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What is the cost and potential of low carbon electricity transition in the MENA region?

Energy Strategy Reviews 2025年 59卷

作者： Bamisile, Olusola Xing, Yankai Acen, Caroline Cai, Dongsheng Zhang, Guangdou Adun, Humphrey Staffell, Iain Electrical Engineering Department College of Nuclear Science and Automation Engineering Chengdu University of Technology Sichuan China Centre for Environmental Policy Faculty of Natural Science Imperial College London United Kingdom Energy and Environmental Science Division CEPMLP University of Dundee United Kingdom Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University China Operational Research Centre in Healthcare Near East University TRNC Mersin 10 Nicosia99138 Turkey

Replacing fossil fuels with renewable energy sources, including wind and solar power, is a validated approach for mitigating carbon emissions. To maximize renewable energy potential and account for costs and impacts of low carbon energy transitions, research models or modelling tools must provide an accurate representation of the technological and economic capabilities of renewable energy technologies. Hence, this study assesses the electricity generation potential, and costs associated with onshore and offshore wind power, and solar photovoltaic (PV) system, in the Middle East and North Africa (MENA) region based on wind and solar atlas datasets. Unlike existing works in literature, the assessment of offshore wind energy potential and the calculation of the levelized cost of electricity (LCOE) in this study incorporates factors like water depth, distance from shore, and wind speed. Also, the potential of onshore wind energy and solar photovoltaic systems is evaluated by considering variables such as population density, availability of suitable land area, and geographical location, for every grid cell in the MENA region. We found that the average LCOE can be as low as US$40/MWh for solar PV and US$45/MWh for wind power, achieved in Saudi Arabia and Kuwait respectively. This work contributes to existing literature by providing reliable LCOE, capacity factor, and productivity data for solar and wind energy potential estimation in the MENA region. © 2025 The Authors

关键词： Solar fuels

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Misalignment Tolerance of Inductive power Transfer Coupler with Low Loss and High Magnetic Induction Ferromagnetic Materials

Misalignment Tolerance of Inductive Power Transfer Coupler w...

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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Kang, Jinping Cheng, Shaoyu Wang, Yubo Zhang, Xueying Yang, Fuyao Xu, Guorui Eldeeb, Hassan H. Zhao, Haisen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China State Grid Smart Grid Research Institute Co. LTD. State Key Laboratory of Advanced Power Transmission Technology 102211 China Noblesville Technical Center of BorgWarner Inc. NoblesvilleIN United States Florida International University Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9781665478151

In an inductive power transfer (IPT) system, various magnetic materials have difference characteristics in enhancing the degree of coupling and misalignment tolerance between the primary and secondary coils. However, the iron losses in ferromagnetic materials may decrease the transfer efficiency of the IPT system significantly. Therefore, in this study, performance and misalignment tolerance of IPT systems with four types of ferromagnetic materials are investigated. Firstly, an equivalent circuit model of the IPT system is established and the variations of system efficiency with different ferromagnetic materials are also revealed. Then, a 3-D finite element analysis (FEA) model of the magnetic coupler is established. In addition, loss test platform is established to obtain the accurate loss coefficients. Finally, the transfer efficiency and magnetic field distributions are both investigated. The results show that the high efficiency and magnetic flux density can be achieved when nanocrystalline core with vertical laminations is used. © 2022 IEEE.

关键词： Ferromagnetic materials

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Resilience Assessment of Urban power Network Considering Multi-Stage electrical-Water Fault Propagation

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IEEE Transactions on power systems 2024年

作者： Shi, Qingxin Yan, Yilu Liu, Wenxia Zeng, Bo Wang, Zhuning Li, Fangxing North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China State Grid Suzhou Electric Company Suzhou215000 China Beijing Municipal Institute of City Planning & Design Beijing100032 China The University of Tennessee Department of Electrical and Computer Engineering KnoxvilleTN37996 United States

Multi-voltage-level urban power network (UPN) is the basis for other infrastructure networks, especially water distribution network (WDN). Compared with other networks, UPN is more vulnerable to extreme events, including extreme weather events and deliberate attacks. The affection of UPN fault on WDN displays complex multi-stage characteristics. This paper first proposes a comprehensive tri-stage model of electrical-water fault propagation, including the electrical-water network connection model, the reduced-order hydraulic network model, and the water supply model of customers. Second, a scenario selection method and two resilience assessment indices are proposed. The indices measure the resistive capability of UPN considering the fault propagation effect. Finally, the proposed model and assessment method are verified by a simplified real-world urban power and water network based on the selected typical fault scenarios. The resilience assessment method helps electrical utilities to identify critical components of UPN and to make optimal decisions for long-term component hardening and short-term post-disaster restoration. © 2024 IEEE.

关键词： power distribution networks

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system power electronics

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A Stackelberg Game Approach for Signal Temporal Logic Control Synthesis with Uncontrollable Agents

arXiv

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arXiv 2025年

作者： Cui, Bohan Yu, Xinyi Giua, Alessandro Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Thomas Lord Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Electrical and Electronic Engineering University of Cagliari Cagliari09123 Italy

In this paper, we investigate the control synthesis problem for Signal Temporal Logic (STL) specifications in the presence of uncontrollable agents. Existing works mainly address this problem in a robust control setting by assuming the uncontrollable agents are adversarial and accounting for the worst-case scenario. While this approach ensures safety, it can be overly conservative in scenarios where uncontrollable agents have their own objectives that are not entirely opposed to the system’s goals. Motivated by this limitation, we propose a new framework for STL control synthesis within the Stackelberg game setting. Specifically, we assume that the system controller, acting as the leader, first commits to a plan, after which the uncontrollable agents, acting as followers, take a best response based on the committed plan and their own objectives. Our goal is to synthesize a control sequence for the leader such that, for any rational followers producing a best response, the leader’s STL task is guaranteed to be satisfied. We present an effective solution to this problem by transforming it into a single-stage optimization problem and leveraging counterexample guided synthesis techniques. We demonstrate that the proposed approach is sound and identify conditions under which it is optimal. Simulation results are also provided to illustrate the effectiveness of the proposed framework. © 2025, CC BY.

关键词： Control system synthesis

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Integral Inequalities for the Analysis of Distributed Parameter systems: A Complete Characterization via the Least-Squares Principle

Integral Inequalities for the Analysis of Distributed Parame...

引用

American Control Conference (ACC)

作者： Qian Feng Alexandre Seuret Sing Kiong Nguang Feng Xiao School of Control and Computer Engineering North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted £2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures.

关键词： Upper bound Integral equations Stability analysis Hilbert space Linear matrix inequalities Delays Kernel

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Multi-fidelity Gaussian Process for Distribution system Voltage Probabilistic Estimation with PVs

Multi-fidelity Gaussian Process for Distribution System Volt...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Jinxian Zhang Junbo Zhao Ketian Ye Fei Ding Department of Electrical and Computer Engineering University of Connecticut Storrs CT USA Power System Engineering Center National Renewable Energy Laboratory Golden CO USA

The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.

关键词： Voltage measurement Uncertainty Fuses Estimation Gaussian processes system integration Probabilistic logic

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Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions

arXiv

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arXiv 2024年

作者： Zhu, Xiang Ruan, Guangchun Geng, Hua Liu, Honghai Bai, Mingfei Peng, Chao The Department of Automation Tsinghua University Beijing10084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing10084 China MIT BostonMA02139 United States The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical & Electronic Engineering North China Electric Power University Beijing102206 China The School of Automation Engineering University of Electronics and Technology of China Chengdu611731 China

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Inner-approximate Reachability Computation via Zonotopic Boundary Analysis

arXiv

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arXiv 2024年

作者： Ren, Dejin Liang, Zhen Wu, Chenyu Ding, Jianqiang Wu, Taoran Xue, Bai Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China Department of Electrical Engineering and Automation Aalto University Espoo Finland

Inner-approximate reachability analysis involves calculating subsets of reachable sets, known as inner-approximations. This analysis is crucial in the fields of dynamic systems analysis and control theory as it provides a reliable estimation of the set of states that a system can reach from given initial states at a specific time instant. In this paper, we study the inner-approximate reachability analysis problem based on the set-boundary reachability method for systems modelled by ordinary differential equations, in which the computed inner-approximations are represented with zonotopes. The set-boundary reachability method computes an inner-approximation by excluding states reached from the initial set’s boundary. The effectiveness of this method is highly dependent on the efficient extraction of the exact boundary of the initial set. To address this, we propose methods leveraging boundary and tiling matrices that can efficiently extract and refine the exact boundary of the initial set represented by zonotopes. Additionally, we enhance the exclusion strategy by contracting the outer-approximations in a flexible way, which allows for the computation of less conservative inner-approximations. To evaluate the proposed method, we compare it with state-of-the-art methods against a series of benchmarks. The numerical results demonstrate that our method is not only efficient but also accurate in computing inner-approximations. Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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