A multi‐stage transition toward high renewable energy penetration in Queensland, Australia. Issue 4 (15th January 2018)

• Record Type: Journal Article
• Authors: Nunes, Juliana B.; Mahmoudi, Nadali; Saha, Tapan K.; Chattopadhyay, Debabrata
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 4(2018)
• Page Start: 850
• Page End: 858
• Publication Date: 2018-01-15
• Subjects: power generation planning -- power transmission planning -- stochastic processes -- integer programming -- power generation economics -- power transmission economics; multistage transition -- high renewable energy penetration -- Queensland -- Australia -- power system planning -- high variable renewable energy resources -- stochastic multistage planning model -- investment decisions -- transmission systems -- VRE resources -- mixed‐integer programming approach -- transmission investments -- generation investments -- alternative renewable policy targets -- renewable energy target -- cleaner technologies -- solar photovoltaic -- wind power; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.0930
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : This study deals with new challenges in the long‐term planning of power systems in the presence of high variable renewable energy resources (VRE). To this end, a stochastic multi‐stage planning model is proposed, through which the investment decisions on the new generation and transmission systems are made in several stages of the planning horizon considering the attributes of VRE resources. The model takes into account both long‐term and short‐term uncertainties through their plausible scenario realisations. The proposed model is formulated as a mixed‐integer programming approach that co‐optimises generation and transmission investments under uncertainty for alternative renewable policy targets. The model is applied in a realistic case of Queensland, Australia in which an equivalent network of the state is driven to accurately capture the existing and new generation candidates. The model results demonstrate that a 50% renewable energy target may increase the system costs – mostly through an increase in capital costs of cleaner technologies – by three to four times. The analysis also shows that the complementarity of solar photovoltaic and wind and their locations in the power system are important factors in deciding the optimal renewable‐based investments.