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Oceans contain rich tidal current energy, which can provide sufficient power for offshore microgrids. However, the uncertainty of tidal flow may endanger the operational reliability of an offshore microgrid. In this paper, a probabilistic prediction model of tidal current is established based on support vector quantile regression to reduce the infl...

## Contexts in source publication

**Context 1**

... structural diagram of the offshore microgrid is shown in Figure 1. The system mainly includes renewable energy power generation units, a gas turbine unit, an energy storage unit and an emergency power unit. ...

**Context 2**

... paper adopted the dragonfly algorithm to solve the model. Figure 10 shows the power output of each unit in the offshore microgrid in island operation mode. ...

**Context 3**

... can be seen from Figure 10 that during the island operation of the offshore microgrid, the system first used tidal energy, followed by the gas turbine. The emergency diesel engine would not start if there was no special case, which was determined by the economic cost and environmental protection cost in this paper. ...

**Context 4**

... emergency diesel engine would not start if there was no special case, which was determined by the economic cost and environmental protection cost in this paper. Moreover, it can be observed in Figure 10 that under the optimization of the algorithm, the energy storage system was charged in the electricity valley and discharged at the electricity consumption peak, which fully reflects the value of cutting peaks, filling the valley and reducing fluctuations. Figure 11 shows that the dragonfly algorithm converged to the optimal value when the iteration reached 50 times, which reflected that the dragonfly algorithm had satisfied convergence speed and less optimization time. ...

**Context 5**

... it can be observed in Figure 10 that under the optimization of the algorithm, the energy storage system was charged in the electricity valley and discharged at the electricity consumption peak, which fully reflects the value of cutting peaks, filling the valley and reducing fluctuations. Figure 11 shows that the dragonfly algorithm converged to the optimal value when the iteration reached 50 times, which reflected that the dragonfly algorithm had satisfied convergence speed and less optimization time. The offshore microgrid in grid-connected mode was employed to verify the effectiveness and correctness of the proposed method under different operation modes, as shown in Figure 12, and the tidal current generators are located in node 21 and node 25. ...

**Context 6**

... 11 shows that the dragonfly algorithm converged to the optimal value when the iteration reached 50 times, which reflected that the dragonfly algorithm had satisfied convergence speed and less optimization time. The offshore microgrid in grid-connected mode was employed to verify the effectiveness and correctness of the proposed method under different operation modes, as shown in Figure 12, and the tidal current generators are located in node 21 and node 25. In the dotted frame is a small circular island microgrid, which is connected to another large offshore microgrid to form an offshore microgrid with the grid-connected mode. ...

**Context 7**

... the dotted frame is a small circular island microgrid, which is connected to another large offshore microgrid to form an offshore microgrid with the grid-connected mode. Figure 12. Offshore microgrid in grid-connected operation mode. ...

**Context 8**

... microgrid in grid-connected operation mode. Figure 13 shows the power output of each unit in the offshore microgrid under grid-connected operation mode. It can be seen from Figure 13 that the tidal energy was still the priority power and then the gas turbine. ...

**Context 9**

... 13 shows the power output of each unit in the offshore microgrid under grid-connected operation mode. It can be seen from Figure 13 that the tidal energy was still the priority power and then the gas turbine. Figure 14 shows that the dragonfly algorithm converged to the optimal value when the iteration reached forty-five times. ...

**Context 10**

... can be seen from Figure 13 that the tidal energy was still the priority power and then the gas turbine. Figure 14 shows that the dragonfly algorithm converged to the optimal value when the iteration reached forty-five times. In summary, both in island operation mode or grid-connected operation mode, the optimal dispatching strategy proposed in this paper could make the microgrid run in a more effective and environmentally friendly state. ...

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Tidal-stream energy can be predicted deterministically, provided tidal harmonics and turbine-device characteristics are known. Many turbine designs exist, all having different characteristics (e.g. rated speed), which creates uncertainty in resource assessment or renewable energy system-design decision-making. A standardised normalised tidal-stream power-density curve was parameterised with data from 14 operational horizontal-axis turbines (e.g. mean cut-in speed was ∼30% of rated speed). Applying FES2014 global tidal data (1/16° gridded resolution) up to 25 km from the coast, allowed optimal turbine rated speed assessment. Maximum yield was found for turbine rated speed ∼97% of maximum current speed (maxU) using the 4 largest tidal constituents (M2, S2, K1 and O1) and ∼87% maxU for a “high yield” scenario (highest Capacity Factor in top 5% of yield cases); with little spatial variability found for either. Optimisation for firm power (highest Capacity Factor with power gaps less than 2 hours), which is important for problematic or expensive energy-storage cases (e.g. off-grid), turbine rated speed of ∼56% maxU was found – but with spatial variability due to tidal form and maximum current speed. We find optimisation and convergent design is possible, and our standardised power curve should help future research in resource and environmental impact assessment.