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Distributed Generation Systems Planning & the Ecosystem of Electricity Technological Learning Open Access

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Across the world, energy policies are promoting distributed generation systems in order to (1)improve the resilience of the energy infrastructure, (2) to mitigate the incidence of CO2 inducedclimate change, and (3) to improve cost effectiveness. When these three objectives are combined,a strong case is made for understanding how energy technologies have evolved. Planning fordistributed generation systems is complicated by demand and supply volatilities because ofthe increase in the scale of renewable, intermittent technologies. Therefore, it is imperative tounpack the mechanism of how these technologies have evolved within the electricity ecosystem.As a result, this research seeks to address two critical issues:(1) How would modern distributed generation systems meet increasingly fluctuating demands while adequately utilizing more renewable, but intermittentenergies?To address this question, we modify and apply a stochastic optimization framework thatcaptures the randomness of supply and demand, and the effects of policy, simultaneously.Specifically, we modify the Benders' decomposition model to explore the optimal schedulingof supply from different technology units in a microgrid system.(2) Given the growth of distributed generation systems, the question becomes:What can we learn from the ecosystem of energy technologies evolution toguide future investments decisions?To answer this question, we effectuate a longitudinal study of 5,573 U.S. electricity firmsoperating from 1998 through 2010. We gauge the strains created by regulatory policiesthrough their susceptibility to induce uncertainties. We consecutively explain the transferof learning and how organizations learn from the experience of others or from their ownexperience. We link these two frameworks to another model so as to capture the knowledgeacquired through production and technological change.

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