International Conference on Operations Research - OR 2024, Múnich (Alemania). 03-06 septiembre 2024
Resumen:
Energy system optimization models (ESOMs) are useful tools to analyze the decarbonization of our current energy system. However, the underlying input data is subject to uncertainties. Although these uncertainties can substantially impact the structure of the energy system, they are often not taken into account. By applying stochastic programming (SP) the uncertainties can be considered within a single optimization run to achieve risk hedging.
Even without considering uncertainties, ESOMs can become exceedingly large when a high spatial and technological granularity is needed, e.g. for sector coupling. Therefore, acceleration techniques are required to keep the models solvable, especially when taking SP into account. Benders Decomposition (BD) is a method that is typically applied to SP models. Since the stochastic scenarios can be independently optimized within the subproblems, they can be easily parallelized along the stochastic scenario dimension. However, the number of scenarios is rather small in comparison to the number of time steps that are considered in hourly resolved ESOMs.
To further exploit the parallelization potential of stochastic ESOMs, we apply an additional decomposition along the time dimension to two different methods. First, we apply time-splitting to BD in combination with MPI. Second, the performance is compared to the parallel high-performance computing solver PIPS-IPM++. The solver, mainly applied on temporally decomposed ESOMs, has recently been extended to also incorporate stochastic optimization, which enables an additional decomposition along the stochastic scenario dimensión.
Resumen divulgativo:
Los modelos de optimización de sistemas energéticos ayudan a diseñar sistemas energéticos futuros . Aplicamos la descomposición de Benders (BD) a dos métodos: a) consideramos la división temporal en BD en combinación con MPI. b) la comparamos con el optimizador paralelo HPC PIPS-IPM++ descomponiendo a lo largo de los dominios de escenario y tiempo.
Fecha de publicación: 2024-09-03.
Cita:
S. Sasanpour, M. Wetzel, K.K. Cao, A. Ramos, Decomposition of stochastic energy system optimization models – time-splitting in Benders Decomposition vs. PIPS-IPM++, International Conference on Operations Research - OR 2024, Múnich (Alemania). 03-06 septiembre 2024.
