Article
Energy & Fuels
Weidong Chen, Junnan Wang, Guanyi Yu, Jiajia Chen, Yumeng Hu
Summary: This study demonstrates that cooperative game among microgrids can achieve flexible consumption of renewable energy in the region and reduce operating costs. Nash bargaining helps alliance members to get satisfactory trading power and tariff, while effectively improving overall operational efficiency and market competitiveness of microgrid systems.
Article
Computer Science, Information Systems
Sungwook Kim
Summary: This paper proposes a novel energy-aware DC management scheme, using cooperative game theory to address uncertainties, designing two game models and coupling them to achieve greater advantages in DC operations while balancing contradictory requirements for DC management.
Article
Energy & Fuels
Shan Cheng, Xianwang Zuo, Zhaobin Wei, Kaixuan Ni, Can Wang
Summary: This article establishes a cooperative game model based on Nash bargaining between MG and CSSIS, and proposes a distributed computation method based on ADMM to achieve Nash equilibrium. By applying augmented Lagrange function and ADMM, the coupled constrained mathematical problem is divided into two independent subproblems, achieving win-win results.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Xuewei Pan, Fan Yang, Peiwen Ma, Yijin Xing, Jinye Zhang, Lingling Cao
Summary: This paper proposes a general model and analysis method for studying the optimized operation of AC/DC microgrid clusters by introducing a dynamic adjustment mechanism of electricity price and establishing a game model for the optimal operation of multiple microgrids. The validity and economy of the proposed model are verified through an actual case study.
Article
Green & Sustainable Science & Technology
Navid Rezaei, Abbas Fattahi Meyabadi, Mohammadhossein Deihimi
Summary: This paper introduces a demand-side integration (DSI) framework to achieve efficient electrical energy consumption for smart grids and meet customer requirements. The proposed framework is based on a prepaid orderly energy consumption strategy for smart microgrids and captures the interaction between aggregators and end-use customers as a Nash Bargaining Game. A dynamic electricity pricing scheme is implemented to determine profitable daily electricity tariffs, considering the price responsiveness of loads. The framework integrates resources from small-scale consumers to solve the day ahead energy scheduling problem. A supplementary pay-off module accompanies the DSI program to handle real-time deviations. The DSI framework is formulated as a stochastic optimization problem, considering probabilistic uncertainty in the generation pattern of renewable energy resources. Simulation results show improvements in load factor, net profit, and reduction in total gas emissions compared to the normal consumption paradigm, demonstrating the accuracy and merit of the proposed method.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Green & Sustainable Science & Technology
G. M. Cabello, S. J. Navas, I. M. Vazquez, A. Iranzo, F. J. Pino
Summary: This paper reviews the ongoing research studies and microgrid pilot projects in Spain, focusing on the renewable energy potential in the country. It highlights the main investigation trends in the field, including the use of technologies, control methods, and operational challenges. The study finds that photovoltaic and wind power are the most favored energy generation technologies, and batteries are the most widely used energy storage systems. Advanced control strategies such as MPC or fuzzy logic are replacing traditional strategies for higher efficiency. The paper provides a comprehensive analysis of the potential of renewable energy penetration through smart grids in Spain, examining the equipment and control strategies implemented in various facilities.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Economics
Zhi Liu, Xiao-Xue Zheng, Deng-Feng Li, Chen-Nan Liao, Jiuh-Biing Sheu
Summary: This study investigates the impact of retailers' fairness concerns on cooperative relationships in a three-party sustainable supply chain using Nash equilibrium strategy, and proposes a novel coordination method combining IVLSPN approach and three-party Nash bargaining game model. Fairness concerns do affect members' decisions and their cooperation for sustainable supply chain management, and the developed coordination method aims to perfectly coordinate the TSSC.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Computer Science, Information Systems
Jeongmeen Suh, Sung-Guk Yoon
Summary: By studying cooperative game theory, a profit-sharing rule suitable for incomplete networks is proposed, and after comparative experiments with Korean data, it is confirmed that this rule can fairly distribute profits based on the contributions of each MG.
Article
Automation & Control Systems
Kai Ma, Pei Liu, Jie Yang, Bo Yang, Rong Wu, Kuan Zhang
Summary: This article examines a cooperative communication network in the smart grid, optimizing the transmission power allocation of relays to reduce errors and costs. By introducing a two-layer game model and iterative algorithm, the total costs of utility companies can be minimized.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Theory & Methods
Luca Anzilli, Giovanni Villani
Summary: We investigate the optimal strategies of firms in an R&D oligopoly setting with different types of cooperative agreements under fuzzy uncertainty. We approach the problem as an R&D fuzzy bargaining game, incorporating firms' fuzzy payoffs through the real exchange option methodology that considers managerial flexibility. The novelty lies in the parametric description of collaboration intensity and its fuzzy impact on firms' payoffs. Notably, the level of imprecise knowledge significantly influences firms' strategic alliances.
FUZZY SETS AND SYSTEMS
(2023)
Review
Energy & Fuels
Kang Miao Tan, Thanikanti Sudhakar Babu, Vigna K. Ramachandaramurthy, Padmanathan Kasinathan, Sunil G. Solanki, Shangari K. Raveendran
Summary: The rapid growth in the usage and development of renewable energy sources in the present day electrical grid necessitates the use of energy storage technologies to mitigate intermittent power discrepancies. These technologies stabilize power production and energy demand by storing excess energy for use when needed. Researchers and industrial experts have explored various energy storage technologies by integrating different renewable energy sources into storage systems, with the potential for energy storage systems to become the primary electricity sources in future electric grids.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Nongmaithem Nandini Devi, Surmila Thokchom, Thoudam Doren Singh, Gayadhar Panda, Ramasamy Thaiyal Naayagi
Summary: Due to the problem of global warming and climate change, it is crucial to generate power using renewable energy sources like solar, wind, and fuel cells. With the transition from a traditional grid to a smart grid, digital communication technologies and information technology play a significant role. The article introduces a model for energy trading in the smart grid, using game theory-based multi-stage Nash Bargaining Solution (NBS) to negotiate a fair price among participants, promoting their active involvement and reducing greenhouse gas emissions.
Article
Environmental Sciences
Tek Narayan Bhattarai, Swastik Ghimire, Bandita Mainali, Shiva Gorjian, Helen Treichel, Shukra Raj Paudel
Summary: Energy transformation and sustainability pose a significant challenge for developing countries, and smart grid technology is seen as a crucial solution. This article explores the pivotal role of smart grids in strengthening power systems, integrating renewable energy sources, promoting electrification in transportation, and harnessing bioenergy. It also highlights the potential of smart grids in Nepal and other developing nations with similar energy-related barriers.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Energy & Fuels
Songli Fan, Qian Ai, Guodong Xu, Haijun Xing, Yang Gao
Summary: In this paper, an incentive-based cooperative coordination framework between port microgrid and berthed ships is proposed, aiming to minimize the net cost by adjusting the CI demand requests of berthed ships, thereby reducing the operational burden of the port.
Article
Thermodynamics
Jiazhu Xu, Yuqin Yi
Summary: In this paper, a microgrid model is established for coordinating electricity, heat, and gas. Robust interval optimization is used to deal with uncertainties in the source and load. A combined heat and power (CHP) model integrating power to gas (P2G) and carbon capture systems (CCS) is constructed to achieve emissions reduction through carbon trading. A multi-microgrid Peer-to-Peer (P2P) low-carbon economic operation model is established based on the Nash bargaining theory and solved using the alternating direction method of multipliers (ADMM) with good convergence and privacy.
Article
Thermodynamics
Wenjun Tang, Hao Wang, Xian-Long Lee, Hong-Tzer Yang
Summary: This study uses machine learning to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on smart meter data. The findings reveal the diversity of load patterns and the difference between weekdays and weekends, and suggest that age and education level may influence load patterns. The proposed analytical model using feature selection and machine learning proves to be more effective in mapping the relationship between load patterns and socioeconomic features than XGBoost and conventional neural network models.
Article
Green & Sustainable Science & Technology
Md Murshadul Hoque, Mohsen Khorasany, Reza Razzaghi, Hao Wang, Mahdi Jalili
Summary: This paper proposes a novel sensitivity-based transactive energy framework to coordinate electric vehicles with voltage control in low-voltage power distribution network. The framework combines economic and control mechanisms to enable EVs to participate in the real-time local energy market and mitigate voltage issues through scheduling. The proposed method prioritizes EV owners' preferences and concerns, achieving customers' satisfaction and robust voltage control.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Computer Science, Information Systems
Yongsheng Cao, Hao Wang, Demin Li, Guanglin Zhang
Summary: With the advancement of IoT technology, scheduling electric vehicles has become easier, but the negative impact on the power grid due to charging needs to be addressed. This article investigates the problem of scheduling EV charging to minimize cost and balance electricity load without knowing future information. The proposed smart charging algorithms effectively reduce charging costs and peak load while considering uncertainties in EV charging behaviors.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Guoliang Xing, Jianwei Huang
Summary: ClusterFL is a clustering-based federated learning system that achieves high model accuracy and low communication overhead for human activity recognition applications. It captures the intrinsic clustering relationship among nodes and drops slow-converging or less correlated nodes to speed up convergence while maintaining accuracy.
ACM TRANSACTIONS ON SENSOR NETWORKS
(2023)
Article
Engineering, Electrical & Electronic
Yang Weng, Shuman Luo, Qiushi Cui, Robert Trask, Hao Wang
Summary: Although batteries are increasingly used in households, utilities lack knowledge of customer-owned batteries, making it difficult to determine the necessity of adding a DC meter. To address this issue, this paper proposes a bi-level optimization framework that considers battery incentive design and physical law to enable utilities and customers to evaluate the costs and benefits of adopting DC meters.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Chao Huang, Haoran Yu, Jianwei Huang, Randall Berry
Summary: We study the design of incentive mechanisms for information elicitation without verification. We propose a continuum-armed bandit-based mechanism that dynamically learns the optimal reward level. We also enhance the inference algorithm and propose a novel rule for aggregating workers' solutions more effectively.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Chao Huang, Haoran Yu, Jianwei Huang, Randall A. Berry
Summary: We study a crowdsourcing problem where a platform has more information about workers' accuracy and strategically reveals it to incentivize high-quality solutions. We analyze the cases where workers trust or update their beliefs based on the platform's announcement. For naive workers, the platform should announce a high accuracy, while for strategic workers, announcing a lower accuracy may be beneficial. We also show that when the platform is uninformed about workers' prior, increasing the accuracy may paradoxically lead to decreased platform payoff and social welfare.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Tiance Zhang, Jianxiao Wang, Hao Wang, Jin Ruiyang, Gengyin Li, Ming Zhou
Summary: Recently, there has been increasing attention to the efficient coordination between transmission system operators (TSO) and distribution system operators (DSO) in the turnover of energy services between transmission-distribution grids. We propose a TSO-DSO coordination framework with temporal-coupled constraints embedded, called dynamic feasible region (DFR), to achieve effective interaction between TSO and DSO with minimal information. We derive the polyhedral form of DFR and design a novel outer progressive approximation (OPA)-based algorithm to effectively add feasibility cuts.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Md Murshadul Hoque, Mohsen Khorasany, Reza Razzaghi, Mahdi Jalili, Hao Wang
Summary: This paper presents a novel framework for network-aware coordination of aggregated electric vehicles (EVs) via transactive control in distribution networks considering the operating constraints of the network. The proposed framework facilitates participation of EVs in local energy markets through aggregators, while their preferences and privacy are preserved. The flexibility of EVs in charging-discharging is employed to resolve the congestion and voltage problems in distribution networks. The performance of the proposed method is evaluated in various case studies conducted on the IEEE 55-node, 100-node and 302-node LV European test systems. Simulation results and a comparative study demonstrate the effectiveness of the proposed framework in terms of achieving high-quality congestion and voltage management, satisfying EV owners' preferences, and computation efficiency.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Computer Science, Artificial Intelligence
Jinhao Li, Changlong Wang, Hao Wang
Summary: Wind curtailment in wind farms causes significant economic losses. This study proposes a deep reinforcement learning approach for a wind-battery system to reduce curtailment and maximize revenue through joint-market bidding.
Article
Engineering, Industrial
Yuanzheng Li, Jingjing Huang, Yun Liu, Hao Wang, Yongzhen Wang, Xiaomeng Ai
Summary: The rapid development of data centers poses a significant challenge in terms of energy consumption and environmental impact. Establishing DC microgrids can address these issues by utilizing renewable energy generation and waste heat recovery systems. This article presents a multicriteria optimal operation framework for DC microgrids, considering various criteria through a multicriteria optimization approach. An augmented constraint algorithm and a balanced decision-making method are proposed to determine the optimal scheduling solution. A case study and results analysis demonstrate the effectiveness of this framework.
IEEE INDUSTRY APPLICATIONS MAGAZINE
(2023)
Article
Computer Science, Hardware & Architecture
Guocheng Liao, Xu Chen, Jianwei Huang
Summary: Data reporters have privacy concerns due to data correlation and social relationship. However, these factors are difficult to quantify precisely. A novel Bayesian game-theoretic framework is proposed to analyze the data reporters' behaviors. The lack of complete information can lead to a degradation of overall privacy protection.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Information Systems
Ningning Ding, Lin Gao, Jianwei Huang
Summary: This paper investigates the impact of provider interaction structures on the overall IoT system. By studying three different structures, the optimal pricing strategies of providers in each structure are obtained. The results show that the coordinated structure is the best choice for providers and customers, and the vertically-uncoordinated structure is preferred when customers have high demand variance and medium utility-cost ratio.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Chaofan Yu, Yuanzheng Li, Yun Liu, Leijiao Ge, Hao Wang, Yunfeng Luo, Linqiang Pan
Summary: This study develops a bi-objective stochastic dispatch model to investigate the relationship between renewable energy utilization and transmission security. The model considers the objectives of renewable energy curtailment and the capacity margin of transmission lines, and proposes a data-driven Bayesian assisted optimization algorithm to improve the searching efficiency.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chenyou Fan, Junjie Hu, Jianwei Huang
Summary: In this article, a metric-based multi-agent few-shot learning framework is proposed, which enables agents to accurately and efficiently perceive the environment under limited communication and computation conditions through an efficient communication mechanism, asymmetric attention mechanism, and metric-learning module. Additionally, a specially designed ranking-based feature learning module is utilized to improve accuracy by maximizing inter-class distance and minimizing intra-class distance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
