The deepening penetration of wind resources introduces major challenges into power system planning and operation activities. This is due to the need to appropriately represent salient features of wind power generation from multiple wind farm sites such as nonstationarity with distinct diurnal and seasonal patterns, spatial and temporal correlations, and non-Gaussianity. Hence, an appropriate model of multisite wind power production in systems with integrated wind resources represents a major challenge to meet a critical need. In this paper, we aim at defining a new methodology to improve the quality of generated scenarios by means of historical multisite wind data and effective deployment of time series and principal component (PC) techniques. Scenario-based methodologies are already available in power systems, but sometimes lack in accuracy: this paper proposes a methodology that is able to capture the main features of wind: it can both characterize spatio-temporal properties and be used to reduce size of data sets in practical applications without using any simplifying assumption. Extensive testing indicates good performance in effectively capturing the salient wind characteristics to provide useful models for various problems related to multisite wind production, including security assessment, operational planning, environmental analysis, and system planning. An application to security assessment is presented.
- stochastic processes
- time series
- wind power generation
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment