31 de octubre de 2022
Resumen:
This doctoral dissertation addresses the development of new tools for the optimal expansion planning of power systems considering uncertainty and operational variability. The results of the research developed are presented in five journal papers and an international conference paper. The first paper presents a novel aggregation technique based on the maximum dissimilarity algorithm that allows modeling the operational variability in power systems, paying special attention to the representation of extreme conditions such as the peak demand level of loads. The second paper provides a novel aggregation technique based on chronological time-period clustering that, in addition to modeling long-term dynamics throughout the entire time horizon, assigns different priorities to the input data during the clustering procedure to improve the representation of certain values. The third paper proposes a novel two-stage aggregation procedure and a novel exact acceleration technique for the risk-averse two-stage stochastic generation and transmission network expansion planning problem, where the conditional value-at-risk is used as a risk measure. The fourth paper presents a three-level model for the static two-stage adaptive robust optimization transmission network expansion planning problem considering non-convex operational constraints to model the commitment statuses of conventional generating units and to prevent the simultaneous charging and discharging of storage facilities. The problem is solved using the nested column-and-constraint generation algorithm (NCCGA). The fifth paper extends the work described in the fourth paper by presenting two novel exact acceleration techniques applied to the NCCGA. Finally, the sixth paper extends the work described in the fifth paper by considering a dynamic approach, i.e., investment decisions can be made at different years rather than building new facilities only at the beginning of the planning horizon.
Palabras clave: Expansion planning problems, decision-making under uncertainty, clustering techniques, stochastic programming, robust optimization, two-stage optimization models.
Cita:
A. García-Cerezo (2022), New tools for the optimal expansion planning of power systems considering uncertainty and operational variability. Ciudad Real (España).
