Co-optimized trading strategy of a renewable energy aggregator in electricity and green certificates markets

As the presence of renewable energy sources in the energy and derivatives markets grows, the need to develop comprehensive mathematical frameworks to study their dominant position in the market becomes greater. This work provides a bi-level mathematical model to derive the optimal trading strategies for a strategic renewable energy aggregator, exerting market power in a joint auction-based electricity and green certificates market. The upper-level problem aims at maximizing strategic player's expected profits, while the two lower-level problems represent the sequential day-ahead electricity and green certificates market clearing mechanism, respectively. The bi-level model is initially reformulated into a mathematical program with equilibrium constraints (MPEC), employing the Karush-Kuhn-Tucker conditions and strong duality theory, and is further recast into a mixed integer linear program (MILP). The proposed approach is first applied to a Pennsylvania-New Jersey-Maryland (PJM) 5-bus transmission constrained power network and then to a modified IEEE 24-bus system to illustrate its computational efficiency and applicability in a real-life case study. Numerical simulations determine electricity and green certificates clearing prices and optimal trading plan for the strategic renewable aggregator in order to maximize its expected profits, under plausible power network congestion.