Demand Response Contracts as Real Options: A Probabilistic Evaluation Framework Under Short-Term and Long-Term Uncertainties

Abstract

This paper aims to set up a probabilistic framework to assess the value of a portfolio of demand response (DR) customers under both operational (short-term) and planning (long-term) uncertainties through real options (ROs) modeling borrowed from financial theory. In an operational setting, DR is considered as an RO contract allowing an aggregator seeking to maximize its revenue to sell flexible demand in the day-ahead market and balance its energy portfolio in the balancing markets. Sequential Monte Carlo simulations (SMCS) are used to value DR activation decisions based on market price evolutions. These decisions combine DR physical characteristics and portfolio scheduling optimization, whereby the aggregator chooses to exercise only the contracts probabilistically leading to a profit, also considering the physical payback effects of load recovery. Sensitivity of profits to changing market conditions and payback characteristics is also assessed. In an investment setting, subject to long-term uncertainties, the value of an investment in DR-enabling technology is quantified through the Datar–Mathews RO approach that applies hybrid SMCS and scenario analysis. The results show how the flexibility value of DR can be highlighted by modeling it as RO, particularly in high volatile markets, and how realistic inclusion of payback characteristics significantly decrease the benefits estimated for DR. In addition, the proposed RO framework generally allows hedging of the risks incurred under long-term and short-term uncertainties.