Article
Engineering, Electrical & Electronic
Guanguan Li, Qiqiang Li, Wen Song, Luhao Wang
Summary: This paper explores the energy interaction problem among autonomous prosumers and proposes an incentive scheme to incentivize proactive energy trading. By quantifying prosumers' contributions and leveraging bargaining power, a distributed energy trading problem is formulated to achieve fair benefits allocation. The method is verified through simulations based on actual data in Shanghai, China, demonstrating effectiveness in distributed energy trading and fair benefits allocation.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Automation & Control Systems
Da Xu, Bin Zhou, Nian Liu, Qiuwei Wu, Nikolai Voropai, Canbing Li, Evgeny Barakhtenko
Summary: This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids, addressing the optimization problem of multiple resources and independent decision-makings. The methodology developed can optimize communication and energy flows through Nash bargaining problem and decomposition, showing superiority in system operational economy and resource utilization.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Energy & Fuels
Shayan Mohseni, Mir Saman Pishvaee, Reza Dashti
Summary: This paper proposes a data-driven two-level transactive energy management framework to address the main issues of trading mechanism design, uncertainty treatment, and privacy protection in networked microgrids. The framework involves determining optimal strategies for internal scheduling and external trading, as well as fair allocation of trading benefits. The uncertainties of renewable energy sources are captured using a robust optimization approach with a robust kernel density estimation (RKDE) technique. The proposed model is solved in a distributed manner using the alternating direction method of multipliers (ADMM) and the augmented Lagrange-based alternating direction inexact Newton (ALADIN) algorithms.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Engineering, Electrical & Electronic
Shuai Xuanyue, Xiuli Wang, Xiong Wu, Yifei Wang, Zhenzi Song, Bangyan Wang, Zhicheng Ma
Summary: This paper proposes a distributed coordinated operation model for multi-microgrids, which can reduce the operating cost of microgrids and maintain the enthusiasm of each microgrid for cooperation, taking into account factors such as electric and heat demand response, multi-energy interaction, and uncertainties.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Xianlong Chen, Xiuli Wang, Zelong Lu, Jing Huang, Yijun Huang
Summary: This paper proposes a distributed energy trading framework considering transportation network for electric vehicle charging stations equipped with distributed photovoltaic system. The framework addresses the electric vehicle allocation problem in the transportation network and the distributed energy trading problem among the charging stations. Numerical simulations confirm the effectiveness and viability of the framework and models.
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
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
Energy & Fuels
Lingling Wang, Quan Zhou, Zhan Xiong, Zean Zhu, Chuanwen Jiang, Runnan Xu, Zuyi Li
Summary: This paper proposes a security constrained decentralized P2P transactive energy trading framework that allows direct energy trades among neighboring prosumers. By utilizing the Nash Bargaining theory and the alternating direction method of multiplier, the framework ensures the efficiency and security of the P2P transactive energy trading without the need for traditional intermediaries.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Boyu Chen, Yanbo Che, Yue Zhou, Shuaijun Zhao
Summary: This paper proposes an optimization model to obtain the day-ahead optimal peer-to-peer trading strategy for multi-microgrids. The model considers network constraints and introduces chance constraints to mitigate the impact of renewable energy and load forecasting errors. It adopts a data-driven method to capture the probability distribution of uncertain variables and uses Nash bargaining theory and a distributed solving algorithm to handle the P2P energy trading problem.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(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)
Article
Green & Sustainable Science & Technology
Yuxin Wu, Haoyuan Yan, Min Liu, Tianyang Zhao, Jiayu Qiu, Shengwei Liu
Summary: This paper proposes a distributed energy trading scheme for a cluster of networked energy hubs, with non-discriminatory pricing. Each energy hub is treated as a self-interested agent and a hybrid AC/DC microgrid-embedded EH model is proposed to optimize operating costs. The global coupling constraints among NEHs are represented as supply limits of input energy systems. The distributed energy trading is formulated as a generalized Nash game (GNG), and an efficient distributed algorithm is proposed to compute a Nash equilibrium for the problem.
Article
Energy & Fuels
Ali Alizadeh, Moein Esfahani, Farid Dinar, Innocent Kamwa, Ali Moeini, Seyed Masoud Mohseni-Bonab, Eric Busvelle
Summary: This paper proposes a Prosumer-Based Multi-Carrier Energy System (PB-MCES) and a cooperative Transactive Energy Control (TEC) model for scheduling and controlling MCESs. The results show that implementing cooperative TEC can decrease total costs significantly, and P2P reserve trading can mitigate the impact of uncertainty.
Article
Green & Sustainable Science & Technology
Bahram Fathi, Malihe Ashena, Ali Reza Bahari
Summary: The study examines the energy, environmental, and economic (E3) efficiency in fossil fuel exporting countries during 2015-2017. The DEA approach shows different efficiency performances in each country, while the Nash bargaining game-DEA model ranks China, Oman, and others as top performers and Gabon, Saudi Arabia, and others as low performers. The second model is considered more suitable for ranking due to its unique solution.
SUSTAINABLE PRODUCTION AND CONSUMPTION
(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
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
Automation & Control Systems
Mahdi Khodayar, Jianhui Wang
Summary: This study proposes a new deep generative architecture (DGA) based on the LSTM network for probabilistic time-varying parameter identification. By learning the continuous probability density function, composite load modeling is achieved, showing accurate estimation of uncertain power resources.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Peng Li, Qiuwei Wu, Ming Yang, Zhengshuo Li, Nikos D. Hatziargyriou
Summary: This paper proposes a new distributed dispatch scheme for efficient coordinated real-time dispatch of the coupled transmission grid and active distribution grids. The scheme considers uncertainties of renewable distributions and utilizes the analytical target cascading method, diagonal quadratic approximation, and affine policy to increase computational efficiency. By reformulating the original non-convex dispatch model as a linear optimization problem, the convergence of the iterative process is ensured, reducing the computational burden.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Review
Green & Sustainable Science & Technology
S. Yin, J. Wang, Z. Li, X. Fang
Summary: With the increasing penetration of solar energy in the energy systems, the correct modeling of solar generation in the market and addressing uncertainty-based operational problems have become crucial issues. Unlike other renewable resources, solar power can be easily integrated in a distributed manner on the demand side with potential for significant future expansion. The electricity markets are transitioning from a deterministic and centralized framework to a stochastic and decentralized one, driven by the development of deregulated power markets.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Energy & Fuels
Yu Zhou, Zhengshuo Li, Guangrui Wang
Summary: This paper suggests leveraging the reactive power range embedded in wind farms to improve safety and optimality during the power system reactive power optimization process. An uncertain reactive power optimization problem involving wind farm reactive power range is introduced, which is recast as a deterministic optimization problem. The study confirms that wind farms are competent reactive power resources even with notable uncertainty.
Article
Engineering, Multidisciplinary
Xiaoxing Lu, Peng Zhang, Kangping Li, Fei Wang, Zhengshuo Li, Zhao Zhen, Tieqiang Wang
Summary: Aggregators, such as data center aggregators, are emerging entities in the electricity market that gather small flexible resources and optimize bidding strategies for participation in demand response programs, benefiting both the aggregators and the data centers.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Han Gao, Zhengshuo Li
Summary: This study presents a new Benders decomposition-based algorithm for optimizing the operation of an integrated electricity-gas system (IEGS). The algorithm features a refined decomposition structure with linear subproblems that can be solved in parallel. Case studies confirm the higher computational efficiency of the proposed algorithm compared to existing methods.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Jiayong Li, Mohammad E. Khodayar, Jianhui Wang, Bin Zhou
Summary: This paper presents a data-driven distributionally robust co-optimization model for P2P energy trading and network operation of MGs. The model considers various operational constraints and uncertainties from load consumption and RG, utilizing emerging technologies to address them effectively.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Green & Sustainable Science & Technology
Shengfei Yin, Jianhui Wang, Harsha Gangammanavar
Summary: This paper presents a three-stage unit commitment model for transmission and distribution coordination in the presence of renewable generation and demand uncertainties. The model utilizes a multi-stage stochastic programming approach to handle uncertainties and adopts a convexified AC branch flow formulation in the distribution system. A generalized nested L-shaped algorithm is devised for efficient solving of the proposed framework, with numerical experiments confirming its efficacy on multi-scale test systems.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Proceedings Paper
Energy & Fuels
Shengfei Yin, Jianhui Wang, Yanling Lin, Xin Fang, Jin Tan, Haoyu Yuan
Summary: With renewable resources increasingly entering power systems, energy storage systems (ESSs) have become essential for providing energy arbitrage and ancillary services. This paper proposes a general framework in the current electricity market environment to model the participation of multi-type ESSs and evaluate their performance, demonstrating the excellent potential of ESSs in providing ancillary services for the bulk power system.
2020 52ND NORTH AMERICAN POWER SYMPOSIUM (NAPS)
(2021)
Proceedings Paper
Energy & Fuels
Xin Fang, Jin Tan, Haoyu Yuan, Shengfei Yin, Jianhui Wang
Summary: With the increasing penetration of photovoltaic generation, electric power systems require more flexible resources and renewable generation, including PV, to provide more flexible ancillary services to improve system reliability and increase PV profitability.
2020 52ND NORTH AMERICAN POWER SYMPOSIUM (NAPS)
(2021)
Article
Engineering, Electrical & Electronic
Mingjian Cui, Jianhui Wang
Summary: This paper proposed a new defense mechanism called DH-MTD to hide the reactance of each phase in unbalanced AC distribution system and ensure system voltage stability. By using data-driven methods to combat cyberattacks, the effectiveness of DH-MTD was demonstrated in an unbalanced IEEE 123-bus distribution system.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Review
Energy & Fuels
Mandi Khodayar, Guangyi Liu, Jianhui Wang, Mohammad E. Khodayar
Summary: With the rapid growth of power systems measurements, utilizing deep learning algorithms for power systems data processing has become a research trend. The study reveals the theoretical advantages of deep learning in power systems research and discusses solutions under various problem settings.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Zhe Chen, Zhengshuo Li, Chuangxin Guo, Jianhui Wang, Yi Ding
Summary: In this paper, a coordinated robust reserve scheduling model for the coupled transmission and distribution networks is proposed, using a fully distributed ADMM framework to solve the problem. A two-layer iterative process is presented to enhance the convergence, improving cost-effectiveness and reliability effectively.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
