Risk-Calibrated conventional-renewable generation mix using master-slave portfolio approach guided by flexible investor preferencing

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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

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. It uses multi-criteria decision-making techniques to trade-off the subjective preferences of the investor on risk, cost and emission. By combining and optimizing the efficient frontiers of energy-risk and cost-risk, the best solution is selected from the pareto-front.