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The current energy infrastructure heavily depends on fossil energy, which will be mostly depleted beyond 21st century. Another built-in disadvantage of fossil energy is the pollutant and green house gas emission. It is time to reform the environment-degrading energy infrastructure into a sustainable and resilient energy infrastructure such that it is more environmental friendly. Compared with fossil energy, it is expensive to transport renewable energy for a long distance. Another problem of renewable energy is fluctuation and it is not so stable as fossil energy. To solve the two bottleneck energy investment planning problems (transmission and fluctuation) of renewable energy development, we propose a long-term investment planning model that can help analysts, investors and policy makers find out how to take full use of current and emerging technologies to support the development of renewable energy so that our energy infrastructure can be reformed to be cleaner in a longterm period, e.g. 40 years. In this model, we propose and implement a parallel planning method for power systems. In this method, a large region that needs to be planned is partitioned into multiple subregions. Each subregion is modeled as two optimization models. One is an hour-level model with the goal to minimize the power price volatility caused by imbalance of power demand and supply and the CO2 emission at hour level. Another is a year-level model with the goal to minimize the investment cost of transmission, operation, and fossil/clean power capacity expansion at year-level. The year-level model also needs to satisfy the RPS (Renewable Portfolio Standard, which has been approved by 27 states and D.C.) requirements because it is a year-level policy. We use an energy storage system to store surplus clean power e.g. wind power and this helps solve the fluctuation problem of wind energy. The stored energy is allowed to be traded among neighbouring subregions. All models are linear or mixed integ- - er linear programming models and need to satisfy the constraints about fossil/clean power capacity expansion and available clean energy. We use Midwest area and wind energy as an example and implement the parallel modeling method in a cluster system, which supports parallel computing. According to our best knowledge, this is the first parallel long-term energy investment planning model for exploring the relationships between public policy (RPS), renewable energy and fossil energy. It can be used to solve large-scale planning problems on supercomputers.