Article
Engineering, Electrical & Electronic
Hongsheng Xu, Qiuwei Wu, Jinyu Wen, Zhihong Yang
Summary: This paper proposes a multi-task deep reinforcement learning approach for joint bidding and pricing problem in the electricity market. The approach utilizes shared network and adaptive loss weighting to improve learning efficiency and stability, achieving optimal joint bidding and pricing policy.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Marija Miletic, Mirna Grzanic, Ivan Pavic, Hrvoje Pandzic, Tomislav Capuder
Summary: Empowering end-users to change their behavior and providing flexibility is an important aspect of the EU Clean Energy legislative package. This paper investigates the benefits of automation and different electricity pricing options for households, as well as the impact on suppliers. The results show that automated households can reduce electricity bills through time-of-use pricing, and suppliers' revenue is mostly affected by households' local production.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Energy & Fuels
Cristina Pizarro-Irizar
Summary: A key characteristic of electricity prices is their sensitivity to changes in supply and demand. The Covid-19 lockdown policies modified electricity consumption patterns, affecting the shape and position of the electricity demand curve and leading to a direct effect on electricity prices. This paper concludes that the strict lockdown phase had a strong, immediate impact on the Spanish electricity price, but it was not persistent.
Article
Energy & Fuels
Yi Wang, Zhifang Yang, Juan Yu, Junyong Liu
Summary: In this paper, an optimization-based partial marginal pricing method is proposed to reduce consumer payment under strategic bidding. By introducing discriminatory price components in the pricing formulation, this method preserves the advantages of locational marginal pricing while addressing remaining issues. Numerical results show that the proposed method effectively reduces excessive consumer payments.
Article
Energy & Fuels
Kezheng Ren, Jun Liu, Xinglei Liu, Yongxin Nie
Summary: Due to its efficiency and environmental friendliness, gas-fired units (GFU) are playing an increasingly important role in both electric power systems and natural gas systems. This study proposes a bi-level strategic bidding model that considers price and quantity factors to investigate GFU's performance in integrated electricity and natural gas markets. The model incorporates demand response management and user comfort level considerations. A modified reinforcement learning-based method, combining the deep deterministic policy gradient algorithm with autocorrelated noise, is proposed to solve the model. Test results on an integrated electricity-gas system show that the proposed method effectively reflects GFU's strategic behaviors and outperforms traditional algorithms.
Article
Energy & Fuels
Weiguang Chang, Wei Dong, Qiang Yang
Summary: This paper investigates the optimal day-ahead bidding strategy for cloud energy storage (CES) as an independent entity in the electricity market. Two energy service modes are introduced, considering the requirements and preferences of microgrids (MG). A stochastic programming-based optimization model is formulated to maximize CES's expected profits, taking into account the settlement mechanism and uncertainty of the electricity market. The proposed solution is extensively assessed through a case study of CES's energy services to five heterogeneous MGs. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution.
Article
Energy & Fuels
Hongtao Shen, Peng Tao, Ruiqi Lyu, Peng Ren, Xinxin Ge, Fei Wang
Summary: This paper investigates the strategy of load aggregators in the electricity market to achieve system balance through demand response programs. A stochastic model and the CVaR risk control method are used to address risks brought by uncertainties, and to maximize the profit of the aggregator.
Article
Construction & Building Technology
Qiming Fu, Lu Liu, Lifan Zhao, Yunzhe Wang, Yi Zheng, You Lu, Jianping Chen
Summary: As urbanization continues to accelerate, effective management of peak electricity demand is crucial to avoid power outages and system overloads. In order to address this challenge, a novel model-free predictive control method called D2PC-DDPG is proposed, which combines deep reinforcement learning and optimal control of energy storage systems. Experimental results demonstrate the superior performance of the proposed method in prediction accuracy and control performance compared to traditional machine learning and reinforcement learning methods. The method also shows generalizability in reducing peak load in multiple regions.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Energy & Fuels
Yueyong Yang, Tianyao Ji, Zhaoxia Jing
Summary: In an electricity market, power producers' profits are determined by the market price. The complexity of the market environment, primarily due to participants' interaction, can sometimes lead to unsatisfactory results even with strategies based on machine learning algorithms. A selective learning scheme based on ensemble technique is proposed for more effective strategic bidding.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Article
Thermodynamics
Gwangwoo Han, Hong-Jin Joo, Hee-Won Lim, Young-Sub An, Wang-Je Lee, Kyoung-Ho Lee
Summary: The need for reducing carbon emissions and achieving Net Zero energy has made improving heat pumps' operational efficiency a crucial goal. This study proposes a model-free deep reinforcement learning-based HP operation strategy, which minimizes electricity costs by considering thermal load demand, renewable generation, COP of HPs, and SOC of thermal storage. The results show that this method can reduce the year-round demand charge by 23.1%, energy charge by 21.7%, and electricity cost by 22.2%.
Article
Thermodynamics
Jiahui Wu, Jidong Wang, Xiangyu Kong
Summary: This study proposes an intelligent strategic bidding theoretical framework using MATL and investigates four MATL algorithms. The performance of the intelligent bidding simulation model based on the four MATL algorithms is compared and analyzed from the perspective of accuracy and convergence speed. Furthermore, the rationality and effectiveness of the intelligent bidding method using MATL based on MARL are validated through case examples.
Article
Engineering, Electrical & Electronic
Qinghu Tang, Hongye Guo, Qixin Chen
Summary: This paper proposes a bidding behavior analysis framework based on multi-task inverse reinforcement learning, which is demonstrated to be feasible and effective through empirical analysis on electricity market data.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Oveis Abedinia, Ali Ghasemi-Marzbali, Venera Nurmanova, Mehdi Bagheri
Summary: The electricity market has become competitive, and optimizing bidding strategies can increase consumer profits and promote efficient and equitable allocation of economic resources.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Energy & Fuels
Xiaodi Wang, Yunche Su, Weiting Xu, Shuai Zhang, Yongjie Zhang
Summary: This study establishes a multi-stage electricity market framework to address the challenges posed by the rapid development of renewable energy. A load aggregator is introduced to establish an energy/reserve market in the day-ahead market and real-time balanced market. A load profile perception model is proposed to evaluate the response performance of consumers. Market bidding and clearing models are established for the day-ahead market, as well as a market bidding model for the real-time balance market based on surplus flexible resources. The proposed market framework effectively promotes consumer response to system regulation requirements and reduces the risk of supply-demand imbalance.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Energy & Fuels
Qirui Li, Zhifang Yang, Juan Yu, Wenyuan Li
Summary: This paper proposes a methodology to quantitatively analyze the effect of previous revenue on a GenCo's bidding strategy. Based on the established relationship between previous revenue and bidding strategy, a bidding behavior model based on the prospect theory is developed and the bidding behavior characteristics under the influence of previous revenue are derived and analyzed. The results show that previous revenue has an obvious influence on the bidding behavior of GenCo, and different ranges of previous revenue lead to different bidding tendencies.
Article
Oncology
Zhibing Xu, Zongde Hu, Hanchen Xu, Lifen Zhang, Liang Li, Yi Wang, Yuanqing Zhu, Limeng Yang, Dan Hu
Summary: This study revealed the role of ARHGAP18 in the cardioprotective effects of Liquiritigenin (LQG) in chronic heart failure (CHF). LQG promoted the expression of ARHGAP18 and suppressed the RhoA/ROCK1 pathway, leading to a reduction in cellular apoptosis and cardiac dysfunction caused by doxorubicin. ARHGAP18 knockdown reversed these effects, while the application of LQG restored them. Overall, LQG has potential as a treatment for CHF.
EXPERIMENTAL CELL RESEARCH
(2022)
Article
Oncology
Jiashu Pan, Feng Liu, Xiaoli Xiao, Ruohui Xu, Liang Dai, Mingzhe Zhu, Hanchen Xu, Yangxian Xu, Aiguang Zhao, Wenjun Zhou, Yanqi Dang, Guang Ji
Summary: This study found that levels of m6A and METTL3 were significantly elevated in colorectal carcinoma (CRC) tissues. Patients with CRC with high m6A or METTL3 levels had shorter overall survival, and METTL3 promoted CRC progression. Mechanistically, METTL3 regulated the progression of CRC by regulating the m6A-CRB3-Hippo pathway.
JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
(2022)
Article
Environmental Sciences
Yeqing Zhou, Jingjing Zhang, Luo Wang, Hanchen Xu, Zhiping Lin, Yanxia Liu, Zhenlin Hao, Jun Ding, Yaqing Chang
Summary: The bacterial communities in aquaculture pond ecosystem are influenced by multiple media and regulated by natural evolution. The richness and diversity of bacterial communities are highest in sediment and lowest in water, and the similarity of bacterial communities is high among different regions of culture ponds.
Review
Oncology
Jiahuan Dong, Yufan Qian, Guangtao Zhang, Lu Lu, Shengan Zhang, Guang Ji, Aiguang Zhao, Hanchen Xu
Summary: Colorectal cancer is a common cancer that poses a threat to human health. Immunotherapy is widely used in cancer treatment, but most colorectal cancer patients are not responsive to it. Therefore, developing an effective combination therapy is a goal in cancer research. Natural products, with their diverse immunomodulatory effects, have the potential to be used in comprehensive cancer treatment options.
FRONTIERS IN ONCOLOGY
(2022)
Review
Oncology
Yujing Liu, Shengan Zhang, Wenjun Zhou, Dan Hu, Hanchen Xu, Guang Ji
Summary: Colorectal cancer is a common and deadly cancer associated with inflammation. High-fat diet and overweight are linked to its incidence, with bile acids playing a crucial role in its pathogenesis. Intestinal flora also plays an important role in the development of colorectal cancer.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Pei-Qiu Cheng, Yu-Jing Liu, Sheng-An Zhang, Lu Lu, Wen-Jun Zhou, Dan Hu, Han-Chen Xu, Guang Ji
Summary: This study found that circRNA may be associated with the occurrence of 5-Fu resistance and constructed a regulatory network of circRNA-miRNA-mRNA.
WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY
(2022)
Review
Pharmacology & Pharmacy
Lu Lu, Jiahuan Dong, Yujing Liu, Yufan Qian, Guangtao Zhang, Wenjun Zhou, Aiguang Zhao, Guang Ji, Hanchen Xu
Summary: Colorectal cancer is a common malignant carcinoma that often lacks obvious symptoms, leading to most patients being diagnosed in the middle or advanced stages of the disease. Inflammatory bowel disease and the inflammatory-cancer transformation of colorectal adenoma are the main causes of colorectal cancer. Recent research progress has revealed the important role of gut microbiota in colorectal tumorigenesis and treatment. The regulation of gut microbiota by natural products shows potential for the prevention and treatment of colorectal cancer.
FRONTIERS IN PHARMACOLOGY
(2022)
Article
Education & Educational Research
Xiao Li, Hanchen Xu, Jinming Zhang, Hua-hua Chang
Summary: This article discusses the adaptive learning problem of continuous latent traits and proposes a deep Q-learning algorithm along with a transition model estimator to efficiently find the optimal learning policy for learners.
JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS
(2023)
Article
Pharmacology & Pharmacy
Wenjing Ni, Tao Liu, Yujing Liu, Lu Lu, Bingduo Zhou, Yancheng Dai, Hang Zhao, Hanchen Xu, Guang Ji
Summary: This trial aims to explore the efficacy and safety of Sijunzi decoction (SJZD) in the prevention and treatment of colorectal adenoma (CRA) recurrence. The intervention phase will be 12 months and the follow-up period will last 24 months. The primary outcome is the CRA recurrence rate after intervention, and the secondary outcomes include the CRA recurrence rate at the second year post-polypectomy, the pathological type of adenoma, and the alterations in spleen deficiency syndrome (SDS) scores after intervention.
FRONTIERS IN PHARMACOLOGY
(2023)
Editorial Material
Oncology
Lishun Wang, Hanchen Xu, Yadi Wu
FRONTIERS IN ONCOLOGY
(2023)
Review
Biochemistry & Molecular Biology
Yujing Liu, Qiang Zhang, Wenjing Ni, Guang Ji, Hanchen Xu
Summary: Gastrointestinal (GI) cancer is a type of cancer with high incidence that poses a serious threat to people worldwide. Despite advancements in treatment strategies, patient benefits are still limited, making the search for new treatment strategies a priority. Cell senescence plays a dual role in GI cancer, promoting cancer development but also providing new opportunities for treatment. This review explores the mechanism of inducing cell senescence, biomarkers of senescent cells, and the potential of targeted senescence therapy for GI cancer.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Aleksandar M. Stankovic, Kevin L. Tomsovic, Fabrizio De Caro, Martin Braun, Joe H. Chow, Ninel Cukalevski, Ian Dobson, Joseph Eto, Blair Fink, Christian Hachmann, David Hill, Chuanyi Ji, James A. Kavicky, Victor Levi, Chen-Ching Liu, Lamine Mili, Rodrigo Moreno, Mathaios Panteli, Frederic D. Petit, Giovanni Sansavini, Chanan Singh, Anurag K. Srivastava, Kai Strunz, Hongbo Sun, Yin Xu, Shijia Zhao
Summary: This paper summarizes an IEEE PES Task Force report on the concept of resilience in power systems. Resilience is crucial for power systems to withstand extreme incidents and ensure uninterrupted power supply. Quantifying resilience as a key performance indicator is essential, along with considering costs and reliability. The paper also identifies gaps for future research and development.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Cell Biology
Lu Lu, Yujing Liu, Guangtao Zhang, Yangxian Xu, Dan Hu, Guang Ji, Hanchen Xu
Summary: This study investigated the relationship between circRNA and the malignant transformation from colorectal adenoma to colorectal cancer, and identified potential biomarkers for the early diagnosis of colorectal cancer.
Article
Oncology
Yuan Li, Lu Lu, Guangtao Zhang, Guang Ji, Hanchen Xu
Summary: This article summarizes the progress of endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) research, as well as their interaction with signaling pathways associated with inflammatory cancer transformation. The article mentions the potential strategies for targeting UPR in tumor therapy, and the promising results obtained in some tumor models. This review provides new insights into the mechanisms of inflammatory cancer transformation and tumor-related treatment.
AMERICAN JOURNAL OF CANCER RESEARCH
(2022)
Article
Oncology
Gaoxuan Shao, Yufan Qian, Lu Lu, Ying Liu, Tao Wu, Guang Ji, Hanchen Xu
Summary: This review summarizes the important role and mechanism of lysophosphatidylcholine acyltransferase 3 (LPCAT3) in various diseases, and aims to provide new ideas for the treatment of these diseases.