This suite of tools for resource modeling has two main objectives: (i) prepare a set of renewable candidate projects such as onshore/offshore wind power, solar PV, hydropower, and pumped hydro storage; and (ii) generate multivariate scenarios of hydro inflows, wind speed, and solar irradiation that are translated into energy production scenarios based on the technical characteristics of the projects. The goal of the tools described in this section is to enable a detailed representation of renewable projects in energy planning studies, including seasonality, spatial correlations, daily profiles, and short–term variability (hourly or intra–hourly).

Time Series Lab - Non-conventional renewable resource modeling tool

The Time Series Lab (TSL) is a renewable modeling tool that produces future synthetic scenarios of intermittent Variable Renewable Energy (VRE) sources. TSL has two main modules: (i) the TSL-Data and (ii) the TSL-Scenarios:
  • TSL-Data: creates a 40-year “synthetic” hourly historical record by processing the information available at the global reanalysis database;
  • TSL-Scenarios: generates future VRE scenarios that are temporally and spatially correlated with hydro inflows;
To estimate the statistical model, TSL-Scenarios needs historical data of VRE generation, which may be a very challenging task to obtain for some hotspots. Related to that, TSL has two main functionalities:
  • Real historical data can be introduced by the user
  • A 40-year “synthetic” hourly historical record can be created by TSL-Data based on reanalysis data of wind speed and solar irradiation

Creating a historical renewable record

The TSL calculates the wind production through a model based on the Virtual Wind Farm (VWF) methodology. The following parameters are used to convert wind speed into energy:
  • The power curve of the turbine
  • The height of the turbine
  • The coordinate of the plant (to download the wind speed data)
The solar production is based on the data of Global Horizontal Irradiation, i.e., the irradiation at the top of the atmosphere extracted from the reanalysis database. Taking this information into account, the GSEE (Global Solar Energy Estimator) method is applied. The following parameters are used:
  • The tracking system of the panel
  • The inclination angle of the panel
  • The coordinate of the solar plant (to download the solar irradiation and temperature data)

Finding hotspots for generic projects

Besides providing a framework to create a “synthetic” historical data of renewable generation, the TSL provides a tool to find “hotspots” for generic wind and solar plant projects. For this task, the following tools are available:
  • Wind speed map for the whole world
  • Solar irradiation map for the whole world
  • Protected areas for the whole world
  • Possibility to upload custom user-defined maps

Generating synthetic renewable generation scenarios correlated with hydro inflows

Due to the spatial correlation of wind and solar generation in different regions, as well as the spatial and temporal correlations between hydro inflows and wind speed in some regions, TSL represents the joint probability distribution of all intermittent renewable and hydro resources for both existing and future plants. The Bayesian Network methodology is a statistical model that can produce synthetic scenarios, capturing the most significant correlations existing in the historical data. The main characteristics of this model are:
  • Produce scenarios considering a joint probability distribution
  • The probability distribution of each plant is numerically estimated through a non-parametric method
  • Maintain temporal and spatial correlations in the synthetic scenarios being produced
  • Maintain the original probability distribution of the historical record
In summary, because of the high variability and intermittency of this kind of resource, the TSL generates those scenarios with the following features:
  • Hourly resolution
  • Non-parametric estimation of the probability distributions
  • Bayesian network methodology to capture the temporal and spatial correlations between VRE and hydro inflows