# A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm

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## Abstract

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## 1. Introduction

## 2. A Solar-Diesel Hybrid Electrical Generator System of a Ship

^{2}. With regards to the needs of the maintenance channels, a solar PV system with a total capacity of 143 kWp was installed. The solar module is a 400 mm series monocrystalline silicon solar panel, its peak power under standard test conditions is 265 W, and it measures 1650 × 990 × 40 mm

^{3}and weighs 19.1 kg. According to the standard irradiance conditions (1000 W/m

^{2}, panel temperature: 20 °C), 18 in-series and 30 in-parallel panels were used, accounting for a total of 540 solar panels.

- During an off-grid operation, the PV modules charge the battery through the PV controller, and the battery and/or PV voltage is converted directly to AC 450 V. After that, the voltage is stepped-down through a three-phase transformer and powers the load directly.
- In a grid-connected operation, the PV modules charge the battery via the PV controller, and the energy of the battery and/or PV is fed back to the grid. To prevent current reverse in the ship’s synchronous generator, a set of anti-backflow devices are equipped with a Busbar system.
- When the PV system’s output power drops sharply, and the battery’s energy is insufficient to support the load, the bypass backup function is activated and the ship’s electric power system is used to supply energy to the lighting load.
- When a fault in the PV system occurs, the single-pole, double-throw switch is switched to the original ship’s power grid and diesel generator sets to supply energy to the lighting load.

## 3. A Multi-Objective Optimization Model for Energy Management

_{fi}is the fuel density of the ith diesel generator, setting ρ

_{fi}= 820 kg/m

^{3}; Q

_{i}(t) denotes the fuel consumption of the ith diesel generator (kg/h); and LHVrepresents the lower heating value of the fuel, setting LHV = 43.2. MJ/kg

_{i}(t) and b

_{i}(t) represent the fuel intercept coefficient and fuel slope coefficient of the ith diesel generator, which are 13.44 and 207.58 g/kWh respectively; and P

_{ri}denotes the rated power of the ith diesel generator (P

_{r1}= P

_{r2}= 1020 kW)

## 4. The PSO Algorithm

_{N}(t), and apply Equation (7) to calculate the fitness value of each particle. Then, an optimal value and optimal population value are calculated.

_{N}(t).

## 5. Results

- When the battery’s SOC is ≤30%, the battery is in a state where it needs to be charged. Currently, the maximum power of the new energy connected to the grid is the solar energy’s peak power, that is P
_{Nmax}= 143 kW. Figure 6 shows the distribution of the non-inferior solution with ${P}_{N}$ in the region (0, 143 kW).

- 2
- When the battery’s SOC is >30%, the battery is in a state of discharging. At present, the maximum power required for a new energy injection is 390 kW. Figure 7 displays the distribution of the non-inferior solution with ${P}_{N}$ in the range of 0–390 kW.

_{L}= 600 kW and P

_{L}= 700 kW, there is a slight difference in the specific fuel consumption with the same injection power of new energy. Hence, it can be concluded that when the ship’s electrical load is less than the power injection of new energy, the new energy system should be given priority to power the ship. At this time, the entire ship has the optimal fuel economy. When the ship’s electrical load is greater than the injection power of new energy and the load is high, the ship should be economically oriented and new energy should be injected as much as possible; if the load is low, the ship’s economy and the diesel generator’s efficiency must be fully considered.

_{2}emission are calculated as shown in Equations (9) and (10), respectively.

_{co2}is the ship annual CO

_{2}emission (t); and α is the carbon emission coefficient, with a value of 3.175.

_{2}emissions per year. However, when the new energy source is connected to the power grid, the fuel consumption and CO

_{2}emissions are 1074.44 t and 3411.66 t, respectively, one year after using the PSO algorithm method. This saves 12.38 t of fuel and 39.31 t of CO

_{2}compared to a year without using this method, and the diesel generator’s efficiency is basically kept at the same level. Therefore, the PSO algorithm method can further reduce the ship’s fuel consumption, and lead to the diesel generator operating in the range of appropriate efficiency.

## 6. Conclusions

#### 6.1. The Battery’s SOC is ≤30%

#### 6.2. The Battery’s SOC is >30%

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Overall length | 182.80 m | speed | 20.20 kn |

Beam | 32.20 m | total deadweight tonnage | 14,759.06 t |

Depth | 14.95 m | number of parking Spaces | 5000 pctc |

Design draft | 8.40 m |

${\mathit{c}}_{1}$ | ${\mathit{c}}_{2}$ | $\_{\_}_{\mathit{m}\mathit{a}\mathit{x}}$ | $\_{\_}_{\mathit{m}\mathit{i}\mathit{n}}$ | xSize | MaxIt |
---|---|---|---|---|---|

2 | 2 | 0.9 | -1 | 50 | 4000 |

**Table 3.**The ship’s specific fuel consumption and diesel generator’s efficiency: experimental measurements versus optimization results.

$\mathbf{When}\mathbf{SOC}\le 30\mathbf{\%}$ | $\mathbf{When}\mathbf{SOC}30\mathbf{\%}$ | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Power of Load (kW) | Experimental Measurements | Optimization Results | Experimental Measurements | Optimization Results | ||||||||

${P}_{N}$(kW) | $\overline{g}$ (g/kWh) | $\eta $ | ${P}_{N}$ (kW) | $\overline{g}$ (g/kWh) | $\eta $ | ${P}_{N}$ (kW) | $\overline{g}$ (g/kWh) | $\eta $ | ${P}_{N}$(kW) | $\overline{g}$ (g/kWh) | $\eta $ | |

300 | 130 | 281.8 | 17.64% | 100 | 285.2 | 21.32% | 80 | 327 | 23.1% | 300 | 0 | |

400 | 121 | 272.5 | 27.8% | 115 | 272.1 | 29.6% | 220 | 288.78 | 11.58% | 202.5 | 159.8 | 25.4% |

500 | 111.8 | 237.2 | 40.4% | 140 | 230.3 | 37.9% | 264 | 201.8 | 24.5% | 281.5 | 194.8 | 23.76% |

600 | 117 | 207.2 | 50.3% | 140.8 | 201.1 | 47.8% | 359.1 | 164.5 | 35.5% | 393.8 | 157.5 | 33.2% |

**Table 4.**The effect on energy saving and reducing emission when the PSO algorithm method is used for one year.

Experimental Measurements | Optimization Results | ||
---|---|---|---|

fuel consumption (t) | 1086.82 | fuel consumption (t) | 1074.44 |

CO_{2} emission (t) | 3450.66 | CO_{2} emission (t) | 3411.35 |

efficiency of diesel generator | 39.05% | efficiency of diesel generator | 38.7% |

Fuel saving (t) | 12.38 | ||

Emission reduction (t) | 39.31 |

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**MDPI and ACS Style**

Yang, R.; Yuan, Y.; Ying, R.; Shen, B.; Long, T.
A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm. *Energies* **2020**, *13*, 1380.
https://doi.org/10.3390/en13061380

**AMA Style**

Yang R, Yuan Y, Ying R, Shen B, Long T.
A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm. *Energies*. 2020; 13(6):1380.
https://doi.org/10.3390/en13061380

**Chicago/Turabian Style**

Yang, Rui, Yupeng Yuan, Rushun Ying, Boyang Shen, and Teng Long.
2020. "A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm" *Energies* 13, no. 6: 1380.
https://doi.org/10.3390/en13061380