Forecast-informed operation of transboundary water-energy systems: a case study in the lower Mekong River Basin

The ASEAN economic growth is one of the main factors driving the development of hydropower dams in the lower Mekong River Basin. Recent studies show that the performance of these infrastructures is uncertain and largely affected by both seasonal and inter-annual water availability. During El Niño years, for example, weaker monsoon rainfalls reduce the amount of available hydropower, which must be offset by a deeper reliance on fossil fuels. A potential solution to this problem stands in the idea of informing hydropower operations with seasonal hydro-meteorological forecasts. Here, we explore the value of forecasts through a computational framework consisting of three components. First, we use principal-component regression to predict seasonal cumulated precipitation at multiple sites within the Mekong basin. Second, we harness the information contained in the forecasts to optimize both firm and annual hydropower production of each dam; a result attained by coupling the Variable Infiltration Capacity hydrologic model with a Multi-Objective Evolutionary Algorithm. Third, we use the power system model PowNet to simulate the energy mix of Thailand and Laos, and thereby evaluate the forecast value in terms of reduced CO₂ emissions and energy production costs. Modelling results for the period 1995-2004 show that the use of seasonal forecasts reduces annual operating costs and CO₂ emissions by at least 10 million USD and 20 million tons, respectively.