| 1 |
Understanding New Zealand & rsquo;s wind resources as a route to 100% renewable electricity
Poletti S, Staffell I
Renewable Energy, 170, 449, 2021 |
| 2 |
Risk-controlled economic performance of compressed air energy storage and wind generation in day-ahead, intraday and balancing markets
Sriyakul T, Jermsittiparsert K
Renewable Energy, 165, 182, 2021 |
| 3 |
Battery energy storage system state-of-charge management to ensure availability of frequency regulating services from wind farms
Boyle J, Littler T, Foley A
Renewable Energy, 160, 1119, 2020 |
| 4 |
Assessing new transmission and energy storage in achieving increasing renewable generation targets in a regional grid
Conlon T, Waite M, Modi V
Applied Energy, 250, 1085, 2019 |
| 5 |
CVaR-constrained scheduling strategy for smart multi carrier energy hub considering demand response and compressed air energy storage
Jadidbonab M, Babaei E, Mohammadi-ivatloo B
Energy, 174, 1238, 2019 |
| 6 |
Financial support vis-a-vis share of wind generation: Is there an inflection point?
Gupta D, Das A, Garg A
Energy, 181, 1064, 2019 |
| 7 |
The effect of wind and solar power generation on wholesale electricity prices in Australia
Csereldyei Z, Qu S, Ancev T
Energy Policy, 131, 358, 2019 |
| 8 |
Risk-averse stochastic operation of a power system integrated with hydrogen storage system and wind generation in the presence of demand response program
Yu DM, Wang JW, Li DZ, Jermsittiparsert K, Nojavan S
International Journal of Hydrogen Energy, 44(59), 31204, 2019 |
| 9 |
Consequences of selecting technology pathways on cumulative carbon dioxide emissions for the United Kingdom
Roberts SH, Foran BD, Axon CJ, Warr BS, Goddard NH
Applied Energy, 228, 409, 2018 |
| 10 |
Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks
Camelo HD, Lucio PS, Leal JB, De Carvalho PCM, dos Santos DV
Energy, 151, 347, 2018 |
