MHPS ANNOUNCES FIRST BRAZILIAN WIN for JAC GAS TURBINE Power Auction Victory Promises a Change in Power
LAKE MARY, FL – (February 14, 2018) Mitsubishi Hitachi Power Systems (MHPS) celebrated its first win in Brazil for the technical selection of its M501 JAC Gas Turbine for the Vale Azul II Project. The plant, which will have a contracted output of 466MW in combined cycle, was selected in the A-6 Brazilian Electricity Auction held on December 20, 2017.
The Vale Azul II Project, now known by the consortium name Marlim Azul (Blue Marlin), and will be owned by a special purpose entity with shareholding by MHPS, MH Power Systems Representacoes America Latina and Vale Azul Energia Ltda. The project was one of two winners in the Brazil National Agency of Electric Power (Agência Nacional de Energia Elétrica – "ANEEL") CEL Auction # 05/2017 (“A-6 Auction”) on December 20, 2017.
MHPS technology will provide efficient base load power with low variable cost for the 25-year term of the power purchase agreements to be executed as a result of the auction, which will start in 2023. As a result, the Brazilian grid will be bolstered by reliable and efficient power generation and, subject to execution of a gas supply agreement, fueled by domestic natural gas produced by Shell.
Alex Aoki, President & CEO of MHPS South America said, “This project is a major victory for our South American division, in an incredibly competitive market. We used a ground breaking gas turbine technology that will enable the most fuel efficient and affordable power plant in South America. With leading MHPS technology, an experienced team on the ground, and a trusted industry partner like Shell, this project represents a Change in Power for the Brazilian grid.”
The new plant will be MHPS-TOMONI™ enabled. "Tomoni" is a Japanese word that means "traveling together,” signifying the combination of digital and physical products and services. The plant will have a robust digitally-enabled cloud-based information infrastructure and will use data analytics and machine learning to leverage the massive amount of data that are generated during plant operations, driving optimum power plant performance, flexibility and reliability.