Strengthening Transmission System Resilience Against Extreme Weather Events by Undergrounding Selected Lines
Natural disasters, such as extreme weather events (EWEs), can cause significant damage to power systems. In fact, it is expected that the intensity and frequency of EWEs will increase the next years due to climate change, making power system resilience enhancement necessary. This paper proposes a transmission resilience planning solution by determining the lines to be placed underground in order to minimize load shedding in the most cost-efficient way taking into account historical EWEs (HEWEs).
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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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Two-Scale Stochastic Control for Integrated Multipoint Communication Systems With Renewables
Increasing threats of global warming and climate changes call for an energy-efficient and sustainable design of future wireless communication systems. To this end, a novel two-scale stochastic control framework is put forth for smart-grid powered coordinated multi-point (CoMP) systems. Taking into account renewable energy sources, dynamic pricing, two-way energy trading facilities, and imperfect energy storage devices, the energy management task is formulated as an infinite-horizon optimization problem minimizing the time-averaged energy transaction cost, subject to the users’ quality of service requirements.
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Robust Energy and Reserve Dispatch Under Variable Renewable Generation
Global warming and environmental pollution concerns have promoted dramatic integrations of renewable energy sources all over the world. Associated with benefits of environmental conservation, essentially uncertain and variable characteristics of such energy resources significantly challenge the operation of power systems. In order to implement reliable and economical operations, a robust energy and reserve dispatch (RERD) model is proposed in this paper, in which the operating decisions are divided into pre-dispatch and re-dispatch.
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On the Trade-Off Between Environmental and Economic Objectives in Community Energy Storage Operational Optimization
The need to limit climate change has led to policies that aim for the reduction of greenhouse gas emissions. Often, a trade-off exists between reducing emissions and associated costs. In this article, a multi-objective optimization framework is proposed to determine this trade-off when operating a Community Energy Storage (CES) system in a neighborhood with high shares of photovoltaic (PV) electricity generation capacity.
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