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This study investigates the renewable energy adoption across regions covered by Independent System Operators (ISOs) in the U.S. The study employed a deterministic model in the form of Data Envelopment Analysis (DEA) to determine the performance of ten ISO regions over a five-year period from 2013 to 2017. Inputs into the model include the Renewable Portfolio Standard (RPS) targets, fossil fuel capacity additions and the costs of capacity additions. Outputs from the model include renewable energy capacity additions and CO2 emissions per MWh of generated electricity. The results show the regions covered by CAISO, ERCOT, NE-ISO, SPP and the NON-ISO to be on the efficient frontier. For the regions not on the efficient frontier, the results identify their limitations and provide projections both for reductions in excess inputs and improvements in outputs to be on the efficient frontier. For example, we see that the regions covered by NY-ISO and PJM would require, on average, renewable energy capacity expansions of 593.65MW and 230.24MW, respectively, to be on the efficient frontier. These regions would require their average fossil capacity expansions to be limited to 234.83MW and 365.4MW respectively. These findings offer some guidance on approaches to improving the performance of these markets. 

The growth of renewable energy technologies creates significant challenges for the stability of the system because of their intermittency. Nonetheless, we can value these technologies with storage systems. We model the supply by a renewable technology, wind, into a storage facility using the leaky bucket mechanism. The bucket is synonymous with storage while the leakage is equivalent to meeting load. Modelica is used to capture: (i) the time-dependence of the state of the bucket based on a physical model of storage; (ii) the stochastic representation of wind energy using wind speed data that is fed into a physical model of a wind technology; and (iii) the load, modeled as a resistor-inductor circuit. The strength of Modelica in using non-causal equations for basic sub-systems that are linked together is harnessed through its libraries. We find that there is a diminishing return to storage. Beyond a certain level of storage, the integration of a reliable baseload power supply is required to diminish the risk due to reduced reliability. The need for storage systems as a hedge against intermittency is dependent on the interplay between the supply volatilities and the stochastic load to guarantee an acceptable level of quality of service and reliability.