A Comprehensive Review of Option Value in Energy Economics

Abstract

This review examines the economic concept of option value for the economic integration of renewables in power systems, considering energy markets' distinctive characteristics. Energy markets exhibit limited storability, price volatility, seasonality patterns, and network constraints that necessitate specialised valuation approaches beyond traditional financial models. We provide a comprehensive analysis of methodologies ranging from conventional financial techniques—Black-Scholes models, binomial trees, Monte Carlo simulation, and partial differential equations—to advanced computational approaches including stochastic optimisation, machine learning, and reinforcement learning. The paper explores how real options theory provides a framework for quantifying the value of flexibility in power systems. Emphasis is placed on smart technologies, whose option value stems from their ability to defer or displace conventional investments under uncertainty. We discuss how stochastic optimisation effectively captures the sequential decision-making characteristic of real options in energy systems. The review highlights emerging applications of machine learning and reinforcement learning for valuing energy derivatives and flexibility options, addressing computational challenges of high-dimensional problems. Finally, we identify promising research directions, including integrated physical-financial modelling frameworks, advanced computational methods, and agent-based approaches for capturing strategic behaviour in evolving energy markets characterised by increasing decarbonisation, digitalisation, and decentralisation.