How to plan electricity expansion in hydro-dependent systems?

In many countries, hydropower provides a dominant share of the electricity supply. This dependence brings environmental benefits and competitive costs, but it also creates a vulnerability: when droughts occur or rainfall patterns change, the available energy decreases, and system security is put to the test. In this context, planning investments based only on historical averages is no longer sufficient. The practical question is how to decide, years in advance, what to build and what to reinforce when the key resource, water, can behave in very different ways.

This research proposes an integrated framework for planning generation and transmission expansion in hydro-dependent systems under hydrological uncertainty. The objective is to support decisions that balance cost, supply adequacy, and operational flexibility. The central idea is to explicitly represent water uncertainty and incorporate it into the planning model, so that the resulting plan does not depend on a single “typical” hydrological year.

The end of the energy crisis: turning uncertainty into decisions

The framework quantifies hydrological uncertainty through Monte Carlo simulations calibrated with relationships among precipitation, inflows, and generation. These simulations are used to construct monthly hydropower availability factors, which describe how much the hydropower fleet can generate under different hydrological states. This signal, which comes from climate and hydrological behavior, is directly integrated into the expansion model, avoiding the treatment of water resources as a fixed input.

Based on this information, a multi-stage optimization model formulated as a mixed-integer nonlinear programming problem, MINLP, is solved. The model makes both investment and operational decisions with monthly resolution over the planning horizon. To demonstrate the approach, a 24-bus equivalent of the Ecuadorian power system is used, where hydropower accounts for more than 70 percent of electricity generation. The horizon covers 2025 to 2040, and three expansion pathways are evaluated across 19,200 simulations. The three pathways represent realistic transition alternatives: a business-as-usual trajectory, BAU; a partial renewable expansion, RGE; and a full renewable expansion, FRE. This framework allows comparing plans under wet, normal, and dry conditions, rather than only under an average scenario.

Increasing capacity with the existing grid: repowering and reservoir management to reduce risk

In addition to deciding on new generation capacity, the framework integrates transmission decisions, and particularly the upgrading of conductors on existing lines, known as reconductoring. This alternative increases transfer capacity along already built corridors and can relieve network constraints without relying exclusively on new transmission lines. Its value is straightforward: even when energy is available, transmission bottlenecks may prevent it from reaching demand centers. In parallel, the model incorporates reservoir operation. This allows the hydropower contribution to be represented more realistically, especially during dry months.

The results show that reconductoring improves grid capacity, but its impact on total system cost is marginal compared with the investment required to incorporate new generation. Among the scenarios evaluated, the full renewable expansion strategy, FRE, is the most cost-effective. This pathway achieves the lowest total system cost and the lowest unserved power, PNS, over the planning horizon, while retaining existing thermal capacity as monthly backup.

The analysis also highlights a public policy issue. Under the assumption of a low carbon price, the FRE scenario may register higher CO emissions than the BAU business-as-usual pathway or the partial renewable expansion scenario, RGE. This occurs because legacy thermal units remain competitive for serving residual demand and supplying energy during dry months. The result suggests that, if the goal is to align supply adequacy with deeper decarbonization, stronger carbon-related signals are needed to influence both operation and investment decisions.