Risk-Calibrated conventional-renewable generation mix using master-slave portfolio approach guided by flexible investor preferencing. (15th April 2022)

• Record Type: Journal Article
• Authors: M, Jisma; Mohan, Vivek; Thomas, Mini Shaji; Madhu M, Nimal
• Is Part Of: Energy. Volume 245(2022)
• Publication Date: 2022-04-15
• Subjects: Renewable energy -- Risk -- Portfolio -- Cost -- Multi-criteria decision making; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123261
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes a master-slave approach to quantify, combine and balance energy-risk and cost-risk involved in a generation portfolio with renewable and fuel-based technologies. Subjective preferences of the investor on risk, cost and emission are traded-off using pareto-optimization and quantified using multi-criteria decision-making techniques. Temporal variations in renewable energy production led to 'energy-risk' (kWh), characterized by energy-return-risk Efficient Frontier (EF). The uncertainty in the energy production cost of the fuel-based sources (FBS) results in 'cost-risk' ($/kWh), represented by cost-risk EF. These efficient frontiers are combined using the concepts of Sharpe ratio and tangency portfolio. The master portfolio (MP) gives a percentage share for the total renewable and total conventional generation. The slave portfolio (SP) assigns internal weights within the renewable (solar and wind) and within the fuel-based (coal, natural gas, and oil) generations. The best solution is selected from the pareto-front by calibrating the investor preferences using Analytic Hierarchy Process (AHP) and then verified using Elimination and Choice Translating Reality (ELECTRE). It is understood that a completely customizable multi-criteria portfolio selection can be achieved by incorporating subjective views of the investors, eliminating one-sided energy portfolios. Highlights: Master-slave portfolio with renewable and fuel-based energy sources is built. Risk-return features of multiple efficient frontiers are extracted and combined. Sharpe and tangency portfolio concepts are applied for slave portfolio selection. Cost, risk, and emission are pareto-optimized. Investor's views for portfolio selection are incorporated using AHP.