Abstract
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.
Highlights
Uncertainty in power system planning becomes a critical factor as the capacity of renewable generators in power systems increases to cope with global warming and climate change due to electricity generation
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; ignoring the hourly variability may lead to higher building costs and higher capacity of solar PV systems
A multi-stage stochastic generation planning model is presented in [3], where seasonal, day- and nighttime random samples are generated for wind power availability, solar direct normal irradiance (DNI), and electric load using Gaussian copula
Summary
Uncertainty in power system planning becomes a critical factor as the capacity of renewable generators in power systems increases to cope with global warming and climate change due to electricity generation. The models above consider a detailed temporal variability by applying short time intervals in long-term planning models; the uncertainty of renewable resources is not explicitly modeled in the mathematical problem. Several stochastic generation planning models containing a high-resolution operational model are introduced, e.g., [12], where a stochastic model is developed under a two-stage stochastic programming framework with hourly operational intervals; transmission capacity constraints are not considered, and the scenario paths for renewable resources such as wind and solar power availability are based on the historical information; the obtained solutions rely heavily on historical events, not future events that possibly occur with a certain level of probability.
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