Adaptive robust self-scheduling for a wind-based GenCo equipped with power-to-gas system and gas turbine to participate in electricity and natural gas markets

Abstract

Integrating renewable energy, particularly wind generation, into power systems brings significant uncertainty and intermittency, challenging generation companies (GenCos) to maintain economic operations. This paper proposes a strategy for a wind-based GenCo equipped with a gas turbine and a power-to-gas (PtG) system to balance wind variability. The gas turbine offsets power shortfalls by consuming natural gas, while the PtG system absorbs excess wind energy, converting it to natural gas and injecting it into the natural gas network as a form of storage. The GenCo operates in day-ahead electricity and natural gas markets and the real-time electricity market, addressing uncertainties from wind output, energy prices, and natural gas access. A tri-level adaptive robust optimization model is introduced for the GenCo’s short-term operational scheduling. At the first level, decisions related to the GenCo's participation in the day-ahead electricity and natural gas markets are made. The second level deals with determining the worst-case realization of the uncertainties, constrained by the budget of uncertainty and the limited range of variations of uncertain variables. The third level sets the operational strategy on the actual day, considering power and gas balance, as well as constraints for the wind farm, gas turbine, and PtG unit. A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model’s effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. This approach supports the broader integration of renewable energy into power systems.