Expansion planning via decomposition to achieve fully renewable power and freshwater systems

As the reliance on electricity for producing freshwater continues to grow, the development of an expansion planning model that captures the interdependency between power and freshwater systems becomes increasingly important. This paper proposes a two-stage stochastic expansion planning model that represents the interdependence of these systems and accounts for the uncertainties involved. The first stage represents investments for achieving fully renewable power and freshwater systems, while the subsequent stage represents the operation of both systems. The model accounts for both long-term uncertainties, which pertain to growth in power and freshwater demands, and short-term uncertainties, which pertain to the daily fluctuations in freshwater and power demands as well as in renewable production. Due to the complexity of representing the operation of both systems under numerous operating conditions, expansion planning models often become computationally burdensome. To reduce the computational burden, we propose an effective partitioning technique that relies on Benders’ decomposition, dividing the expansion planning problem into a sufficiently small master problem and numerous subproblems. To enhance convergence, we incorporate the operation constraints pertaining to the worst operating condition into the master problem. Numerical experiments underscore the efficacy of utilizing the proposed technique to solve the expansion planning of large-scale systems.