Optimal hierarchical modeling of power to X stations through a chance constrained Two-Stage stochastic programming

Abstract

The carbon neutrality policy aiming for net zero carbon emissions has led to a significant increase in the use of renewable energy sources (RES) globally. However, due to their uncertain nature, RES can cause imbalances in power demand. Recently, Power-to-X (P2X) station technology has gained attention as a solution to the uncertainties of RES and as a means to enhance the capacity and efficiency of RES operations. P2X stations can be utilized when power demand imbalances occur due to the uncertain output of RES or when the power system cannot accommodate the power supply from RES due to various stability issues. Specifically, when supply disruptions occur in the power system due to RES uncertainties, P2X stations contribute to preventing RES curtailment by supplying power to electric vehicle (EV) fuel sources, producing heat using electric heat pumps (EHP), or producing hydrogen using electrolyzers (ELZ), thus improving the uncertain financial benefits for independent power producers (IPP). This paper proposes a mixed-integer linear programming (MILP) based chance-constrained two-stage stochastic optimization (CCTS) approach to address imbalances in power demand from RES and to enhance the profitability of IPP by finding the optimal planning and operational solutions for P2X stations. The proposed method provides hierarchical level results, demonstrating that economic benefits can increase by up to 60.2% with the application of P2X stations and that curtailed energy from RES can be reduced by up to 76.5%. The proposed methodology is also validated for its superior performance by being compared with both the non-linear stochastic chance constraint method and the stochastic method.

Keywords

• Wind farm
• Power to gas
• Distributionally robust optimization
• Ambiguity set
• Mixed-integer linear programming