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

Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets

Exposing new scalars hiding behind the Higgs boson

It is possible that there is another scalar hiding behind the known 125 GeV Higgs boson. If the hidden scalar exhibits a CP property different from the Higgs boson, it can be exposed in the di-Higgs production at the high-luminosity large hadron collider and future colliders.Comment: 6 pages, 3 figure

Evaluation of a novel saliva-based epidermal growth factor receptor mutation detection for lung cancer: A pilot study.

BackgroundThis article describes a pilot study evaluating a novel liquid biopsy system for non-small cell lung cancer (NSCLC) patients. The electric field-induced release and measurement (EFIRM) method utilizes an electrochemical biosensor for detecting oncogenic mutations in biofluids.MethodsSaliva and plasma of 17 patients were collected from three cancer centers prior to and after surgical resection. The EFIRM method was then applied to the collected samples to assay for exon 19 deletion and p.L858 mutations. EFIRM results were compared with cobas results of exon 19 deletion and p.L858 mutation detection in cancer tissues.ResultsThe EFIRM method was found to detect exon 19 deletion with an area under the curve (AUC) of 1.0 in both saliva and plasma samples in lung cancer patients. For L858R mutation detection, the AUC of saliva was 1.0, while the AUC of plasma was 0.98. Strong correlations were also found between presurgery and post-surgery samples for both saliva (0.86 for exon 19 and 0.98 for L858R) and plasma (0.73 for exon 19 and 0.94 for L858R).ConclusionOur study demonstrates the feasibility of utilizing EFIRM to rapidly, non-invasively, and conveniently detect epidermal growth factor receptor mutations in the saliva of patients with NSCLC, with results corresponding perfectly with the results of cobas tissue genotyping

Target SSR-Seq: A Novel SSR Genotyping Technology Associate With Perfect SSRs in Genetic Analysis of Cucumber Varieties

Simple sequence repeats (SSR) – also known as microsatellites – have been used extensively in genetic analysis, fine mapping, quantitative trait locus (QTL) mapping, as well as marker-assisted selection (MAS) breeding and other techniques. Despite a plethora of studies reporting that perfect SSRs with stable motifs and flanking sequences are more efficient for genetic research, the lack of a high throughput technology for SSR genotyping has limited their use as genetic targets in many crops. In this study, we developed a technology called Target SSR-seq that combined the multiplexed amplification of perfect SSRs with high throughput sequencing. This method can genotype plenty of SSR loci in hundreds of samples with highly accurate results, due to the substantial coverage afforded by high throughput sequencing. We also detected 844 perfect SSRs based on 182 resequencing datasets in cucumber, of which 91 SSRs were selected for Target SSR-seq. Finally, 122 SSRs, including 31 SSRs for varieties identification, were used to genotype 382 key cucumber varieties readily available in Chinese markets using our Target SSR-seq method. Libraries of PCR products were constructed and then sequenced on the Illumina HiSeq X Ten platform. Bioinformatics analysis revealed that 111 filtered SSRs were accurately genotyped with an average coverage of 1289× at an extremely low cost; furthermore, 398 alleles were observed in 382 cucumber cultivars. Genetic analysis identified four populations: northern China type, southern China type, European type, and Xishuangbanna type. Moreover, we acquired a set of 16 core SSRs for the identification of 382 cucumber varieties, of which 42 were isolated as backbone cucumber varieties. This study demonstrated that Target SSR-seq is a novel and efficient method for genetic research

Recent Progress in Detecting Enantiomers in Food

The analysis of enantiomers in food has significant implications for food safety and human health. Conventional analytical methods employed for enantiomer analysis, such as gas chromatography and high-performance liquid chromatography, are characterized by their labor-intensive nature and lengthy analysis times. This review focuses on the development of rapid and reliable biosensors for the analysis of enantiomers in food. Electrochemical and optical biosensors are highlighted, along with their fabrication methods and materials. The determination of enantiomers in food can authenticate products and ensure their safety. Amino acids and chiral pesticides are specifically discussed as important chiral substances found in food. The use of sensors replaces expensive reagents, offers real-time analysis capabilities, and provides a low-cost screening method for enantiomers. This review contributes to the advancement of sensor-based methods in the field of food analysis and promotes food authenticity and safety