Search

Abstract: Two of the most significant challenges in the 21st century will be to improve energy security and reduce the greenhouse gas emissions associated with energy consumption. A co-beneficial solution to these challenges is seen as increasing the use of renewable energy for the production of electricity. Some renewable sources, such as wind are often presented as a way to reduce greenhouse gas emissions; however, since wind’s variability increases uncertainty and risk in expected generation, it can be detrimental to energy security. One of the ways in which wind’s contribution to a jurisdiction’s energy security and greenhouse gas reduction strategies can be improved is to employ a forecasting method that can help reduce risks. This paper proposes a method that applies risk and reliability analysis techniques to obtain the most-likely RL (Resistance–Load) scenario using a set of historical data for wind-supply or generation and load. RL estimates the reliability of a wind-energy system by simulating an anticipated resistance (the electrical generation) attempting to meet a load (the electricity demand) for a future year. The method is demonstrated through a case study and its results are compared with real-time data from a 12MW wind farm to prove its efficacy. [Copyright &y& Elsevier]