Authors: Yinbiao Shu
DOI: 10.1049/ein2.12016
Title: Energy Internet: Redefinition and categories
Authors: Hongbin Sun, Qinglai Guo, Xinwei Shen, Yixun Xue, Mohammad Shahidehpour, Nikos Hatziargyriou
DOI: 10.1049/ein2.12008
Abstract: The concept of ‘Energy Internet’ (EI) has been widely accepted by both academic and industry experts after more than a decade of development. Since it was proposed, EI has been discussed and applied to many technical works in power and energy areas. Some specific definitions were proposed for EI by those who have applied it to respective fields of engineering, but a comprehensive and widely accepted definition of EI is still being debated. In this paper, we propose the redefinition of EI, based on a comprehensive literature review, some latest trends and driving forces in the global energy industry, as well as its development in the past decade. In addition, we summarise the EI framework and features for future applications, where EI is categorised by its scale into local‐ and wide‐area applications to manifest its effectiveness in power and energy.
Title: Enabling artificial intelligence‐based scenario application in new type power systems
Authors: Shixiong Fan, Jianbo Guo, Shicong Ma, Guozheng Wang, Dongqi Li, Zening Zhao
DOI: 10.1049/ein2.12009
Abstract: At present, artificial intelligence (AI) technology, as a disruptive and frontier technology, is changing people's production and lifestyle with the cross combination of other scientific fields. Under the background of Green and low‐carbon transition in energy, the construction of new type power systems (NTPS) is the future direction of the transformation and development of the power industry. AI is an important supporting technology for the digital transformation of the power industry, which can accelerate the construction of NTPS and new energy systems. This article provides the author's viewpoints on application of AI technologies in NTPS, mainly involving the development of electric power AI and the main problems it currently faced. The discussion on the bottlenecks of AI application will be focused on data and models. Some future research directions are also presented.
Title: The application and challenge of energy router in energy internet
Authors: Jinliang Huang, Wei Han, Hanlei Tian, Jie Qiu, C. C. Chan
DOI: 10.1049/ein2.12006
Abstract: The energy internet has emerged as a promising area of research in power systems with distributed generation. Similar to an internet router to connect and switch networks, the energy router within the energy internet plays a crucial role to integrate and distribute the energy flow. This paper provides an overview of the application and challenges associated with energy routers in the energy internet. Firstly, the ability of energy routers to bring together multiple energy sources and information is highlighted. Secondly, the discrepancies in power structure when energy routers are utilised in different voltage‐level power grids are discussed. Besides, the roles, structures, and design requirements of energy routers and how they interconnect with the power system, as well as some practical examples of energy router are delved. Finally, the main challenges faced by energy routers are summarised.
Title: Artificial intelligence driving perception, cognition, decision‐making and deduction in energy systems: State‐of‐the‐art and potential directions
Authors: Zhaoyang Dong, Tianjing Wang
DOI: 10.1049/ein2.12010
Abstract: In the context of energy systems, managing the complex interplay between diverse power sources and dynamic demands is crucial. With a focus on smart grid technology, continuously innovating artificial intelligence (AI) algorithms, such as deep learning, reinforcement learning, and large language model technologies, have been or have the potential to be leveraged to predict energy consumption patterns, enhance grid operation, and manage distributed energy resources efficiently. These capabilities are essential to meet the requirements of perception, cognition, decision‐making, and deduction in energy systems. Nevertheless, there are some critical challenges in efficiency, interpretability, transferability, stability, economy, and robustness. To overcome these challenges, we propose critical potential directions in future research, including reasonable sample generation, training models with small datasets, enhancing transfer ability, combining with physics models, collective generative pre‐trained transformer‐agents, multiple foundation models, and improving system robustness, to make advancing AI technologies more suitable for practical engineering.
Title: Deciphering the electricity–carbon market nexus: Challenges and prospects of electricity–carbon coupling
Authors: Ran Zhang, Zelong Lu, Mohammad Shahidehpour, Zuyi Li, Lei Yan
DOI: 10.1049/ein2.12003
Abstract: The power industry's carbon emissions stand out as a primary contributor to the overall carbon dioxide emissions within the energy system under the context of energy Internet. Thus, reducing emissions in the power sector has become crucial for achieving carbon neutrality. However, challenges from the electricity–carbon nexus have surfaced in effectively coordinating and integrating the carbon market with the electricity market. This paper initiates the exploration of such nexus by analysing the current status of major carbon emission trading markets on a global scale. Subsequently, it delves into a comprehensive examination of the coupling between the electricity and carbon markets across three levels: market participants, operational models and market mechanisms. Four key issues are then identified in the electricity–carbon nexus: challenges in decisionmaking for market participants, discrepancies in operational timelines, the intricate design of market coupling mechanisms and the spillover effects of market risks. To tackle the above challenges in the electricity–carbon nexus, this paper takes a deep dive into two different models in understanding the nexus including econometric/statistical models and optimisation models, serving as the foundation for understanding the intricacies of electricity–carbon market coupling. This paper concludes with a detailed exploration for future roadmap and research prospects in the electricity–carbon market nexus.
Title: Long‐term changes of wind resources and its impact on wind power development under climate change in China
Authors: Shuanglei Feng, Zongpeng Song, Qing Yang, Yunhe Hou, Zheng Wang, Feng Liu, Bo Wang, Weisheng Wang
DOI: 10.1049/ein2.12001
Abstract: The development of wind energy is indispensable in the pursuit of global carbon neutrality. This article's analysis of observational data across China reveals the annual average wind speed declined at a rate of −0.167 m · s−1 decade−1 between 1981 and 2014. This rate is 33 times faster than projections from the Coupled Model Intercomparison Project (CMIP) of the World Climate Research Programme. We propose a novel wind power scale estimation method based on annual average wind speed, suitable for assessing climate change impacts. Considering China's planned wind power generation in 2030, climate change may increase the required wind installed capacity by over 25% under the observed trend scenario. In contrast, historical average and CMIP scenarios could substantially overestimate wind potential while underestimating the necessary future wind power development scale. Climate change poses potential adverse impacts on China's carbon peak goals, necessitating targeted measures to mitigate these effects.
Title: Analysis for integrated energy system: Benchmarking methods and implementation
Authors: Suhan Zhang, Wei Gu, X.‐P. Zhang, C. Y. Chung, Ruizhi Yu, Shuai Lu, Rodrigo Palma‐Behnke
DOI: 10.1049/ein2.12002
Abstract: The selection of suitable models and solutions is a fundamental requirement for conducting energy flow analysis in integrated energy systems (IES). However, this task is challenging due to the vast number of existing models and solutions, making it difficult to comprehensively compare scholars' studies with current work. In this paper, we aim to address this issue by presenting a comprehensive overview of mainstream IES models and clarifying their relationships, thereby providing guidance for scholars in selecting appropriate models. Additionally, we introduce several widely used solvers for solving algebraic and differential equations, along with their detailed implementations in the energy flow analysis of IES. Furthermore, we conduct extensive testing and demonstration of these models and methods in various cases to establish benchmarking datasets. To facilitate reproducibility, verification and comparisons, we provide open‐source access to these datasets, including system data, analysis settings and implementations of the various solvers in the mainstream models. Scholars can utilise the provided datasets to reproduce the results, verify the findings and perform comparative analyses. Moreover, they have the flexibility to customise these settings according to their specific requirements.
Title: Multi‐energy system horizon planning: Early decarbonisation in China avoids stranded assets
Authors: Xiaowei Zhou, Kai Strunz, Tom Brown, Hongbin Sun, Fabian Neumann
DOI: 10.1049/ein2.12011
Abstract: An appropriate decarbonisation pathway is crucial to achieving carbon neutrality in China before 2060. This paper studies decarbonisation pathways for China's energy system between 2020 and 2060 using an open, provincial, and hourly resolved, networked model within the context of multi‐period planning with myopic investment foresight. Two representative decarbonisation pathways are compared, with particular attention to the synergies of coupling the electricity and heating sectors. An early and steady path in which emissions are strongly reduced in the first decade is more cost‐effective than following a late and rapid path. Early decarbonisation in the electricity sector avoids stranded investments in fossil infrastructure and preserves the carbon budget for later emissions in the difficult‐to‐decarbonise heating sector. Retrofitting the existing coal power plants by adding carbon capture facilities is cost‐effective in both decarbonisation pathways. The hourly and non‐interrupted resolution for a full weather year reveals the balancing strategies of highly renewable, sector‐coupled systems. The significant seasonal variation of heat demand dominates long‐term storage behaviours.
Title: Joint chance‐constrained coordinated scheduling for electricityheat coupled systems considering hydrogen storage
Authors: Lun Yang, Xunhang Sun, Xiaoyu Cao, Mengxiao Chen, Xiaohong Guan
DOI: 10.1049/ein2.12007
Abstract: Strong interdependence between power production and heat supply from combined power and heat units could restrict the wind power integration. Deploying hydrogen storage, typically formed by the electrolyser, hydrogen tank, and fuel cell, could be a promising measure to accommodate surplus wind power and facilitate the coordinated operation of power and heat. The authors consider coordinated scheduling under wind power uncertainty for an electricity‐heat coupled system with hydrogen storage and propose a distributionally robust joint chance‐constrained coordinated scheduling (DRJCC‐CS) model, where the waste heat from the electrolyser and fuel cell is incorporated. The authors design distributionally robust joint chance constraints to account for wind power uncertainty related constraints and derive their tractable inner and outer mixed‐integer convex approximations. Consequently, the proposed DRJCC‐CS model is casted into a mixed‐integer convex programme. Case studies are conducted to demonstrate the effectiveness of the proposed DRJCC‐CS method.
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https://mc.manuscriptcentral.com/csee-theiet-ein
https://ietresearch.onlinelibrary.wiley.com/journal/29952166
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