A Preliminary Study on the Role of Energy Storage and Load Rationing in Mitigating the Impact of the 2021 Texas Power Outage

Menati, Ali; Xie, Le

doi:10.1109/naps52732.2021.9654452

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The Texas power system operates independently of the national interconnected power grid and places a greater emphasis on market and competition mechanisms. During this winter storm event, the market mechanism led to drastic fluctuations in electricity prices, causing some generators to be uneconomical to operate or unable to provide power in the short term [17,18].…”

Section: The "215" Blackout In Texas Usamentioning

confidence: 99%

Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration

Song,

Zhou,

Zhou

et al. 2023

Energies

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With the power industry moving toward a green and low-carbon direction, renewable energy is occupying an increasingly larger share in the power system. However, compared with traditional thermal power generation, the instability of new energy generation is very prominent, which also leads to a decrease in the inertia of the power system after the grid integration of a high proportion of renewable energy. If no measures are taken, this may lead to frequency collapse accidents. Therefore, this paper first introduces two international major power outage accidents that have occurred in recent years, analyzing the causes, and then summarizes the inspiration obtained from the accidents. Subsequently, some research results on low inertia-related issues in the power system caused by the high proportion of new energy grid integration in recent years were summarized and analyzed from three aspects: inertia evaluation methods, optimal operation measures for the power system, and under frequency load-shedding (the abbreviation “ULFS” in the following text stands for it) schemes. Finally, suggestions were made for future research directions.

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Section: The "215" Blackout In Texas Usamentioning

confidence: 99%

Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration

Song,

Zhou,

Zhou

et al. 2023

Energies

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“…In this paper, we do not consider using energy storage in the generation scheduling problem. The reason is that for the typical size of microgrids, e.g., a college campus, existing energy storage systems are rather expensive and not widely available [36].…”

Section: System Modelmentioning

confidence: 99%

Competitive prediction-aware online algorithms for energy generation scheduling in microgrids

Menati

Chau

Chen

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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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.

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Section: System Modelmentioning

confidence: 99%

Competitive Prediction-Aware Online Algorithms for Energy Generation Scheduling in Microgrids

Menati¹,

Chau²,

Chen³

2022

Preprint

Self Cite

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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, 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.

show abstract

A Preliminary Study on the Role of Energy Storage and Load Rationing in Mitigating the Impact of the 2021 Texas Power Outage

Cited by 8 publications

References 5 publications

Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration

Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration

Competitive prediction-aware online algorithms for energy generation scheduling in microgrids

Competitive Prediction-Aware Online Algorithms for Energy Generation Scheduling in Microgrids

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