The electricity sector is facing the dual challenge of supporting increasing level of demand electrification while substantially reducing its carbon footprint. Among electricity demands, the energy consumption of cryptocurrency mining data centers has witnessed significant growth worldwide. If well-coordinated, these data centers could be tailor-designed to aggressively absorb the increasing uncertainties of energy supply and, in turn, provide valuable grid-level services in the electricity market. In this paper, we study the impact of integrating new cryptocurrency mining loads into Texas power grid and the potential profit of utilizing demand flexibility from cryptocurrency mining facilities in the electricity market. We investigate different demand response programs available for data centers and quantify the annual profit of cryptocurrency mining units participating in these programs. We perform our simulations using a synthetic 2000 bus ERCOT grid model, along with added cryptocurrency mining loads on top of the real-world demand profiles in Texas. Our preliminary results show that depending on the size and location of these new loads, we observe different impacts on the ERCOT electricity market, where they could increase the electricity prices and incur more fluctuations in a highly non-uniform manner.
Online decision-making in the presence of uncertain future information is abundant in many problem domains. In the critical problem of energy generation scheduling for microgrids, one needs to decide when to switch energy supply between a cheaper local generator with startup cost and the costlier on-demand external grid, considering intermittent renewable generation and fluctuating demands. Without knowledge of future input, competitive online algorithms are appealing as they provide optimality guarantees against the optimal offline solution. In practice, however, future input, e.g., wind generation, is often predictable within a limited time window, and can be exploited to further improve the competitiveness of online algorithms. In this paper, we exploit the structure of information in the prediction window to design a novel prediction-aware online algorithm for energy generation scheduling in microgrids. Our algorithm achieves the best competitive ratio to date for this important problem, which is at most 3, where 𝑤 is the prediction window size. We also characterize a non-trivial lower bound of the competitive ratio and show that the competitive ratio of our algorithm is only 9% away from the lower bound, when a few hours of prediction is available. Simulation results based on real-world traces corroborate our theoretical analysis and highlight the advantage of our new prediction-aware design.
In February 2021, an unprecedented winter storm swept across the U.S., severely affecting the Texas power grid, leading to more than 4.5 million customers' electricity service interruption. This paper assesses the load shedding experienced by customers under realistic scenarios in the actual power grid. It also conducts a preliminary study on using energy storage and load rationing to mitigate rotating blackout's adverse impact on the grid. It is estimated that utility-scale battery storage systems with a total installed capacity of 920 GWh would be required to fully offset the load shedding during the Texas power outage if energy storage were the only technical option. Our simulation result suggests that implementing 20 percent load rationing on the system could potentially reduce this estimated energy storage capacity by 85 percent. This estimate is obtained using the predicted capacity and demand profile from February 15 to February 18, 2021. Recognizing the fact that it would be very challenging to practically deploy energy storage of this size, approaches to provide more granular demand reduction are studied as a means of leveraging the energy storage to maximize the survivability of consumers. Preliminary case study suggests the potential of combining load rationing and proper sizing of energy storage would potentially provide much reliability improvement for the grid under such extreme weather conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.