Study Data Risk Constrained Optimal Trading Strategies Under Short- and Long-term Uncertainties

This dataset was used to produce the results of following paper:

Title: Risk-constrained Optimal Dynamic Trading Strategies Under Short- and Long-term Uncertainties

Abstract: Recent market changes in power systems with high renewable penetration highlighted the need for complex hedging strategies against price volatility and generation uncertainty. This work proposes a dynamic model to represent sequential decision making in this current scenario. Unlike previously reported works, this method provides a framework for considering uncertainties in both strategic (long-term) and operational (short-term) levels, all of which considered as path-dependent stochastic processes. The problem is modeled as a multistage stochastic programming problem in which the correlations between inflow forecasts, renewable generation, spot and contract prices are accounted for by means of interconnected long- and short-term decision trees. Additionally, risk aversion is considered through intuitive time-consistent constraints. A case study of the Brazilian power sector is presented, in which real data was used to define the optimal trading strategy of a wind power producer, conditioned to the future evolution of market prices. The model provides the trader with useful information such as the optimal contractual amount, settlement timing, and term. Furthermore, the value of this solution is demonstrated when compared to state-of-the-art static approaches using a multistage-based certainty equivalent performance measure.