CN111709850A - New energy ship power system capacity optimization method considering ship rolling - Google Patents
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Abstract
The invention discloses a new energy ship power system capacity optimization method considering ship rolling, which comprises the following steps: 1) establishing 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 the traditional particle swarm algorithm, and adding the particle pairwise hybridization process in the genetic algorithm; 5) fusing the models established and corrected in the steps 1), 2) and 3) by using the optimized algorithm in the step 4), and outputting the optimal particles after the operation of mathematical simulation software, wherein the optimized result of the optimal particles is the optimal capacity optimization configuration. The method of the invention is optimized in a rolling state when the ship navigates, and can reduce the cost and the loss under the condition of ensuring the navigation safety of the ship, thereby achieving the purposes of safety and energy saving.
Description
Technical Field
The invention belongs to the technical field of new energy ships, relates to a capacity optimization strategy of a hybrid power system of a new energy ship, and particularly relates to an optimization strategy under the condition of rolling of a ship during navigation.
Background
With the increasing severity of marine environmental problems, the consumption of traditional ship power stations is increasing with the rise of ship operation requirements, and the optimization research of a ship power system based on new energy sources becomes one of the main trends of future ship development. At present, many domestic and foreign scholars have studied the capacity optimization of the micro-grid, and a great deal of theoretical basis is provided for the development of the ship micro-grid. However, optimization of single target and single constraint is mostly considered, related optimization conditions are sacrificed while the target maximization is obtained, a single algorithm is easy to fall into local optimization, and objective reasons such as ship swinging and the like facing a ship in navigation are not considered. Therefore, the invention provides a capacity optimization strategy for the hybrid power system of the new energy ship.
Disclosure of Invention
The invention aims to overcome the defects of inaccurate optimization and unobvious algorithm optimization effect of the existing microgrid capacity optimization strategy, and provides a new energy ship power system capacity optimization method considering ship rolling.
The invention takes the light/firewood/storage basic model as the basis to make the light receiving area A of the solar cell panel under the condition of ship rollingpvThe influence of (a) is converted into the influence of 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 cross factor in a genetic algorithm, and a rolling period is set as an optimization period of the algorithm to obtain the optimal energy storage capacity configuration of the ship. The method is optimized in a rolling state when the ship navigates, so that the navigation safety of the ship is ensured, the operation cost of a ship power system is effectively reduced, and the purposes of safety and energy conservation are achieved while the optimization is more accurate.
Compared with an optimization method without considering a rolling state, the operation cost of the ship power system is reduced by 8.77%; compared with a difference algorithm and a traditional particle swarm algorithm, the running cost is reduced by 10.52% and 1.99%.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a new energy ship power system capacity optimization method considering ship rolling comprises the following steps:
(1) according to the light receiving area A of the solar panelPVη conversion efficiencyPVmaxAnd establishing a photovoltaic system output model according to the solar radiation degree G (t); according to the output power P of the diesel generatordEstablishing a diesel engine power generation cost model according to the fuel consumption coefficient, and η according to the charging and discharging power delta P and efficiency of the storage batterycEstablishing a charge-discharge model of the energy storage system; establishing load models of the ship under different working conditions according to the 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 when the ship is sailing;
(3) taking the total cost of the ship power system and the system reliability as optimization conditions, and establishing a multi-target multi-constraint optimization configuration model of the ship power system;
(4) optimizing a traditional particle swarm algorithm, and adding a pairwise crossing process of particles in a genetic algorithm into the traditional particle swarm algorithm;
(5) and (3) fusing the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model which are established in the step (1), the photovoltaic system output model corrected in the step (2) and the optimized configuration model established in the step (3) in the optimized algorithm in the step (4), simulating by using mathematical simulation software, and outputting optimal particles, wherein the number of solar panels, the diesel generator capacity and the number of storage batteries which are output by the particles are the optimal capacity configuration of the ship power system.
Further preferably, the specific contents and methods for establishing the four models in the step (1) are as follows:
1) the specific process for establishing the photovoltaic system output model comprises the following steps: according to the output power P of the photovoltaic power generation systemPV(t) degree of solar irradiance G (t) received at time t, area A of photovoltaic arrayPVMaximum conversion efficiency η of solar panelPVmaxConversion efficiency η for maximum power tracking control of solar panelMPPTTemperature and real-time temperature T during normal operation of solar panelC-normalAnd TCBuilding a shipborne photovoltaic power generation system model under the influence of factors;
2) the specific process for establishing the diesel engine power generation cost model comprises the following steps: approximating a cost model of a diesel generator to its output power PdIs expressed as
Wherein a, b and c are fuel coefficients;
3) the specific process for establishing the energy storage system charge-discharge model comprises the following steps: the method comprises the steps that the storage capacity of an energy storage system at the time t +1 is represented as the storage capacity of the energy storage system at the time t and the storage variation at the time t, and the variation is mainly influenced by the power generation capacity of a diesel engine and a 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 comprises the following steps: and judging different working conditions of the ship during navigation according to the navigation data of the ship, and establishing a load change model of the ship under different working conditions during navigation.
Further preferably, the specific content and process of the correction method in the step (2) are as follows:
firstly, taking the extreme condition of the ship during navigation as an analysis basis, and setting the rolling period and frequency under the extreme condition; secondly, the light receiving area A of the solar cell panelpvThe variation of the size is equivalent to the variation of the size of the illumination intensity Eav; then the maximum value Eav of the illumination intensity is calculatedmaxSetting the illumination intensity when the included angle between the solar cell panel and the horizontal plane is 0 degree, thereby obtaining the minimum value Eav of the illumination intensitymax(1-cos θ); finally, a mathematical relation between the actual illumination intensity and the cosine value cos theta of the roll angle and the roll period f is deduced,
i.e. after correctionThe illumination intensity mathematical model can obtain an accurate solar radiation degree mathematical model after conversion;
further preferably, the specific process of establishing the multi-target multi-constraint optimal configuration model of the ship power system in the step (3) is as follows:
1) firstly, calculating the equivalent annual investment cost C of the systemACSThe cost is the sum of equipment installation cost, maintenance cost, replacement cost, fuel cost and environmental protection conversion cost of each power supply; recalculating System reliability costs C for the SystemrelA penalty function of the system load power shortage LPSP and the system capacity excess multiplying power EXC is represented by the formula Crel＝γ[max(0,LPSP-LPSPmax)]+[max(0,EXC-EXCmax)]γ and a penalty factor; finally calculating the equivalent annual investment cost C of the systemACSAnd system reliability cost C of the systemrelIs the objective function of the optimal configuration model.
2) Respectively outputting power P to the micro power supply of the new energy ship according to the area size and the navigation parameters of the shipiAnd battery capacity SOCtThe installed number N of micro power suppliesiA constraint is set.
Further preferably, the specific process of adding the pairwise crossing process of the particles in the genetic algorithm into the traditional particle swarm optimization in the step (4) is as follows:
firstly, after an individual fitness optimal value and a global fitness optimal value are found by a traditional particle swarm algorithm, global particles are divided into two parts; then, the speed and position of the former part of particles are used for replacing the latter part of particles, and the latter part of particles are crossed pairwise to generate new filial generation particles; finally, the fitness values of the front part and the rear part of the particles are compared, and the particles with high fitness values are reserved, namely the generated new progeny particles.
Further optimization, the concrete process of fusing the five established and corrected models in the optimized algorithm in the step (5) is as follows:
1) initializing the particle speed and the particle position according to the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model which are established in the step (1) and the photovoltaic system output model which is corrected in the step (2);
2) calculating the individual fitness value of each particle according to the objective function in the optimized 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 to the current individual fitness optimal value of the particle, otherwise, returning to the step 3), and continuously searching the particle individual and the global fitness optimal value;
5) optimizing the traditional particle swarm algorithm according to the step (4) to generate new progeny particles;
6) and (4) judging whether the new child particles meet the requirements of the constraint conditions in the optimized configuration model established in the step (3), if so, outputting an optimized 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) the invention takes the actual situation of ship rolling into consideration, and the light receiving area A of the solar cell panel ispvThe influence of (a) is converted into the influence of 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 a rolling state, the operation cost of a ship power system is reduced by 8.77%. The optimization result is more accurate, and the light energy is utilized to a greater extent.
(2) The method establishes the multi-target multi-constraint optimal configuration model, and considers the safety and the cost of the ship power station at the same time, namely, the cost is reduced to the maximum extent under the condition of ensuring the navigation safety of the ship. And (3) considering special placing conditions on the ship, and establishing a constraint model for the micro-power output power, the storage battery capacity and the micro-power installed quantity of the new energy ship, so that the optimization result is more accurate.
(3) After the particle swarm algorithm is added into the cross process, compared with the difference and particle swarm algorithm, the local optimization condition is improved, and the cost of the ship power station system is respectively reduced by 10.52 percent and 1.99 percent.
Drawings
FIG. 1a is a flow chart of the method of the present invention
Figure 1b is a flow chart for correcting the output of a photovoltaic system,
figure 1c is a flow chart of a conventional particle swarm optimization algorithm,
figure 2 is a schematic diagram of the hybrid power system of the ship,
figure 3a is a graph of hourly light intensity data for an ocean vessel from china big to also santa gulf in an embodiment of the present invention,
figure 3b is a graph of hourly ambient temperature data for a flight,
figure 3c is a graph of the load change during a voyage of the vessel over a voyage,
figure 4 is a flow chart of the calculation of the load power shortage,
FIG. 5a is a comparison of the results of the same genetic particle swarm algorithm for two different examples, i.e., whether ship roll is considered,
FIG. 5b is a comparison graph of the optimization results with a differential evolution algorithm, a particle swarm algorithm, and a genetic particle swarm optimization algorithm, respectively, under the condition of considering the ship rolling.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention takes the photovoltaic system output, the diesel engine power generation cost and the energy storage system charge-discharge model as the basis to ensure that the light receiving area A of the solar cell panel is increased under the condition of ship rollingpvThe influence of (a) is converted into the influence of 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 cross factor in a genetic algorithm, and a rolling period is set as an optimization period of the algorithm to obtain the optimal energy storage capacity configuration of the ship. The method not only ensures the safety of the ship navigation, but also effectively reducesThe operation cost of the ship power system is reduced, and the purposes of safety and energy conservation are achieved.
As shown in fig. 1a, the method for optimizing the capacity of the new energy ship power system in consideration of ship rolling comprises the following steps:
(1) according to the light receiving area A of the solar panelPVη conversion efficiencyPVmaxAnd establishing a photovoltaic system output model according to the solar radiation degree G (t); according to the output power P of the diesel generatordEstablishing a diesel engine power generation cost model according to the fuel consumption coefficient, and η according to the charging and discharging power delta P and efficiency of the storage batterycEstablishing a charge-discharge model of the energy storage system; establishing load models of the ship under different working conditions according to the 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 when the ship is sailing;
(3) taking the total cost of the ship power system and the system reliability as optimization conditions, and establishing a multi-target multi-constraint optimization configuration model of the ship power system;
(4) optimizing a traditional particle swarm algorithm, and adding a pairwise crossing process of particles in a genetic algorithm into the traditional particle swarm algorithm;
(5) and (3) fusing the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model which are established in the step (1), the photovoltaic system output model corrected in the step (2) and the optimized configuration model established in the step (3) in the optimized algorithm in the step (4), simulating by using mathematical simulation software, and outputting optimal particles, wherein the number of solar panels, the diesel generator capacity and the number of storage batteries which are output by the particles are the optimal capacity configuration of the ship power system.
Fig. 2 is a schematic diagram of a hybrid power system of a ship. The concrete contents and methods for establishing the four models in the step (1) are as follows:
1) the specific process for establishing the photovoltaic system output model comprises the following steps: output power P of photovoltaic power generation systemPV(t) degree of solar irradiance G (t) received at time t, area A of photovoltaic arrayPVMaximum conversion efficiency η of solar panelPVmaxConversion efficiency η for maximum power tracking control of solar panelMPPTTemperature and real-time temperature T during normal operation of solar panelC-normalAnd TCAnd the like. The built shipborne photovoltaic power generation system model is as follows: pPV(t)＝APVG(t)ηPVmaxηMPPT[1-α(TC-TC-normal)]. Taking the actually measured data of 40 days from 1 month to 9 months from 1 month to 2 months from 2020 in the voyage approach area as the required environmental parameters in the expression, as shown in fig. 3a, the data is a data graph of the illumination intensity within 40 days; FIG. 3b is a graph of the gas temperature data for 40 days.
2) The specific process for establishing the diesel engine power generation cost model comprises the following steps: approximating a cost model of a diesel generator to its output power PdIs expressed as
Wherein a, b and c are fuel coefficients;
3) the specific process for establishing the energy storage system charge-discharge model comprises the following steps: the method comprises the steps that the storage capacity of an energy storage system at the time t +1 is represented as the storage capacity of the energy storage system at the time t and the storage variation at the time t, and the variation is mainly influenced by the power generation capacity of a diesel engine and a 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 comprises the following steps: in the case of a 10 ten thousand ton ocean going tanker from china to the so-called santa bay, the vessel has mainly 4 load cases, docking, loading and unloading, cruising, full speed. Wherein the load is the largest when the vehicle is sailing at full speed, and is 1790 kW; the load is the minimum at dock parking, 490 kW. The load change of the vessel during one voyage is shown in figure 3 c.
As shown in fig. 1b, the specific content and process of the correction method in step (2) are as follows:
firstly, taking the extreme condition of the ship during navigation as an analysis basis, and setting the rolling period and frequency under the extreme condition; secondly, the light receiving area A of the solar cell panelpvThe variation of the size is equivalent to the variation of the size of the illumination intensity Eav; then the maximum value Eav of the illumination intensity is calculatedmaxSetting the illumination intensity when the included angle between the solar cell panel and the horizontal plane is 0 degree, thereby obtaining the minimum value Eav of the illumination intensitymax(1-cos θ); finally, a 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;
the specific process of establishing the multi-target multi-constraint optimal configuration model of the ship power system in the step (3) is as follows:
1) firstly, calculating the equivalent annual investment cost C of the systemACSThe cost is the sum of equipment installation cost, maintenance cost, replacement cost, fuel cost and environmental protection conversion cost of each power supply; recalculating System reliability costs C for the SystemrelA penalty function of the system load power shortage LPSP and the system capacity excess multiplying power EXC is represented by the formula Crel＝γ[max(0，LPSP-LPSPmax)]+[max(0，EXC-EXCmax)]The gamma sum is a penalty factor, and a flow chart for calculating the load power shortage rate is shown in FIG. 4; finally calculating the equivalent annual investment cost C of the systemACSAnd system reliability cost C of the systemrelIs the objective function of the optimal configuration model.
2) Respectively outputting power P to the micro power supply of the new energy ship according to the area size and the navigation parameters of the shipiAnd battery capacity SOCtThe installed number N of micro power suppliesiA constraint is set. The specific constraint conditions are as follows: pi-min≤Pi≤Pi-max、SOCmin≤SOCt≤SOCmax、1≤Ni≤Ni-max。
The step (4) of adding the pairwise crossing process of the particles in the genetic algorithm into the traditional particle swarm algorithm is shown in figure 1c, and the specific process is as follows:
firstly, after an individual fitness optimal value and a global fitness optimal value are found by a traditional particle swarm algorithm, global particles are divided into two parts; then, the speed and position of the former part of particles are used for replacing the latter part of particles, and the latter part of particles are crossed pairwise to generate new filial generation particles; finally, the fitness values of the front part and the rear part of the particles are compared, and the particles with high fitness values are reserved, namely the generated new progeny particles.
The concrete process of fusing the five established and corrected models in the optimized algorithm in the step (5) is as follows:
1) initializing the particle speed and the particle position according to the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model which are established in the step (1) and the photovoltaic system output model which is corrected in the step (2);
2) calculating the individual fitness value of each particle according to the objective function in the optimized 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 to the current individual fitness optimal value of the particle, otherwise, returning to the step 3), and continuously searching the particle individual and the global fitness optimal value;
5) optimizing the traditional particle swarm algorithm according to the step (4) to generate new progeny particles;
6) and (4) judging whether the new child particles meet the requirements of the constraint conditions in the optimized configuration model established in the step (3), if so, outputting an optimized result, otherwise, returning to the step (2), and continuously calculating the fitness value of each particle.
For comparison, simulation optimization was performed in the mathematical simulation software using different algorithms and examples. And comparing the final optimizing result by using the same genetic particle swarm algorithm according to two different calculation examples, namely whether the ship rolling condition is considered. Different from the optimization period of 1h in the embodiment 1, the optimization period of the embodiment 2 introduces an onboard photovoltaic model considering the rolling of the ship, and sets one rolling period 20s as one optimization period to consider the change of solar irradiance every 20 s. One voyage 960h, totally related to 172800 optimization cycles, two example fitness function optimization process pairs are 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 the target function under the condition of considering the rolling of the ship. Setting the particle population size to be 100, the iteration times of the algorithm to be 50 and the maximum inertia weight omega to be 50max0.9, minimum inertial weight ωmin0.4. Setting cross probability C in genetic particle swarm optimization algorithmr0.8. The fitness function optimization process for the three algorithms is shown in fig. 5 b.
The example optimization results are shown in table 1, and it can be seen from table 1 that since the example 2 considers the rolling condition of the ship and has 172800 optimization cycles, the early convergence speed is slower than that of the example 1, but the optimization results show that the operating cost of the power station of the example 2 is reduced by 8.77% compared with that of the example 1, and the optimization effect is significant. In comparison of the algorithms, the particle swarm algorithm is slightly faster than the genetic particle swarm algorithm in convergence speed in the early stage, the optimized numerical value is stable after 10 times of iteration, but the optimized result obtained in the later stage is not as good as the genetic particle swarm, and the global optimal value cannot be found; while the difference algorithm is inferior to the two algorithms in convergence speed and optimization results. The running cost of the power station is respectively reduced by 10.52 percent and 1.99 percent by using the genetic particle swarm specific differential algorithm and the particle swarm algorithm.
Table 1: comparative table of optimization results of examples
| Examples/algorithms | EXAMPLE 1 | EXAMPLE 2 | DE | PSO | GAPSO |
| Number/block of solar panels | 2000 | 2000 | 2000 | 2000 | 2000 |
| Storage battery/ | 133 | 176 | 128 | 155 | 176 |
| Diesel engine total output 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 is only a preferred embodiment of the present invention. The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it is therefore intended that all such equivalent changes and modifications as would be obvious to one skilled in the art be included herein are deemed to be within the scope and spirit of the present invention as defined by the appended claims.
Claims (6)
1. A new energy ship power system capacity optimization method considering ship rolling is characterized by comprising the following steps:
(1) according to the light receiving area A of the solar panelPVη conversion efficiencyPVmaxAnd establishing a photovoltaic system output model according to the solar radiation degree G (t); according to the output power P of the diesel generatordEstablishing a diesel engine power generation cost model according to the fuel consumption coefficient, and η according to the charging and discharging power delta P and efficiency of the storage batterycEstablishing a charge-discharge model of the energy storage system; establishing load models of the ship under different working conditions according to the 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 when the ship is sailing;
(3) taking the total cost of the ship power system and the system reliability as optimization conditions, and establishing a multi-target multi-constraint optimization configuration model of the ship power system;
(4) optimizing a traditional particle swarm algorithm, and adding a pairwise crossing process of particles in a genetic algorithm into the traditional particle swarm algorithm;
(5) and (3) fusing the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model which are established in the step (1), the photovoltaic system output model corrected in the step (2) and the optimized configuration model established in the step (3) in the optimized algorithm in the step (4), simulating by using mathematical simulation software, and outputting optimal particles, wherein the number of solar panels, the diesel generator capacity and the number of storage batteries which are output by the particles are the optimal capacity configuration of the ship power system.
2. The new energy vessel power system capacity optimization method considering vessel rolling according to claim 1, wherein the concrete contents and methods of establishing the four models in the step (1) are:
1) the specific process for establishing the photovoltaic system output model comprises the following steps: according to the output power P of the photovoltaic power generation systemPV(t) degree of solar irradiance G (t) received at time t, area A of photovoltaic arrayPVMaximum conversion efficiency η of solar panelPVmaxConversion efficiency η for maximum power tracking control of solar panelMPPTTemperature and real-time temperature T during normal operation of solar panelC-normalAnd TCBuilding a shipborne photovoltaic power generation system model under the influence of factors;
2) the specific process for establishing the diesel engine power generation cost model comprises the following steps: approximating a cost model of a diesel generator to its output power PdIs expressed as
Wherein a, b and c are fuel coefficients;
3) the specific process for establishing the energy storage system charge-discharge model comprises the following steps: the method comprises the steps that the storage capacity of an energy storage system at the time t +1 is represented as the storage capacity of the energy storage system at the time t and the storage variation at the time t, and the variation is mainly influenced by the power generation capacity of a diesel engine and a 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 comprises the following steps: and judging different working conditions of the ship during navigation according to the navigation data of the ship, and establishing a load change model of the ship under different working conditions during navigation.
3. The method for optimizing the capacity of the power system of the new energy ship in consideration of the rolling of the ship as claimed in claim 1, wherein the method for correcting in the step (2) comprises the following specific contents and processes:
firstly, taking the extreme condition of the ship during navigation as an analysis basis, and setting the rolling period and frequency under the extreme condition; secondly, the light receiving area A of the solar cell panelpvThe variation of the size is equivalent to the variation of the size of the illumination intensity Eav; then the maximum value Eav of the illumination intensity is calculatedmaxSetting the illumination intensity when the included angle between the solar cell panel and the horizontal plane is 0 degree, thereby obtaining the illumination intensityMinimum value of Eavmax(1-cos θ); finally, a 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 in consideration of ship rolling according to claim 1, wherein the specific process of establishing the multi-objective multi-constraint optimal configuration model of the ship power system in the step (3) is as follows:
1) firstly, calculating the equivalent annual investment cost C of the systemACSThe cost is the sum of equipment installation cost, maintenance cost, replacement cost, fuel cost and environmental protection conversion cost of each power supply; recalculating System reliability costs C for the SystemrelA penalty function of the system load power shortage LPSP and the system capacity excess multiplying power EXC is represented by the formula Crel＝γ[max(0，LPSP-LPSPmax)]+[max(0，EXC-EXCmax)]γ and a penalty factor; finally calculating the equivalent annual investment cost C of the systemACSAnd system reliability cost C of the systemrelThe sum of (a) is an objective function of the optimized configuration model;
2) respectively outputting power P to the micro power supply of the new energy ship according to the area size and the navigation parameters of the shipiAnd battery capacity SOCtThe installed number N of micro power suppliesiA constraint is set.
5. The method for optimizing the capacity of the power system of the new energy ship considering the rolling of the ship as claimed in claim 1, wherein the specific method for adding the pairwise crossing process of the particles in the genetic algorithm into the traditional particle swarm optimization comprises the following steps:
firstly, after an individual fitness optimal value and a global fitness optimal value are found by a traditional particle swarm algorithm, global particles are divided into two parts; then, the speed and position of the former part of particles are used for replacing the latter part of particles, and the latter part of particles are crossed pairwise to generate new filial generation particles; finally, the fitness values of the front part and the rear part of the particles are compared, and the particles with high fitness values are reserved, namely the generated new progeny particles.
6. The method for optimizing the capacity of the new energy ship power system in consideration of ship rolling according to claim 1, wherein the concrete process of fusing the five models to be established and corrected in the optimized algorithm in the step (5) is as follows:
1) initializing the particle speed and the particle position according to the diesel engine power generation cost model, the energy storage system charge-discharge model and the load model which are established in the step (1) and the photovoltaic system output model which is corrected in the step (2);
2) calculating the individual fitness value of each particle according to the objective function in the optimized 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 to the current individual fitness optimal value of the particle, otherwise, returning to the step 3), and continuously searching the particle individual and the global fitness optimal value;
5) optimizing the traditional particle swarm algorithm according to the step (4) to generate new progeny particles;
6) and (4) judging whether the new child particles meet the requirements of the constraint conditions in the optimized configuration model established in the step (3), if so, outputting an optimized result, otherwise, returning to the step (2), and continuously calculating the fitness value of each particle.
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