Abstract
The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.
Highlights
Renewable generation has received interest over the last few decades due to its environmentally sustainable and cost-effective operation relative to traditional energy sources
The economic dispatch problem of a power system is formulated mathematically, taking into account the following: the use of a DC optimal power flow model; the cost functions are linear; the inter-temporal constraints, such as ramping limits, are not included in the formulation; the uncertainty is presumed to be exclusively generated by wind generators; the uncertainty associated with wind generation can be effectively modeled by a finite set of scenarios and their probability of occurrence; and conventional units are considered to be entirely dispatchable from their minimum to their maximum capacity
The proposed scenario creation methodology consists of a three-step process: first, the spatial covariance structure of the sampled points is determined by fitting a semivariogram; second, weights derived from this covariance structure are used to interpolate values for unsampled points; and third, the forecast values are used to generate power scenarios by the analog method
Summary
Renewable generation has received interest over the last few decades due to its environmentally sustainable and cost-effective operation relative to traditional energy sources. The integration of this intermittent energy into the power grid presents a range of challenges in the operation and management of these systems because energy cannot be dispatched in the conventional sense [1,2,3,4]. Wind is influenced by temperature, humidity, direction, and certain factors, which can lead to unpredictable behavior It is non-stationary and typically has strong diurnal and seasonal trends. It is temporally and spatially auto-correlated and demonstrates heteroscedasticity. There is a requirement to develop more reliable, scalable, and realistic alternatives to modeling renewable generation uncertainties in order to accommodate a higher penetration of renewable energy sources into the grid [1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
