Enable a Carbon Efficient Power Grid via Minimal Uplift Payments

The COVID-19 has slowed down global economic growth. Meanwhile, it also significantly cuts the global carbon emission, which provides a golden opportunity for the whole world to combat the climate change together. While the former policies (e.g., the CAFE standards, renewable portfolio standards, etc.) have reduced certain level of fossil fuel consumption, the most effective measures (such as carbon tax, cap-and-trade programs) are still far from ready for global implementation. This paper investigates an alternative way to achieve a more carbon efficient power grid using the uplift payment scheme.
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Resilience Enhancement With Sequentially Proactive Operation Strategies

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events.
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Towards Optimal Coordination Between Regional Groups: HVDC Supplementary Power Control

With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation. The new challenges arising from the energy transition will require a better coordination between neighboring system operators to maintain system security. To this end, this paper studies the benefit of exchanging primary frequency reserves between asynchronous areas using the Supplementary Power Control (SPC) functionality of High-Voltage Direct-Current (HVDC) lines.
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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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