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This paper exploits the decomposition structure of the large-scalehydrothermal generation expansion planning problem with an integrated modifiedBenders Decomposition and Progressive Hedging approach. We consider detailedand realistic data from the Brazilian power system to represent hourlychronological constraints based on typical days per month and year. Also, werepresent the multistage stochastic nature of the optimal hydrothermaloperational policy through co-optimized linear decision rules for individualreservoirs. Therefore, we ensure investment decisions compatible with anonanticipative (implementable) operational policy. To solve the large-scaleoptimization problem, we propose an improved Benders Decomposition method withmultiple instances of the master problem, each of which strengthened by primalcuts and new Benders cuts generated by each master's trial solution.Additionally, our new approach allows using Progressive Hedging penalizationterms for accelerating the convergence of the method. We show that our methodis 60\% faster than the benchmark. Finally, the consideration of anonanticipative operational policy can save 7.64\% of the total cost (16.18\%of the investment costs) and significantly improve spot price profiles.

An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem