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  • Open Access


    Enhancing Renewable Energy Integration: A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

    Ali S. Alghamdi1,*, Mohamed A. Zohdy2, Saad Aldoihi3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1339-1370, 2024, DOI:10.32604/cmes.2024.048839

    Abstract In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in… More >

  • Open Access


    Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

    Aadel M. Alatwi1,2, Abualkasim Bakeer3, Sherif A. Zaid1,*, Ibrahem E. Atawi1, Hani Albalawi1,4, Ahmed M. Kassem5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1807-1830, 2024, DOI:10.32604/cmes.2024.048521

    Abstract An autonomous microgrid that runs on renewable energy sources is presented in this article. It has a superconducting magnetic energy storage (SMES) device, wind energy-producing devices, and an energy storage battery. However, because such microgrids are nonlinear and the energy they create varies with time, controlling and managing the energy inside them is a difficult issue. Fractional-order proportional integral (FOPI) controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance. The suggested dedicated control for the SMES comprises two loops: the outer loop, which uses the FOPI to regulate… More >

  • Open Access


    Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

    Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 449-471, 2024, DOI:10.32604/cmc.2024.048771

    Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel… More >

  • Open Access


    Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

    Mousumi Basu1, Chitralekha Jena2, Baseem Khan3,4,*, Ahmed Ali4

    Energy Engineering, Vol.121, No.4, pp. 849-867, 2024, DOI:10.32604/ee.2024.043294

    Abstract In the restructured electricity market, microgrid (MG), with the incorporation of smart grid technologies, distributed energy resources (DERs), a pumped-storage-hydraulic (PSH) unit, and a demand response program (DRP), is a smarter and more reliable electricity provider. DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines. Better bidding strategies, prepared by MG operators, decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources (RES). But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate. To More >

  • Open Access


    Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

    Yan Shi1, Wenjie Li1, Gongbo Fan2,*, Luxi Zhang1, Fengjiu Yang1

    Energy Engineering, Vol.121, No.2, pp. 461-482, 2024, DOI:10.32604/ee.2023.043835

    Abstract Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation, this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side. A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation, with the overarching goal of optimizing the system for low-carbon operation. To begin with, an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation… More >

  • Open Access


    A Novel Non-Isolated Cubic DC-DC Converter with High Voltage Gain for Renewable Energy Power Generation System

    Qin Yao, Yida Zeng*, Qingui Jia

    Energy Engineering, Vol.121, No.1, pp. 221-241, 2024, DOI:10.32604/ee.2023.041028

    Abstract In recent years, switched inductor (SL) technology, switched capacitor (SC) technology, and switched inductor-capacitor (SL-SC) technology have been widely applied to optimize and improve DC-DC boost converters, which can effectively enhance voltage gain and reduce device stress. To address the issue of low output voltage in current renewable energy power generation systems, this study proposes a novel non-isolated cubic high-gain DC-DC converter based on the traditional quadratic DC-DC boost converter by incorporating a SC and a SL-SC unit. Firstly, the proposed converter’s details are elaborated, including its topology structure, operating mode, voltage gain, device stress,… More >

  • Open Access


    Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

    Abdennasser Dahmani1, Yamina Ammi2, Nadjem Bailek3,4,*, Alban Kuriqi5,6, Nadhir Al-Ansari7,*, Salah Hanini2, Ilhami Colak8, Laith Abualigah9,10,11,12,13,14, El-Sayed M. El-kenawy15

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2579-2594, 2023, DOI:10.32604/cmc.2023.040625

    Abstract Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations… More >

  • Open Access


    Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity

    Ju Hyeon Lee1, Jiho Shin2, Jung Taek Seo3,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 757-779, 2023, DOI:10.32604/cmc.2023.039461

    Abstract As energy-related problems continue to emerge, the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration. Renewable energy is becoming increasingly important, with solar power accounting for the most significant proportion of renewables. As the scale and importance of solar energy have increased, cyber threats against solar power plants have also increased. So, we need an anomaly detection system that effectively detects cyber threats to solar power plants. However, as mentioned earlier, the existing solar power plant anomaly detection system monitors only operating information such as power generation, making… More >

  • Open Access


    Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

    Weigang Jin1, Lei Chen2,*, Yifei Li2, Shencong Zheng2, Yuqi Jiang2, Hongkun Chen2

    Energy Engineering, Vol.120, No.11, pp. 2469-2487, 2023, DOI:10.32604/ee.2023.043021

    Abstract Along with the increasing integration of renewable energy generation in AC-DC power networks, investigating the dynamic behaviors of this complex system with a proper equivalent model is significant. This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator (DFIG) based wind farms to decrease the simulation scale and computational burden. For the AC-DC power networks, the equivalent modeling strategy in accordance with the physical structure simplification is stated. Regarding the DFIG-based wind farms, the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo More >

  • Open Access


    CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

    Muhammad Munsif1,2, Fath U Min Ullah1,2, Samee Ullah Khan1,2, Noman Khan1,2, Sung Wook Baik1,2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1751-1773, 2023, DOI:10.32604/csse.2023.038514

    Abstract Photovoltaic (PV) systems are environmentally friendly, generate green energy, and receive support from policies and organizations. However, weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits. Existing PV forecasting techniques (sequential and convolutional neural networks (CNN)) are sensitive to environmental conditions, reducing energy distribution system performance. To handle these issues, this article proposes an efficient, weather-resilient convolutional-transformer-based network (CT-NET) for accurate and efficient PV power forecasting. The network consists of three main modules. First, the acquired PV generation data are forwarded to the pre-processing module for data refinement. Next, to… More >

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