Combining wave energy with wind and solar: Short-term forecasting
While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the Unite...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Reikard, Gordon [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
15 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Technologies and practice of CO - HU, Yongle ELSEVIER, 2019, an international journal : the official journal of WREN, The World Renewable Energy Network, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:81 ; year:2015 ; pages:442-456 ; extent:15 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.renene.2015.03.032 |
|---|
| Katalog-ID: |
ELV013304240 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV013304240 | ||
| 003 | DE-627 | ||
| 005 | 20230625111849.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180602s2015 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.renene.2015.03.032 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000200A.pica |
| 035 | |a (DE-627)ELV013304240 | ||
| 035 | |a (ELSEVIER)S0960-1481(15)00214-1 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 530 |a 620 | |
| 082 | 0 | 4 | |a 530 |q DE-600 |
| 082 | 0 | 4 | |a 620 |q DE-600 |
| 100 | 1 | |a Reikard, Gordon |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Combining wave energy with wind and solar: Short-term forecasting |
| 264 | 1 | |c 2015transfer abstract | |
| 300 | |a 15 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. | ||
| 520 | |a While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. | ||
| 650 | 7 | |a Wave energy |2 Elsevier | |
| 650 | 7 | |a Solar power |2 Elsevier | |
| 650 | 7 | |a Grid integration |2 Elsevier | |
| 650 | 7 | |a Wind power |2 Elsevier | |
| 650 | 7 | |a Forecasting |2 Elsevier | |
| 700 | 1 | |a Robertson, Bryson |4 oth | |
| 700 | 1 | |a Bidlot, Jean-Raymond |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a HU, Yongle ELSEVIER |t Technologies and practice of CO |d 2019 |d an international journal : the official journal of WREN, The World Renewable Energy Network |g Amsterdam [u.a.] |w (DE-627)ELV002723662 |
| 773 | 1 | 8 | |g volume:81 |g year:2015 |g pages:442-456 |g extent:15 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.renene.2015.03.032 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 951 | |a AR | ||
| 952 | |d 81 |j 2015 |h 442-456 |g 15 | ||
| 953 | |2 045F |a 530 | ||
| author_variant |
g r gr |
|---|---|
| matchkey_str |
reikardgordonrobertsonbrysonbidlotjeanra:2015----:obnnwveegwtwnadoaso |
| hierarchy_sort_str |
2015transfer abstract |
| publishDate |
2015 |
| allfields |
10.1016/j.renene.2015.03.032 doi GBV00000000000200A.pica (DE-627)ELV013304240 (ELSEVIER)S0960-1481(15)00214-1 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Reikard, Gordon verfasserin aut Combining wave energy with wind and solar: Short-term forecasting 2015transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Elsevier Robertson, Bryson oth Bidlot, Jean-Raymond oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:81 year:2015 pages:442-456 extent:15 https://doi.org/10.1016/j.renene.2015.03.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 81 2015 442-456 15 045F 530 |
| spelling |
10.1016/j.renene.2015.03.032 doi GBV00000000000200A.pica (DE-627)ELV013304240 (ELSEVIER)S0960-1481(15)00214-1 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Reikard, Gordon verfasserin aut Combining wave energy with wind and solar: Short-term forecasting 2015transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Elsevier Robertson, Bryson oth Bidlot, Jean-Raymond oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:81 year:2015 pages:442-456 extent:15 https://doi.org/10.1016/j.renene.2015.03.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 81 2015 442-456 15 045F 530 |
| allfields_unstemmed |
10.1016/j.renene.2015.03.032 doi GBV00000000000200A.pica (DE-627)ELV013304240 (ELSEVIER)S0960-1481(15)00214-1 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Reikard, Gordon verfasserin aut Combining wave energy with wind and solar: Short-term forecasting 2015transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Elsevier Robertson, Bryson oth Bidlot, Jean-Raymond oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:81 year:2015 pages:442-456 extent:15 https://doi.org/10.1016/j.renene.2015.03.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 81 2015 442-456 15 045F 530 |
| allfieldsGer |
10.1016/j.renene.2015.03.032 doi GBV00000000000200A.pica (DE-627)ELV013304240 (ELSEVIER)S0960-1481(15)00214-1 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Reikard, Gordon verfasserin aut Combining wave energy with wind and solar: Short-term forecasting 2015transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Elsevier Robertson, Bryson oth Bidlot, Jean-Raymond oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:81 year:2015 pages:442-456 extent:15 https://doi.org/10.1016/j.renene.2015.03.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 81 2015 442-456 15 045F 530 |
| allfieldsSound |
10.1016/j.renene.2015.03.032 doi GBV00000000000200A.pica (DE-627)ELV013304240 (ELSEVIER)S0960-1481(15)00214-1 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Reikard, Gordon verfasserin aut Combining wave energy with wind and solar: Short-term forecasting 2015transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Elsevier Robertson, Bryson oth Bidlot, Jean-Raymond oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:81 year:2015 pages:442-456 extent:15 https://doi.org/10.1016/j.renene.2015.03.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 81 2015 442-456 15 045F 530 |
| language |
English |
| source |
Enthalten in Technologies and practice of CO Amsterdam [u.a.] volume:81 year:2015 pages:442-456 extent:15 |
| sourceStr |
Enthalten in Technologies and practice of CO Amsterdam [u.a.] volume:81 year:2015 pages:442-456 extent:15 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Wave energy Solar power Grid integration Wind power Forecasting |
| dewey-raw |
530 |
| isfreeaccess_bool |
false |
| container_title |
Technologies and practice of CO |
| authorswithroles_txt_mv |
Reikard, Gordon @@aut@@ Robertson, Bryson @@oth@@ Bidlot, Jean-Raymond @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
ELV002723662 |
| dewey-sort |
3530 |
| id |
ELV013304240 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV013304240</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625111849.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180602s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.renene.2015.03.032</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000200A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV013304240</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0960-1481(15)00214-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">530</subfield><subfield code="a">620</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Reikard, Gordon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Combining wave energy with wind and solar: Short-term forecasting</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">15</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wave energy</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Solar power</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Grid integration</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wind power</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Forecasting</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Robertson, Bryson</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bidlot, Jean-Raymond</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">HU, Yongle ELSEVIER</subfield><subfield code="t">Technologies and practice of CO</subfield><subfield code="d">2019</subfield><subfield code="d">an international journal : the official journal of WREN, The World Renewable Energy Network</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV002723662</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:81</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:442-456</subfield><subfield code="g">extent:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.renene.2015.03.032</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">81</subfield><subfield code="j">2015</subfield><subfield code="h">442-456</subfield><subfield code="g">15</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">530</subfield></datafield></record></collection>
|
| author |
Reikard, Gordon |
| spellingShingle |
Reikard, Gordon ddc 530 ddc 620 Elsevier Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Combining wave energy with wind and solar: Short-term forecasting |
| authorStr |
Reikard, Gordon |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV002723662 |
| format |
electronic Article |
| dewey-ones |
530 - Physics 620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
530 620 530 DE-600 620 DE-600 Combining wave energy with wind and solar: Short-term forecasting Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting Elsevier |
| topic |
ddc 530 ddc 620 Elsevier Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting |
| topic_unstemmed |
ddc 530 ddc 620 Elsevier Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting |
| topic_browse |
ddc 530 ddc 620 Elsevier Wave energy Elsevier Solar power Elsevier Grid integration Elsevier Wind power Elsevier Forecasting |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
b r br j r b jrb |
| hierarchy_parent_title |
Technologies and practice of CO |
| hierarchy_parent_id |
ELV002723662 |
| dewey-tens |
530 - Physics 620 - Engineering |
| hierarchy_top_title |
Technologies and practice of CO |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV002723662 |
| title |
Combining wave energy with wind and solar: Short-term forecasting |
| ctrlnum |
(DE-627)ELV013304240 (ELSEVIER)S0960-1481(15)00214-1 |
| title_full |
Combining wave energy with wind and solar: Short-term forecasting |
| author_sort |
Reikard, Gordon |
| journal |
Technologies and practice of CO |
| journalStr |
Technologies and practice of CO |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
zzz |
| container_start_page |
442 |
| author_browse |
Reikard, Gordon |
| container_volume |
81 |
| physical |
15 |
| class |
530 620 530 DE-600 620 DE-600 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Reikard, Gordon |
| doi_str_mv |
10.1016/j.renene.2015.03.032 |
| dewey-full |
530 620 |
| title_sort |
combining wave energy with wind and solar: short-term forecasting |
| title_auth |
Combining wave energy with wind and solar: Short-term forecasting |
| abstract |
While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. |
| abstractGer |
While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. |
| abstract_unstemmed |
While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Combining wave energy with wind and solar: Short-term forecasting |
| url |
https://doi.org/10.1016/j.renene.2015.03.032 |
| remote_bool |
true |
| author2 |
Robertson, Bryson Bidlot, Jean-Raymond |
| author2Str |
Robertson, Bryson Bidlot, Jean-Raymond |
| ppnlink |
ELV002723662 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.renene.2015.03.032 |
| up_date |
2024-07-06T18:33:49.695Z |
| _version_ |
1803855677039312896 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV013304240</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625111849.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180602s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.renene.2015.03.032</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000200A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV013304240</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0960-1481(15)00214-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">530</subfield><subfield code="a">620</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Reikard, Gordon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Combining wave energy with wind and solar: Short-term forecasting</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">15</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">While wind and solar have been the leading sources of renewable energy up to now, waves are increasingly being recognized as a viable source of power for coastal regions. This study analyzes integrating wave energy into the grid, in conjunction with wind and solar. The Pacific Northwest in the United States has a favorable mix of all three sources. Load and wind power series are obtained from government databases. Solar power is calculated from 12 sites over five states. Wave energy is calculated using buoy data, simulations of the ECMWF model, and power matrices for three types of wave energy converters. At the short horizons required for planning, the properties of the load and renewable energy are dissimilar. The load exhibits cycles at 24 h and seven days, seasonality and long-term trending. Solar power is dominated by the diurnal cycle and by seasonality, but also exhibits nonlinear variability due to cloud cover, atmospheric turbidity and precipitation. Wind power is dominated by large ramp events–irregular transitions between states of high and low power. Wave energy exhibits seasonal cycles and is generally smoother, although there are still some large transitions, particularly during winter months. Forecasting experiments are run over horizons of 1–4 h for the load and all three types of renewable energy. Waves are found to be more predictable than wind and solar. The forecast error at 1 h for the simulated wave farms is in the range of 5–7 percent, while the forecast errors for solar and wind are 17 and 22 percent. Geographic dispersal increases forecast accuracy. At the 1 h horizon, the forecast error for large-scale wave farms is 39–49 percent lower than at individual buoys. Grid integration costs are quantified by calculating balancing reserves. Waves show the lowest reserve costs, less than half wind and solar.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wave energy</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Solar power</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Grid integration</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wind power</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Forecasting</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Robertson, Bryson</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bidlot, Jean-Raymond</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">HU, Yongle ELSEVIER</subfield><subfield code="t">Technologies and practice of CO</subfield><subfield code="d">2019</subfield><subfield code="d">an international journal : the official journal of WREN, The World Renewable Energy Network</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV002723662</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:81</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:442-456</subfield><subfield code="g">extent:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.renene.2015.03.032</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">81</subfield><subfield code="j">2015</subfield><subfield code="h">442-456</subfield><subfield code="g">15</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">530</subfield></datafield></record></collection>
|
| score |
7.402439 |