Long-term optimal energy mix planning towards high energy security and low GHG emission. (15th September 2015)

• Record Type: Journal Article
• Authors: Thangavelu, Sundar Raj; Khambadkone, Ashwin M.; Karimi, Iftekhar A.
• Is Part Of: Applied energy. Volume 154(2015:Sep. 15)
• Page Start: 959
• Page End: 969
• Publication Date: 2015-09-15
• Subjects: Long-term energy planning -- Stochastic optimization -- Optimal energy mix -- Uncertainty modeling -- Energy security -- Emission reduction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.05.087
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: We develop long-term energy planning considering the future uncertain inputs. We analyze the effect of uncertain inputs on the energy cost and energy security. Conventional energy mix prone to cause high energy cost and energy security issues. Stochastic and optimal energy mix show benefits over conventional energy planning. Nuclear option consideration reduces the energy cost and carbon emissions. Abstract: Conventional energy planning focused on energy cost, GHG emission and renewable contribution based on future energy demand, fuel price, etc. Uncertainty in the projected variables such as energy demand, volatile fuel price and evolution of renewable technologies will influence the cost of energy when projected over a period of 15–30 years. Inaccurate projected variables could affect energy security and lead to the risk of high energy cost, high emission and low energy security. The energy security is an ability of generation capacity to meet the future energy demand. In order to minimize the risks, a generic methodology is presented to determine an optimal energy mix for a period of around 15 years. The proposed optimal energy mix is a right combination of energy sources that minimize the risk caused due to future uncertainties related to the energy sources. The proposed methodology uses stochastic optimization to address future uncertainties over a planning horizon and minimize the variations in the desired performance criteria such as energy security and costs. The developed methodology is validated using a case study for a South East Asian region with diverse fuel sources consists of wind, solar, geothermal, coal, biomass and natural gas, etc. The derived optimal energy mix decision outperformed the conventional energy planning by remaining stable and feasible against 79% of future energy demand scenarios at the expense of 0–10% increase in the energy cost. Including the nuclear option in the energy mix resulted 26.7% reduction in the total energy cost, 53.2% reduction in the GHG emission and guarantees feasibility against 79% of future energy demand scenarios over a 15 year planning horizon.