Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies, such as energy storage, can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. In this paper, we propose a novel, efficient, and highly generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders.
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From Reliability to Resilience: Planning the Grid Against the Extremes

Although extreme events, mainly natural disasters and climate change-driven severe weather, are the result of naturally occurring processes, power system planners, regulators, and policy makers do not usually recognize them within network reliability standards. Instead, planners have historically designed the electric power infrastructure accounting for the so-called credible (or “average”) outages that usually represent single or (some kind of) simultaneous faults (e.g., faults on double circuits).
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Toward Fully Renewable Electric Energy Systems

Renewable energy sources are here to stay for a number of important reasons, including global warming and the depletion of fossil fuels. We explore in this paper how a thermal-dominated electric energy system can be transformed into a renewable-dominated one. This study relies on a stochastic programming model that allows representing the uncertain parameters plaguing such long-term planning exercise. Being the final year of our analysis 2050, we represent the transition from today to 2050 by allowing investment in both production and transmission facilities, with the target of achieving a renewable-dominated minimum-cost system. The methodology developed is illustrated using a realistic large-scale case study. Finally, policy conclusions are drawn.
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Decomposition Strategy for Districts as Renewable Energy Hubs

In light of the energy transition, it becomes a widespread solution to decentralize and to decarbonize energy systems. However, limited transformer capacities are a hurdle for large-scale integration of solar energy in the electricity grid. The aim of this paper is to define a novel concept of renewable energy hubs and to optimize its design strategy at the district scale in an appropriate computational time. To overcome runtime issues, the Dantzig–Wolfe decomposition method is applied to a mixed-integer linear programming framework of the renewable energy hub.Distributed energy units as well as centralized district units are considered.
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