Optimal capacity planning of generation system integrating uncertain solar and wind energy with seasonal variability

This paper presents a generation capacity planning model for integration of utility-scale wind farms and grid-connected solar photovoltaic (PV) generation systems via multi-stage stochastic programming. A multi-stage scenario tree for available wind power, electric load, and solar irradiance is constructed with nine stages for a year. Random samples for wind, load, and solar irradiance are generated using Gaussian copula which represents correlation between random samples. Environmental energy policies to control carbon dioxide (CO₂) emissions and increase energy generation from renewable sources are implemented, and the resulting generation capacity mix is investigated. A case study with a modified IEEE 300-bus system is performed. With the presented model, optimal generation capacity satisfying policy constraints in a future time was found within a reasonable amount of time.