A soft robust methodology to devise hedging strategies in renewable energy trading based on electricity options - Brigatto, Arthur; Fanzeres, Bruno (Electric power systems research, 2022)
•Portfolio allocation of electricity contracts under risk- and ambiguity-constraints.•Trading of forward-type contracts, tooling agreements and call/put options.•Uncertainty in energy spot price is considered through an uncertainty set.•An efficient solution approach is provided to handle the proposed multi-level model.•Benefits are illustrated in a numerical study based on the Brazilian power system. Supply contracts play an important role in competitive electricity markets. They help ensure supply adequacy and hedge against market power, among other benefits. However, when variable renewable energy is involved, the mixture of uncertainty in energy production with a high volatile spot market poses a risk difficult to manage. Within this context, call/put options are suitable hedging instruments to cover production imbalance with respect to supply contracts. To efficiently balance the cost/benefit of a set of energy options in a portfolio composed by renewable sources short in supply contracts, a precise definition of the probabilistic nature of the spot market price should be available. However, it is recognized that in practice agents only have an imprecise approximation of the “true” underlying process. Thus, decisions are made under ambiguity, which challenges the practical application of standard pure stochastic-programming methods for the optimal composition of electricity contracts. In this work, we propose a risk- and ambiguity-constrained portfolio allocation model that defines the optimal composition of call/put options and renewable sources to back a supply contract. A case study with realistic data from the Brazilian electricity market is presented to illustrate the applicability of the proposed portfolio allocation methodology. Comparing with two benchmark, we found an increase of roughly 20% and 42% on the average revenue and at least 40% on the average of the 5% worst-case revenue scenarios for different spot price probability distributions.