CF3I Gas Mixtures: Breakdown Characteristics and Potential for Electrical Insulation
SF6 is a potent greenhouse gas, and there has been research into more environmental friendly alternative gases with the aim of replacing the use of SF6 gas in high-voltage equipment. So far, the research into alternative gases has shown that CF3 I gas mixtures have promising dielectric properties comparable to those of SF6 . This paper provides an overview of research into CF3 I gas and its mixtures, and gives an insight into its key properties. These include laboratory tests on the gas mixtures and initial applications to electrical power equipment.
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Nonlinear Performance Degradation Prediction of Proton Exchange Membrane Fuel Cells Using Relevance Vector Machine
Environmental issues, especially global warming due to the greenhouse effect, have become more and more critical in recent decades. As one potential candidate among different alternative “green energy” solutions for sustainable development, the proton exchange membrane fuel cell (PEMFC) has received extensive research attention for many years for energy and transportation applications. In this paper, an advanced self-adaptive relevance vector machine (RVM) has been developed and demonstrated to predict the performance degradation of PEMFCs.
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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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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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The Green Impact: How Renewable Sources Are Changing EU Electricity Prices
The European Union (EU) energy policy focuses on achieving a balance between three main pillars: increase the security of supply, reduce the impact of climate change, and improve economic competitiveness. To accomplish these objectives, the EU has been creating competitive conditions that internalize environmental externalities, and it has also actively promoted renewable energy.
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Conditional Kernel Density Estimation Considering Autocorrelation for Renewable Energy Probabilistic Modeling
Renewable energy is essential for energy security and global warming mitigation. However, renewable power generation is uncertain due to volatile weather conditions and complex equipment operations. It is therefore important to understand and characterize the uncertainty in renewable power generation to improve operational efficiency. In this paper, we proposed a novel conditional density estimation method to model the distribution of power generation under various weather conditions. Compared with existing literature, our approach is especially useful for the purpose of short-term modeling, where the temporal dependence plays a more significant role.
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An Innovative Coalitional Trading Model for a Biomass Power Plant Paired With Green Energy Resources
The role of biomass resources to diminish the dependency on fossil fuels is steadily increasing worldwide. More importantly, governments set goals to boost the share of renewable energy resources in the power sector to face up to global warming issues. In this paper, a coalitional game model for the trading of a Biomass Power Plant (BPP) paired with a concentrating solar power facility and a wind park is proposed.
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Resilience Assessment of Distribution Systems Integrated With Distributed Energy Resources
The resilience of electric systems is receiving growing attention due to their increased vulnerability to infrastructure damages and widespread outages from frequent extreme climactic conditions attributed to global warming effects. Resilience evaluation methods should recognize the uncertainties and correlations in the performance variations of different types of energy resources, load characteristics, extreme events and their impacts on the grid elements.
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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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