One of the largest challenges in integrating large quantities of wind power into the grid is the variability in its power output. The fast ramping up or down of its power output can have negative consequences for grid operations, including increased costs, inefficient operation of conventional generators, and the need for additional ancillary services. One method to deal with the variability of power output from individual wind farms is to develop other wind farms that are subject to different wind patterns, essentially diversifying the wind power portfolio. If proper wind power portfolios are chosen, the cumulative power output of the portfolio will have a smoother output relative to the individual wind farms. The goal of this study is to enhance techniques used to optimize diversified wind power systems and to develop methods to quantify the benefits of diverse wind power.
To optimize diversified wind, a modified version of the multi-objective optimization method called Mean-Variance Portfolio optimization (MVP) is used. The modified MVP both minimizes the ramp rate variability and maximizes the average power output of the cumulative wind power output, where ramp rate variability is defined as the percent change of the cumulative power output from time step to time step. Ten-minute interval wind power data from NREL's Eastern Wind Dataset is used in the optimization to develop the set of optimal wind power portfolios. To measure the benefits of diversified wind power, wind portfolios of varying ramp rate variability are added to an economic dispatch model of the Eastern Interconnect of the United States (EIC). The EIC model uses actual generator, load, and transmission capacity data and constraints to deliver electricity at least cost.