Stochastic Optimal Planning for Renewable energy s.. (SOPRIS)
Stochastic Optimal Planning for Renewable energy sources Integration in power Systems
Start date: May 15, 2015,
End date: May 14, 2018
"Environmental concerns and the pursuit of sustainable energy sources have resulted in policy measures toward high shares of renewable generation. However, Renewable Energy Sources (RES), such as wind and photovoltaic power generation, are non-dispatchable, variable and uncertain. Traditionally, conventional generators provide balancing reserves. The increasing share of RES results in an increasing amount of required reserves, and hence it may have an opposite effect both from an environmental and economic point of view. The latter raises the need of cheaper and environmentally friendlier reserves providers. Demand response and storage resources could be utilized to offer ancillary services including reserve provision. However, these technologies include uncertainty, mainly introduced due to human behavior and weather conditions, rendering their successful exploitation challenging.Making optimal scheduling decisions in the presence of uncertainty is a challenging problem. The core in such mechanism to assess the trade-off between optimality and reliability. The SOPRIS project aims to address this problem by providing algorithms and tools for optimal decision making in power systems with uncertainty and provide performance guarantees for the system reliability. Specifically, we propose using stochastic and optimization based techniques to address the problem of unit commitment, reserve provision and energy scheduling both on the generation and the demand side, while taking the network and reliability constraints into account. The SOPRIS project proposes additional corrective control schemes that exploit the controllability not only of the demand side but also of other network components that may not provide reserve capacity but their set-point can be modulated in a post-disturbance situation thus leading to lower operating costs. Emphasis will be also given on the tractability of the developed algorithms to facilitate their application on large scale systems."
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