Article
Computer Science, Artificial Intelligence
Srikanth Reddy Konda, Lokesh Kumar Panwar, Bijaya Ketan Panigrahi, Rajesh Kumar, Vishu Gupta
Summary: This paper investigates the potential of electric vehicles in providing reserve services, demonstrating the potential profits of EVs in the reserve market, and providing references for the future application of EVs by comparing the differences between BEVs and fuel cell electric vehicles in market participation.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2021)
Article
Energy & Fuels
Menghan Zhang, Zhifang Yang, Wei Lin, Juan Yu, Wenyuan Li
Summary: To ensure the reliability of power supply, reliability unit commitment (RUC) is implemented beyond the day-ahead market (DAM) in the power industry. However, out-of-market RUC leads to incentive issues for reliability units (RUs) and additional payments. This paper analyses the compensation for RUs and presents an internalizing framework to integrate RUC into the day-ahead market, reducing out-of-market payments.
Article
Energy & Fuels
Ali Karimi, Nader Tarashandeh, Amirmasoud Kouchakzadeh, Farshad Kouchakmohseni, Mitra Naghiloo
Summary: This article analyzes the current state of the Iranian day-ahead market (IDAM) and proposes solutions for its drawbacks.
Article
Computer Science, Hardware & Architecture
Jia Luo, Ge Zhu, Hui Xiang
Summary: This article develops an AI-based model for predicting the day ahead stock market profit, using LSTM to learn the long-term reliance of stock market samples. The proposed model also incorporates shuffled frog leaping algorithm (SFLA) for random search and mutation and crossover for improving performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Thermodynamics
Xiaohui Lu, Yang Yang, Peifang Wang, Yiming Fan, Fangzhong Yu, Nicholas Zafetti
Summary: A new optimization bidding strategy is proposed for case research in China's day-ahead market, using the Emperor Penguin Optimizer and probability functions. The approach involves clustering and fitness function estimation, showing statistically effective outcomes compared to other literature-based plans.
Article
Energy & Fuels
Song Gu, Chaoping Rao, Sida Yang, Zhichao Liu, Ateekh Ur Rehman, Mohamed A. Mohamed
Summary: This paper proposes a stochastic scheme for coordinating energy management in a day-ahead market by considering electric, gas, and heat systems. It includes combined heat and power (CHP) and energy storage systems, boilers, renewable energy resources (RERs), and electric vehicles (EVs). The goal is to maximize the hub's profit while considering linear systems and restrictions. The proposed scheme also takes into account uncertainties in loads, power prices, and RER output power, and uses the grey wolf optimization algorithm for optimization.
Article
Engineering, Electrical & Electronic
Mansour Hosseini-Firouz, Asef Alemi, Behruz Alefy, Shahzad Balalpour
Summary: This study analyzes the relationship between wind power uncertainty and system operating costs, finding a way to balance optimal solutions and risk aversion through multi-objective optimization. By establishing conditional value-at-risk, the study achieves adequate trade-offs in the worst-case scenarios of wind power uncertainty.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING
(2021)
Article
Automation & Control Systems
T. Anbazhagi, K. Asokan, R. AshokKumar
Summary: This paper introduces a mutual technique for solving the profit-based unit commitment problem in a wind power-integrated power system. By predicting uncertainties in wind power generation using artificial intelligence techniques, the proposed method aims to maximize profit for generating companies and select optimal solutions through non-dominated sorting.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Makedon Karasavvidis, Dimitrios Papadaskalopoulos, Goran Strbac
Summary: This paper proposes a new optimal offering model that considers more complex profile block orders and linked block orders. The model reduces the number of binary variables and improves computational performance, and analyzes the impact of different types of block orders on the producer's profit.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Energy & Fuels
Juan Gea-Bermudez, Kaushik Das, Hardi Koduvere, Matti Juhani Koivisto
Summary: This paper introduces a mathematical model for simulating Day-ahead markets of large-scale multi-energy systems with a high share of renewable energy and emphasizes the importance of including unit commitment in the analysis. Case study results illustrate the impact of renewable energy penetration and district heating electrification on other energy sources. Relaxing integer variables can significantly reduce computational time without compromising result accuracy.
Article
Engineering, Electrical & Electronic
Bing Huang, Arezou Ghesmati, Yonghong Chen, Ross Baldick
Summary: Pumped storage hydro units (PSHU) are valuable sources of flexibility in power systems, but their flexibility in the real-time market has not been thoroughly studied. This paper proposes two PSH models that incorporate uncertainties and manage risks using probabilistic price forecast in the daily operation. Numerical studies demonstrate that the proposed models improve market efficiency compared to traditional methods.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Energy & Fuels
Priti Das, Tanmoy Malakar
Summary: This paper discusses the regulatory measures that encourage investment in green energy in the competitive global energy market, emphasizing the need to unlock the potential of short-term contracts to address imbalance issues in the long-term energy market. It proposes a day-ahead energy contract model and forecasts season-specific wind speed scenarios for a wind farm in the Indian energy market context, demonstrating the model's effectiveness through case studies and results.
ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY
(2021)
Article
Engineering, Electrical & Electronic
Yonghong Chen, Feng Pan, Feng Qiu, Alinson S. Xavier, Tongxin Zheng, Muhammad Marwali, Bernard Knueven, Yongpei Guan, Peter B. Luh, Lei Wu, Bing Yan, Mikhail A. Bragin, Haiwang Zhong, Anthony Giacomoni, Ross Baldick, Boris Gisin, Qun Gu, Russ Philbrick, Fangxing Li
Summary: This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established to review and analyze the current state of the security-constrained unit commitment (SCUC) business model and its solution techniques in electricity market clearing problems. It also investigates future challenges in market clearing problems and presents efforts in developing benchmark models.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Computer Science, Information Systems
Hossein Mehdipourpicha, Rui Bo
Summary: Virtual bidding allows financial players and physical market participants to take advantage of opportunities in electricity markets. A bi-level optimization model is proposed to help physical market participants maximize profits by leveraging both physical assets and virtual transactions. Through case studies, it is shown that physical market participants may deploy virtual transactions in a different way than purely financial players.
Article
Engineering, Electrical & Electronic
Pedro Otaola-Arca, Javier Garcia-Gonzalez, Fernando Marino, Ignacio Rivera
Summary: The operational costs of gas fired units (GFU) in real power systems are complex due to factors like gas procurement and Third Party Access (TPA) tariffs. A novel mathematical formulation is presented to properly model these issues and highlight the benefits in a self-UC model.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Forestry
Arun Kumar Thakur, Rajesh Kumar, Raj Kumar Verma, Pankaj Kumar
Summary: This study explores the relationships between tree species distribution and environmental parameters in Western Himalaya, finding that altitude and temperature are the major determining factors. It also shows an upward shift in the regeneration pattern of these tree species. These findings are important for conservation planning and monitoring tree range dynamics under climate change.
JOURNAL OF SUSTAINABLE FORESTRY
(2023)
Article
Computer Science, Artificial Intelligence
Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar
Summary: To tackle the complexity of trajectory generation for biped robots on uneven terrain, a data-driven Gait model is proposed in this paper. Deep learning methods are employed to develop seven different data-driven models, with LSTM+GRU-based model showing the best performance. Experimental results demonstrate the superiority of the proposed Gait models over traditional finite state machine and Basis models in terms of error summary statistics.
Article
Computer Science, Artificial Intelligence
Sidharth Gautam, Tapan Kumar Gandhi, B. K. Panigrahi
Summary: In this research, a novel two-fold method named Weighted Median Channel Prior (WMCP) is proposed, focusing on self-adaptive prior, which resolves the problems caused by using a fixed size local-patch in the dehazing process. WMCP leverages spatially changing haze statistics to estimate depth-map in varying haze conditions. It is a scale-invariant technique that retains most of the information in the local neighborhood of the hazy input image for estimating scene depth, which traditional methods fail to preserve. Additionally, an unsharp-masking based technique called edge-modulation (EM) enhances hidden or missing details lost due to haze, resulting in visually aesthetic and realistic dehazed images. Comparative evaluation shows the superiority of this method in terms of visibility improvement and edge preservation, especially in dense haze regions.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2023)
Article
Computer Science, Information Systems
Arup Anshuman, Bijaya Ketan Panigrahi, Manas Kumar Jena
Summary: This article proposes a method for real-time monitoring of oscillation events, which can visualize and classify low-frequency oscillatory modes in the system. Significant oscillatory modes are extracted and analyzed through a novel algorithm, and presented in the form of time-domain plots to enhance operator understanding. The method is validated and compared, demonstrating its superior performance.
IEEE SYSTEMS JOURNAL
(2023)
Article
Computer Science, Information Systems
Varaha Satya Bharath Kurukuru, Ahteshamul Haque, Mohammed Ali Khan, Rajesh Kumar
Summary: This article proposes a failure mode effect classification (FMEC) approach for localizing faults in power electronic converters. The approach uses model-driven fault detection and data-driven fault identification to determine the fault effect on inputs, components, and sensors without compromising the power stage of the converter. Numerical simulations and experimental analysis validate the effectiveness of the proposed approach.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar
Summary: Long-term prediction of joint kinematics is crucial for advanced technology development in prosthetic leg, biped robot, automotive, and human-robot collaboration fields. However, finding the suitable activation function is challenging. This research studies the suitability of activation functions for a data-driven gait model on uneven surfaces, and the Sigmoid-weighted Linear Units (SiLU) function-based gait model outperforms others in terms of maximum error statistic.
RESULTS IN ENGINEERING
(2023)
Review
Engineering, Electrical & Electronic
Vikash Kumar Saini, Rajesh Kumar, Ameena S. Al-Sumaiti, A. Sujil, Ehsan Heydarian-Forushani
Summary: This paper provides a comprehensive review of learning-based short-term forecasting models for smart grid applications. It explores various types of forecasting models, including physical, statistical, hybrid, and uncertainty analysis models, specifically for wind speed forecasting. The study employs 41 different models and evaluates their performance based on regression coefficients and error indices. The findings suggest that the models' performance varies with seasonal variability. The paper also presents recommendations for energy storage planning, energy market and policymakers, and reliability and reserve sizing.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
V. Kumar Saini, Rajesh Kumar, Ameena Saad Al-Sumaiti, B. K. Panigrahi
Summary: Cloud energy storage systems (CES) are a new paradigm for residential community microgrids, allowing consumers to become self-sustaining and interact with utilities and other consumers. This paper proposes the use of CES for residential prosumers, utilizing machine learning-based uncertainty quantization and an artificial ecosystem optimization (AEO) method to determine optimal battery capacity considering uncertainty in PV, load, and price. The feasibility and profitability of deploying CES with residential PV are assessed, and simulation results show that the suggested framework integrated with a distributed PV system is more economical.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Sauvik Biswas, Paresh Kumar Nayak, Bijaya Ketan Panigrahi, Gayadhar Pradhan
Summary: This paper proposes an intelligent relaying scheme for efficient detection and classification of faults in AC transmission lines. This scheme extracts suitable features from locally measured current signals using variational mode decomposition (VMD) and utilizes a deep convolutional neural network (CNN) classifier for fault classification. The proposed scheme is evaluated on a DFIG wind farm and UPFC compensation system, showing fast fault detection time (<10 ms) and high accuracy in fault detection and classification (100% and 99.86%).
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Thermodynamics
Karan Sareen, Bijaya Ketan Panigrahi, Tushar Shikhola, Rajneesh Sharma
Summary: Due to its renewable and ecological attributes, wind energy is gaining global attention, but accurate forecasting of wind speed is challenging due to its variable and stochastic nature. Many wind speed forecasting algorithms neglect missing value imputation, which can significantly affect prediction accuracy. This study proposes a hybrid technique using k-NN-CEEMDAN-BiDLSTM, which combines data imputation, signal denoising, and neural network analysis to achieve better prediction accuracy. Empirical findings show that this hybrid technique outperforms other existing techniques in terms of accuracy.
Review
Computer Science, Artificial Intelligence
Karan Sareen, Bijaya Ketan Panigrahi, Tushar Shikhola
Summary: This study focuses on four Indian cities in the state of Rajasthan, namely Ajmer, Jaipur, Jodhpur, and Kota, and applies a proposed technique for predicting Global Horizontal Irradiance (GHI) using 30-minute ahead data obtained from the National Institute of Wind Energy and Wind Resource (NIWE) data site. Three different signal decomposition algorithms, namely Empirical Mode Decomposition (EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Ensemble Empirical Mode Decomposition (EEMD), are used for data preprocessing. The signal reconstruction is based on the comparison of Pearson's Correlation Coefficient (PCC) values of the corresponding Intrinsic Mode Functions (IMFs) and Residuals obtained from the three decomposition algorithms. The selected IMFs and Residuals from each algorithm are then combined to form a single input for solar irradiance forecasting using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed technique demonstrates high accuracy with less than 2% Mean Absolute Percentage Error (MAPE) for different seasons and site locations considered.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Energy & Fuels
Prashant Shrivastava, P. Amritansh Naidu, Sakshi Sharma, Bijaya Ketan Panigrahi, Akhil Garg
Summary: The accuracy of state estimation for lithium-ion batteries directly affects the performance and lifespan of the battery management system (BMS) due to their dynamic and non-linear behavior. This paper explores existing state estimation algorithms for SOC, SOE, SOP, and SOH and proposes a new combined states estimation method for higher accuracy and lower computational burden. Experimental results show that the estimated SOC and SOE error is <2.5% under various operating conditions. Additionally, the proposed method accurately estimates actual capacity and (dis)charge SOP. Rating: 9/10.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Computer Science, Artificial Intelligence
Bhawna Mewara, Gunjan Sahni, Soniya Lalwani, Rajesh Kumar
Summary: Protein-protein interactions (PPIs) play a crucial role in various biological processes and have become a key focus in system biology. They are essential for predicting protein function and drug ability. This article introduces a new feature representation method called CAA-PPI for extracting features from protein sequences and achieving high prediction accuracy in PPI analysis.
Article
Engineering, Electrical & Electronic
Vikash Kumar Saini, Ameena S. Al-Sumaiti, Rajesh Kumar
Summary: This paper proposes a data-driven approach to managing uncertainties in cloud-based energy storage systems integrated with renewable energy. SVR, LSTM, and CNN-GRU algorithms are used to estimate the forecast errors of load and PV power, and two mechanisms are proposed to determine the net load error. The net error is analyzed statistically to form different uncertainty-bound confidence intervals, and the operation cost of the cloud energy storage system is calculated.
ELECTRIC POWER SYSTEMS RESEARCH
(2024)
Article
Engineering, Electrical & Electronic
Wandry R. Faria, Gregorio Munoz-Delgado, Javier Contreras, Benvindo R. Pereira Jr
Summary: This paper proposes a new bilevel mathematical model for competitive electricity markets, taking into account the participation of distribution systems operators. A new pricing method is introduced as an alternative to the inaccessible dual variables of the transmission system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Chao Zhang, Liwei Zhang, Dong Wang, Kaiyuan Lu
Summary: The load disturbance rejection ability of electrical machine systems is crucial in many applications. Existing studies mainly focus on improving disturbance observers, but the speed response control during the transient also plays a significant role. This paper proposes a sliding mode disturbance observer-based load disturbance rejection control with an adaptive filter and a Smith predictor-based speed filter delay compensator to enhance the transient speed response.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Arif Hussain, Arif Mehdi, Chul-Hwan Kim
Summary: The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems. The scheme demonstrates good performance in feature extraction, feature selection, and islanding detection, and it also performs effectively in noisy environments.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Zonghui Sun, Xizheng Guo, Shinan Wang, Xiaojie You
Summary: This paper presents a status pre-matching method (SPM) that eliminates the iterative calculations for resistance switch model, and simulates all operation modes of PECs through a more convenient approach. Furthermore, a FPGA implementation scheme is proposed to fully utilize the multiplier units of FPGA.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Rui Zhou, Shuheng Chen, Yang Han, Qunying Liu, Zhe Chen, Weihao Hu
Summary: In power system scheduling with variable renewable energy sources, considering both spatial and temporal correlations is a challenging task due to the complex intertwining of spatiotemporal characteristics and computational complexity caused by high dimensionality. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method that generates probabilistic scenarios accounting for spatial and temporal correlations simultaneously. The method incorporates Latin hypercube sampling, copula-importance sampling theory, and probability-based scenario reduction technique to efficiently capture the spatial and temporal correlation in the dynamic optimal power flow problem. Numerical simulations demonstrate the superiority of the proposed approach in terms of computational efficiency and accuracy compared to existing methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Juan Manuel Mauricio, J. Carlos Olives-Camps, Jose Maria Maza-Ortega, Antonio Gomez-Exposito
Summary: This paper proposes a simplified thermal model of VSC, which can produce accurate results at a low computational cost. The model consists of a simple first-order thermal dynamics system and two quadratic equations to model power losses. A methodology is also provided to derive the model parameters from manufacturer data.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Jae-Kyeong Kim, Kyeon Hur
Summary: This paper investigates the relationship between the accuracy of finite difference-based trajectory sensitivity (FDTS) analysis and the perturbation size in non-smooth systems. The study reveals that the approximation accuracy is significantly influenced by the perturbation size, and linear approximation is the most suitable method for practical applications.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yuan Si, Amjad Anvari-Moghaddam
Summary: This paper investigates the impact of geomagnetic disturbances on small signal stability in power systems and proposes the installation of blocking devices to mitigate the negative effects. Quantitative evaluation reveals that intense geomagnetic disturbances significantly increase the risk of small signal instability. Optimal placement of blocking devices based on sensitivity scenarios results in a significant reduction in the risk index compared to constant and varying induced geoelectric fields scenarios.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xuejian Zhang, Wenxin Kong, Nian Yu, Huang Chen, Tianyang Li, Enci Wang
Summary: The intensity estimation of geomagnetically induced currents (GICs) varies depending on the method used. The estimation using field magnetotelluric (MT) data provides the highest accuracy, followed by the estimation using 3D conductivity models and the estimation using a 1D conductivity model. The GICs in the North China 1000-kV power grid have reached a very high-risk level, with C3 and C4 having a significant impact on the geoelectric field and GICs.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yue Pan, Shunjiang Lin, Weikun Liang, Xiangyong Feng, Xuan Sheng, Mingbo Liu
Summary: This paper introduces the concept and model of offshore-onshore regional integrated energy system, and proposes a stochastic optimal dispatch model and an improved state-space approximate dynamic programming algorithm to solve the model. The case study demonstrates the effectiveness and high efficiency of the proposed method in improving economic and environmental benefits.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Mohammad Eydi, Reza Ghazi, Majid Oloomi Buygi
Summary: Proportional current sharing, voltage restoration, and SOCs balancing in DC microgrid control algorithms are the leading challenges. This paper proposes a novel communication-less control method using a capacitor and a DC/DC converter to stabilize the system and restore the DC bus voltage. The method includes injecting an AC signal into the DC bus, setting the current of energy storage units based on frequency and SOC, and incorporating droop control for system stability. Stability analysis and simulation results validate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xiangjian Meng, Xinyu Shi, Weiqi Wang, Yumin Zhang, Feng Gao
Summary: With the increasing penetration of photovoltaic power generation, regional power forecasting becomes critical for stable and economical operation of power systems. This paper proposes a minute-level regional PV power forecasting scheme using selected reference PV plants. The challenges include the lack of complete historical power data and the heavy computation burden. The proposed method incorporates a novel reference PV plant selection method and a flexible approach to decrease the accumulated error of rolling forecasting.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Huabo Shi, Yuhong Wang, Xinwei Sun, Gang Chen, Lijie Ding, Pengyu Pan, Qi Zeng
Summary: This article investigates the dynamic stability characteristics of the full size converter variable speed pumped storage unit and proposes improvements for the control strategy. The research is important for ensuring the safe and efficient operation of the unit.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Firmansyah Nur Budiman, Makbul A. M. Ramli, Houssem R. E. H. Bouchekara, Ahmad H. Milyani
Summary: This paper proposes an optimal harmonic power flow framework for the daily scheduling of a grid-connected microgrid, which addresses power quality issues and ensures effective control through demand side management.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Cong Zeng, Ziyu Chen, Jizhong Zhu, Fellew Ieee
Summary: This paper introduces a distributed solution method for the multi-objective OPF problem, using a coevolutionary multi-objective evolutionary algorithm and the idea of decomposition. The problem is alleviated by decomposing decision variables and objective functions, and a new distributed fitness evaluation method is proposed. The experimental results demonstrate the effectiveness of the method and its excellence in large-scale systems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
