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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Stochastic Unit Commitment in Isolated Systems With Renewable Penetration Under CVaR Assessment
Isolated regions and islands are facing imported fossil-fuel dependency, higher electricity prices, and vulnerability to climate change. At the same time, they are increasing their renewable penetration and, therefore, risk for electric utilities. Integrating stochastic energy resources in noninterconnected systems may take advantage of an intelligent and optimized risk-averse unit commitment (UC) model. This paper presents a two-stage stochastic UC model with high renewable penetration including reserve requirements for the efficient management of uncertainty.
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Stochastic Generation Capacity Expansion Planning Reducing Greenhouse Gas Emissions
With increasing concerns about greenhouse gas emissions, a least-cost generation capacity expansion model to control carbon dioxide (CO 2) emissions is proposed in this paper. The mathematical model employs a decomposed two-stage stochastic integer program. Realizations of uncertain load and wind are represented by independent and identically distributed (i.i.d.) random samples generated via the Gaussian copula method. Two policies that affect CO 2 emissions directly and indirectly, carbon tax and renewable portfolio standard (RPS), are investigated to assess how much CO 2 emissions are expected to be reduced through those policies.
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Data-Driven Dynamical Control for Bottom-up Energy Internet System
With the increasing concern on climate change and global warming, the reduction of carbon emission becomes an important topic in many aspects of human society. The development of energy Internet (EI) makes it possible to achieve better utilization of distributed renewable energy sources with the power sharing functionality introduced by energy routers (ERs). In this paper, a bottom-up EI architecture is designed, and a novel data-driven dynamical control strategy is proposed.
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