TY - GEN
T1 - Integration of hydro and renewable energy resources in energy planning
AU - De La Cruz, Lucero Cynthia Luciano
AU - Celis, Cesar
N1 - Publisher Copyright:
Copyright © 2020 ASME.
PY - 2020
Y1 - 2020
N2 - Renewable energy is the energy obtained from resources inexhaustible in the long term. Furthermore, in some countries, non-conventional renewable energy includes solar, wind, biomass, geothermal and mini-hydropower. The definition of mini-hydropower plants varies depending on the country. As an example, in Peru and Canada, mini-hydropower plants have different installing capacities, below 20MW and 50MW, respectively. Accordingly, this work (i) discusses the Energy Balance and challenges that renewable energies have to face on their way to the energy transition, (ii) highlights the forecast models to generate renewable energy in short-term energy planning. The historical data about the renewable energy resources and the energy produced have been obtained by COES. The R studio software was used for statistical analysis of renewable resources and electricity generation. Also, a forecast model was developed using a neural network to forecast renewable energy generation. The results show a strong correlation between hydro resources and non-conventional renewable energy resources. Finally, the data obtained from the renewable generation forecast model were used as input to carry out a short-term dispatch model using GAMS software to determine the forecast of daily marginal cost in SEIN.
AB - Renewable energy is the energy obtained from resources inexhaustible in the long term. Furthermore, in some countries, non-conventional renewable energy includes solar, wind, biomass, geothermal and mini-hydropower. The definition of mini-hydropower plants varies depending on the country. As an example, in Peru and Canada, mini-hydropower plants have different installing capacities, below 20MW and 50MW, respectively. Accordingly, this work (i) discusses the Energy Balance and challenges that renewable energies have to face on their way to the energy transition, (ii) highlights the forecast models to generate renewable energy in short-term energy planning. The historical data about the renewable energy resources and the energy produced have been obtained by COES. The R studio software was used for statistical analysis of renewable resources and electricity generation. Also, a forecast model was developed using a neural network to forecast renewable energy generation. The results show a strong correlation between hydro resources and non-conventional renewable energy resources. Finally, the data obtained from the renewable generation forecast model were used as input to carry out a short-term dispatch model using GAMS software to determine the forecast of daily marginal cost in SEIN.
KW - Hydro
KW - Planning
KW - Renewable energy
KW - Solar energy
UR - http://www.scopus.com/inward/record.url?scp=85094184620&partnerID=8YFLogxK
U2 - 10.1115/POWER2020-16376
DO - 10.1115/POWER2020-16376
M3 - Conference contribution
AN - SCOPUS:85094184620
T3 - American Society of Mechanical Engineers, Power Division (Publication) POWER
BT - ASME 2020 Power Conference, POWER 2020, collocated with the 2020 International Conference on Nuclear Engineering
PB - American Society of Mechanical Engineers (ASME)
T2 - 2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019
Y2 - 12 June 2019 through 15 June 2019
ER -