge model of the energy storage system; load models of the ship under different working conditions are built according to ship navigation data;
(2) Correcting the photovoltaic system output model in the step (1) according to the influence of ship rolling on the photovoltaic system output during ship navigation;
(3) Taking the total cost of the ship power system and the system reliability as optimization conditions, and establishing a multi-objective and multi-constraint optimization configuration model of the ship power system;
(4) Optimizing a traditional particle swarm algorithm, adding a particle pairwise crossing process in a genetic algorithm into the traditional particle swarm algorithm, and specifically comprises the following steps:
firstly, after the optimal value of the individual fitness and the optimal value of the global fitness are found by the traditional particle swarm algorithm, dividing the global particles into two parts; then the former part of particles replace the speed and the position of the latter part of particles, and the latter part of particles are crossed two by two to generate new child particles; finally, comparing the fitness values of the front and rear particles, and reserving particles with high fitness values, namely the generated new child particles;
(5) Fusing the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model established in the step (1), the photovoltaic system output model corrected in the step (2) and the optimal configuration model established in the step (3) in the optimized algorithm in the step (4), carrying out simulation by using mathematical simulation software, and outputting optimal particles, wherein the number of solar panels, the capacity of a diesel generator and the number of storage batteries output by the particles are the optimal capacity configuration of a ship power system, and the specific fusion process is as follows:
1) Initializing particle speed and position according to the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model established in the step (1) and the photovoltaic system output model corrected in the step (2);
2) Calculating the individual fitness value of each particle according to the objective function in the optimal configuration model established in the step (3);
3) Searching an individual fitness optimal value and a global fitness optimal value of the particles;
4) Judging whether the individual fitness optimal value of each particle is larger than the global fitness optimal value, if so, updating the global fitness optimal value into the current individual fitness optimal value of the particle, otherwise, returning to the step 3), and continuously searching the individual particles and the global fitness optimal value;
5) Optimizing the traditional particle swarm algorithm according to the step (4) to generate new daughter particles;
6) Judging whether the new child particles meet the requirement of constraint conditions in the optimal configuration model established in the step (3), outputting an optimal result when the new child particles meet the requirement, otherwise returning to the step (2), and continuously calculating the fitness value of each particle.
2. The capacity optimization method of the new energy ship power system considering ship rolling as claimed in claim 1, wherein the specific contents and methods for establishing four models in the step (1) are as follows:
1) The specific process for building the photovoltaic system output model is as follows: according to the output power P of the photovoltaic power generation system PV (t) solar radiation G (t) received at t moment, photovoltaic array area A PV Maximum conversion efficiency eta of solar cell panel PVmax Conversion efficiency eta of solar panel maximum power tracking control MPPT Temperature and real-time temperature T of solar panel during normal operation C-normal And T C Factor influence, constructing a shipborne photovoltaic power generation system model;
2) The specific process for establishing the diesel engine power generation cost model is as follows: approximating a cost model of a diesel generator to its output power P d Is expressed as aP d 2 +bP d +c, wherein a, b, c are fuel factors;
3) The specific process for establishing the charge and discharge model of the energy storage system comprises the following steps: the electricity storage quantity of the energy storage system at the time t+1 is expressed as electricity storage quantity of the energy storage system at the time t and electricity storage variation quantity at the time t, and the variation quantity is mainly influenced by the generated energy of the diesel engine and the photovoltaic power generation system at the time t and the total load required by the system;
4) The specific process for establishing the ship load model is as follows: and judging different working conditions of the ship during sailing according to the sailing data of the ship, and establishing a load change model of the ship during sailing under the different working conditions.
3. The method for optimizing the capacity of the new energy ship power system taking ship roll into consideration as set forth in claim 1, wherein the method for correcting in the step (2) comprises the following specific contents and processes:
firstly, setting a rolling period and a frequency under extreme conditions by taking the extreme conditions during ship navigation as an analysis basis; secondly, the light receiving area A of the solar cell panel pv The change of the intensity is equivalent to the change of the intensity of illumination Eav; then the illumination intensity is maximized Eav max The illumination intensity is set to be when the included angle between the solar panel and the horizontal plane is 0 DEG, so that the minimum value of the illumination intensity is Eav max (1-cos θ); finally, the mathematical relation between the actual illumination intensity and the cosine value cos theta of the roll angle and the roll period f is deduced,the corrected illumination intensity mathematical model is converted to obtain an accurate solar radiation degree mathematical model.
4. The method for optimizing the capacity of the new energy ship power system taking ship rolling into consideration according to claim 1, wherein the specific process of establishing the multi-objective multi-constraint optimizing configuration model of the ship power system in the step (3) is as follows:
1) First, the equivalent annual investment cost C of the system is calculated ACS The expense is the sum of equipment installation cost expense, maintenance cost expense, replacement cost expense, fuel cost expense and environmental protection conversion expense of each power supply; system reliability cost C for recalculation system rel The penalty function of the system load power shortage rate LPSP and the system capacity excess rate EXC is expressed as C rel ＝γ[max(0，LPSP-LPSP max )]+δ[max(0，EXC-EXC max )]Gamma and delta are penalty factors; finally, calculating equivalent annual investment cost C of system ACS And system reliability cost C of the system rel Is the target function of the optimal configuration model;
2) According to the area of the ship and navigation parameters, the micro power source output power P of the new energy ship is respectively output i Storage battery capacity SOC t Number N of micro-power supply installation i And setting constraint conditions.
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