326 research outputs found
Deep Reinforcement Learning for Wind and Energy Storage Coordination in Wholesale Energy and Ancillary Service Markets
Global power systems are increasingly reliant on wind energy as a mitigation
strategy for climate change. However, the variability of wind energy causes
system reliability to erode, resulting in the wind being curtailed and,
ultimately, leading to substantial economic losses for wind farm owners. Wind
curtailment can be reduced using battery energy storage systems (BESS) that
serve as onsite backup sources. Yet, this auxiliary role may significantly
hamper the BESS's capacity to generate revenues from the electricity market,
particularly in conducting energy arbitrage in the Spot market and providing
frequency control ancillary services (FCAS) in the FCAS markets. Ideal BESS
scheduling should effectively balance the BESS's role in absorbing onsite wind
curtailment and trading in the electricity market, but it is difficult in
practice because of the underlying coordination complexity and the stochastic
nature of energy prices and wind generation. In this study, we investigate the
bidding strategy of a wind-battery system co-located and participating
simultaneously in both the Spot and Regulation FCAS markets. We propose a deep
reinforcement learning (DRL)-based approach that decouples the market
participation of the wind-battery system into two related Markov decision
processes for each facility, enabling the BESS to absorb onsite wind
curtailment while simultaneously bidding in the wholesale Spot and FCAS markets
to maximize overall operational revenues. Using realistic wind farm data, we
validated the coordinated bidding strategy for the wind-battery system and find
that our strategy generates significantly higher revenue and responds better to
wind curtailment compared to an optimization-based benchmark. Our results show
that joint-market bidding can significantly improve the financial performance
of wind-battery systems compared to individual market participation
Optimal Energy Storage Scheduling for Wind Curtailment Reduction and Energy Arbitrage: A Deep Reinforcement Learning Approach
Wind energy has been rapidly gaining popularity as a means for combating
climate change. However, the variable nature of wind generation can undermine
system reliability and lead to wind curtailment, causing substantial economic
losses to wind power producers. Battery energy storage systems (BESS) that
serve as onsite backup sources are among the solutions to mitigate wind
curtailment. However, such an auxiliary role of the BESS might severely weaken
its economic viability. This paper addresses the issue by proposing joint wind
curtailment reduction and energy arbitrage for the BESS. We decouple the market
participation of the co-located wind-battery system and develop a joint-bidding
framework for the wind farm and BESS. It is challenging to optimize the
joint-bidding because of the stochasticity of energy prices and wind
generation. Therefore, we leverage deep reinforcement learning to maximize the
overall revenue from the spot market while unlocking the BESS's potential in
concurrently reducing wind curtailment and conducting energy arbitrage. We
validate the proposed strategy using realistic wind farm data and demonstrate
that our joint-bidding strategy responds better to wind curtailment and
generates higher revenues than the optimization-based benchmark. Our
simulations also reveal that the extra wind generation used to be curtailed can
be an effective power source to charge the BESS, resulting in additional
financial returns.Comment: 2023 IEEE Power & Energy Society General Meeting (PESGM). arXiv admin
note: text overlap with arXiv:2212.1336
Evaluating Casing Damage Basing on Fuzzy Comprehensive Evaluation and Grey Relational Grade Analysis
Casing damage is one of the main factors influencing oil production, and determined the main factors which lead to casing damage is the premise to develop effective prevention and control measures of casing damage. The relationship between various factors of casing damage is complicated, and it is difficult to determine the main factors influenced the casing damage applying for conventional theoretical analysis and quantitative calculation. In this paper, the main factors influenced casing damage is evaluated by the method of combination fuzzy comprehensive evaluation and grey relational grade analysis. Firstly, this article analyzed factors causing casing damage, and then evaluated 22 wells of Daqing oilfield which is located in the west block of the Southern District fault. Comparing the evaluation and the actual results, the accuracy rate of this model is 86.3%, and showing that the evaluation results are accurate and reliable.Key words: Casing damage; Fuzzy comprehensive evaluation; Grey relational grade; Effect evaluatio
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