A Light Robust Optimization Approach for Uncertainty-based Day-ahead Electricity Markets

Abstract

The traditional deterministic day-ahead (DA) market clearing does not accommodate the uncertainty from variable renewable energy sources, resulting in an increasing activation of expensive reserves and curtailment events. Robust optimization (RO) has been proposed to mitigate this uncertainty. However, as RO considers worst-case scenarios, it results in highly conservative solutions. This paper proposes a light robust (LR) DA market clearing mechanism to address these shortcomings, controlling the trade-off between robustness and economic efficiency. This mechanism integrates the uncertainty from renewables in its formulation and allows the derivation of coherent market prices. The optimal bidding strategy of the stochastic participants is mathematically derived, while considering the expectation on the system imbalance. A comparison with the deterministic formulation proves that stochastic producers can economically benefit from the proposed mechanism, encouraging their participation. The derived analytical results are corroborated by numerical results from a case study based on the IEEE 24-node test system.