Uncertainty Cost Functions for Renewable Generation: A Simplified Approach using a Mixture of Uniform Probability Distribution

Abstract

Photovoltaic energy, wind energy, and plug-in electric/hybrid vehicles are being considered as sources and loads, reflecting the increasing importance of renewable energy resources in new microgrids. However, the stochastic behavior of variables such as wind turbine speed, solar irradiation intensity and, plugin electric vehicle dynamics, introduces uncertainties that could affect the economic dispatch of electric power. This paper employs a mixture of uniform probability distribution (UPDs) techniques to characterize the variability of the available power from renewable energy sources. We propose a new analytical expression derived from the mixture of UPDs to calculate Uncertainty Cost Functions (UCFs), thereby assessing their impact on the economic dispatch of power. Finally, we performed Montecarlo simulations to validate our UCF methodology and its potential applicability in economic dispatch of power. The results demonstrate that our methodology accurately calculates the underestimated and overestimated costs of uncertainty power generation. This methodology holds the potential to optimize economic dispatch, thereby reducing costs and maximizing power generation from the generators.