Generation capacity expansion planning considering hourly dynamics of renewable resources

As more generation capacity using renewable sources is accommodated in the power system, methods to represent the uncertainty of renewable sources become more important, and stochastic models with different methods for uncertainty representation are introduced. This paper investigates the impacts of hourly variability representation of random variables on a stochastic generation capacity expansion planning model. In order to represent the hourly variability as well as uncertainty of the random parameters such as wind power availability, solar irradiance, and load, AutoRegressive-To-Anything (ARTA) stochastic process is applied. By using autocorrelations and marginal distributions of the random parameters, a stochastic process with hourly intervals is generated, where generated random sample paths are used for scenarios. A mathematical formulation using stochastic programming is presented, and a modified IEEE 300-bus system with transmission line constraints is employed to the mathematical model as a test system. Optimal generation capacity solutions obtained using GAMS/CPLEX are compared to the ones from the model only capturing the uncertainty and seasonal variability of the random parameters. The comparison results indicate that the economic value of solar photovoltaic (PV) generation may be overestimated in the case where the hourly variability is not reflected; thus, ignoring the hourly variability may lead to higher building costs and higher capacity of solar PV systems.