{"id":889,"date":"2019-06-24T15:43:07","date_gmt":"2019-06-24T15:43:07","guid":{"rendered":"http:\/\/www.bigdataocean.eu\/site\/?p=889"},"modified":"2019-06-24T15:43:07","modified_gmt":"2019-06-24T15:43:07","slug":"bigdataocean-platform-use-case-assessing-the-impact-of-the-variability-of-the-resource-in-the-electrical-energy-conversion","status":"publish","type":"post","link":"http:\/\/www.bigdataocean.eu\/site\/2019\/06\/24\/bigdataocean-platform-use-case-assessing-the-impact-of-the-variability-of-the-resource-in-the-electrical-energy-conversion\/","title":{"rendered":"BigDataOcean platform use case: Assessing the impact of the variability of the resource in the electrical energy conversion"},"content":{"rendered":"

Renewable resources are intermittent by nature, and this intermittency can impact electrical energy conversion throughout days, seasons and years. BigDataOcean (BDO) platform allows assessing the effect of this variability throughout those different time scales in a fast and efficient way.<\/p>\n

In this post, the BDO platform is used to determine the variability of the wave energy resource throughout two consecutive years, on a specific location on the Portuguese coast, and then to quantify the impact of that interannual variability on the energy output of one wave energy converter (WEC). This would allow a designer to have a preliminary assessment of the best location to place its WEC and even to select the best WEC from its portfolio.<\/p>\n

The selected site is located at 39.510N; -9.640E, 26 km from the coast, with a water depth of 2.000. The selected years are 2017 and 2018, and the WEC is the Pelamis (attenuator type, 750 kW rated power). The \u201cWave resource assessment (single location)\u201d application of the \u201cPrimary energy resource assessment\u201d service, is first run to obtain the sea states matrixes for 2017 and 2018, and to check the interannual variability, as depicted in Figures 1 and 2. The Maretec Wave Forecast dataset, available through the platform, is used.
\nThe interannual variability is clear from those figures. In 2017, the most verified sea states corresponded to a 1.6 m significant height and 5.4 s period, while in 2018 the mean wave significant height decreased to 1.1 m and the period increased to 8.0 s.
\nSeveral differences are also clear in the monthly average power of waves (which corresponds directly to the available energy), which e.g. nearly doubled in January from 2017 to 2018. Comparing the total available energy, there was a 30% increase from 2017 (229.39 MWh\/m) to 2018 (297.24 MWh\/m).<\/p>\n

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\nFigure 1 \u2013 Occurrence matrixes for the selected site<\/p>\n

To calculate the energy production by a Pelamis WEC in those two years, the \u201cMultiple WEC\u2019s Assessment (Single Location)\u201d application, from the \u201cEnergy conversion assessment\u201d service is used. The power matrix of the Pelamis, as well as from other WECs is already loaded in the BDO platform, and only the location, timeframe and dataset need to be defined. <\/p>\n

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\nFigure 2 \u2013 Mean monthly average power of waves (kW\/m) in 2017 (top) and 2018 (bottom).<\/p>\n

The Pelamis prospective production in both years is shown in Figure 3. In 2017 the peak production is achieved in December (658 kW\/m), while in 2018 is in March-April, although with a lower value (586 kW\/m). The larger value of the resource in 2018 also leads to higher global production in that year (1.07 MWh\/m, against 0.93 MWh\/m in 2017), as well as a higher capacity factor (21.0%, against 16.2% in 2017). After processing the production data generated by the BDO platform it is possible to derive the monthly interannual variation of production in 2017 and 2018, which is plotted in Figure 4. It is possible to see that, for instance, even though the primary resource nearly doubled in January, from 2017 to 2018, the Pelamis production only increased by around 50%, which is due to the nonlinear power characteristic of the WEC.
\n\"\"<\/a>
\nFigure 3 \u2013 Production (blue line) of the Pelamis WEC (kW\/m) in 2017 (top) and 2018 (bottom). The monthly capacity factor is also shown (yellow line).<\/p>\n

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\nFigure 4 \u2013 Interannual variation of the Pelamis energy generation, from 2017 to 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

Renewable resources are intermittent by nature, and this intermittency can impact electrical energy conversion throughout days, seasons and years. BigDataOcean (BDO) platform allows assessing the effect of this variability throughout those different time scales in a fast and efficient way. In this post, the BDO platform is used to determine<\/p>\n

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