  This paper exploits the decomposition structure of the large-scale
hydrothermal generation expansion planning problem with an integrated modified
Benders Decomposition and Progressive Hedging approach. We consider detailed
and realistic data from the Brazilian power system to represent hourly
chronological constraints based on typical days per month and year. Also, we
represent the multistage stochastic nature of the optimal hydrothermal
operational policy through co-optimized linear decision rules for individual
reservoirs. Therefore, we ensure investment decisions compatible with a
nonanticipative (implementable) operational policy. To solve the large-scale
optimization problem, we propose an improved Benders Decomposition method with
multiple instances of the master problem, each of which strengthened by primal
cuts and new Benders cuts generated by each master's trial solution.
Additionally, our new approach allows using Progressive Hedging penalization
terms for accelerating the convergence of the method. We show that our method
is 60\% faster than the benchmark. Finally, the consideration of a
nonanticipative operational policy can save 7.64\% of the total cost (16.18\%
of the investment costs) and significantly improve spot price profiles.
