Article
Thermodynamics
Youquan Liu, Huazhen Li, Jiawei Zhu, Yishuai Lin, Weidong Lei
Summary: In this paper, a novel demand-side management method is proposed for managing the operation of residential appliances. By formulating a multi-objective optimization problem, the trade-off between reducing electricity cost and increasing user satisfaction is achieved. The experiment results confirm the effectiveness of the optimization model and the higher efficiency of the hybrid algorithm.
Review
Energy & Fuels
Abiodun E. Onile, Ram Machlev, Eduard Petlenkov, Yoash Levron, Juri Belikov
Summary: Innovative solutions targeting improvements in the behavior of energy consumers, particularly personalized recommendations, are essential for enhancing sustainable progress towards energy efficiency. This study focuses on innovative energy services based on intelligent recommendation systems and digital twins, finding that data-driven twin technologies can provide novel ways of implementing consumer-oriented demand-side management. Barriers associated with the adoption of energy services, especially digital-twins concept, are identified and coherent policy recommendations for wide-spread adoption are summarized.
Article
Energy & Fuels
Muhammad Waseem, Zhenzhi Lin, Shengyuan Liu, Zhi Zhang, Tarique Aziz, Danish Khan
Summary: This paper presents an innovative home appliances scheduling framework based on customer preferences, considering demand response and energy storage systems to reduce pollution and fossil fuel usage. By using game theory and fuzzy compromising method, the framework optimizes consumption cost and comfort level while reducing overall energy cost and gaseous emissions.
Review
Energy & Fuels
Amit Shewale, Anil Mokhade, Nitesh Funde, Neeraj Dhanraj Bokde
Summary: The residential sector plays a significant role in global energy demand and is expected to have a substantial increase in energy consumption. Demand response solutions, particularly home energy management systems (HEMSs), are considered effective ways to meet the growing energy demands while optimizing energy consumption and consumer comfort. However, the performance analysis of these techniques is limited.
Article
Energy & Fuels
Jiaqi Ruan, Guolong Liu, Jing Qiu, Gaoqi Liang, Junhua Zhao, Binghao He, Fushuan Wen
Summary: This paper proposes a time-varying algorithm for estimating the price elasticity of demand (PED) in the smart energy system. It also introduces a demand-side smart dynamic pricing mechanism to encourage user participation in demand response programs. Experimental results demonstrate the feasibility of the proposed mechanism in reducing peak-to-average ratio (PAR) without exposure to price risk.
Review
Green & Sustainable Science & Technology
Daniele Groppi, Antun Pfeifer, Davide Astiaso Garcia, Goran Krajac, Neven Duic
Summary: The European Union has identified the key role of islands in sustainable energy systems, but the integration of renewable energy sources on islands has caused technical issues in the electricity grid. This paper focuses on solutions to improve grid flexibility in dealing with unpredictability of renewable energy sources, particularly in island contexts.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Green & Sustainable Science & Technology
Russell McKenna, Diana Abad Hernando, Till ben Brahim, Simon Bolwig, Jed J. Cohen, Johannes Reichl
Summary: This study assesses the impact of a smartphone app on providing greater demand-side flexibility for household electricity consumers based on data from a field trial in Austria, and applies the findings to an energy system model for analysis. The results demonstrate that ICT-enabled demand side flexibility can significantly impact future system costs and should be integrated into and considered in the future energy system.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Energy & Fuels
Marco Castangia, Riccardo Sappa, Awet Abraha Girmay, Christian Camarda, Enrico Macii, Edoardo Patti
Summary: This paper introduces a novel anomaly detection method based on unsupervised deep learning techniques for detecting electrical faults in household appliances. By training a variational autoencoder to analyze the power signatures of appliances, the method shows higher classification accuracy compared to traditional algorithms, allowing for better characterization of normal cycles and more precise alerts.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Review
Green & Sustainable Science & Technology
Sana Iqbal, Mohammad Sarfraz, Mohammad Ayyub, Mohd Tariq, Ripon K. Chakrabortty, Michael J. Ryan, Basem Alamri
Summary: This paper reviews research on Demand Side Management strategies, identifying challenging perspectives for future study. Researchers use soft computing and optimization techniques to address energy management challenges, with DSM implementation playing an important role in smart energy management.
Article
Engineering, Electrical & Electronic
Alper Cicek, Ayse Kubra Erenoglu, Ozan Erdinc, Altug Bozkurt, Akin Tascikaraoglu, Joao P. S. Catalao
Summary: The significant development of semiconductor technology has paved the way for dominating the household appliances market, leading to harmonic pollution due to the nonlinear characteristics of typical loads in this market. Efforts have been made to control total harmonic distortion and total demand distortion within international standard limits. Furthermore, load shifting strategies have drawn special attention to improve system performance substantially in the smart grid paradigm.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Amit Shewale, Anil Mokhade, Amruta Lipare, Neeraj Dhanraj Bokde
Summary: The evolution of the smart grid has enabled residential users to efficiently manage their growing energy demand. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. In this paper, two novel home energy management models are proposed to optimize energy consumption, and price-based demand response techniques are incorporated. The proposed algorithms are shown to be efficient methods for home energy management, achieving significant cost reductions and peak-to-average ratio reductions.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Marco Castangia, Awet Abraha Girmay, Christian Camarda, Edoardo Patti
Summary: Nonintrusive load monitoring estimates the energy consumption of household appliances by analyzing the aggregated power consumption at the main meter. This study proposes a new solution that allows training nonintrusive load monitoring algorithms without supervision from submeters. The algorithm is divided into two stages, appliance detection and state-based disaggregation, which show significant improvements in performance compared to previous methods trained with submeter data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Ravindra Kumar Yadav, P. N. Hrisheekesha, Vikas Singh Bhadoria
Summary: Most of the world's electrical energy demand is met by natural resources, but there is a widening gap between demand and supply. To address this, renewable/alternate sources of energy need to be integrated with smart grids to meet future electricity demand. Demand side management (DSM) is a promising solution to reduce peak load burden on utilities, and the grey wolf optimization (GWO) algorithm is utilized in this study to solve the DSM minimization problem. The optimization results show significant reductions in peak load and energy costs, and are compared with existing research papers.
Article
Engineering, Multidisciplinary
El Sayed F. Tantawy, Ghada M. Amer, Hanaa M. Fayez
Summary: This paper presents a Home Energy Management System (HEMS) for smart homes. The system includes Photovoltaic Sources and Battery Storage Units (BSU) and scheduling Smart Home (SH) appliances for various residential users. The paper introduces a classification algorithm of energy response programs, based on electricity prices, for scheduling appliances in single and multiple homes using Time of Use (TOU) and Inclining Block Rate (IBR). Four different optimization algorithms are compared, and the simulation results demonstrate that the proposed algorithm achieves desired objectives such as lowering electricity cost, minimizing waiting time for users, and increasing the stability and reliability of the grid and consumer satisfaction.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Hyeontaek Hwang, Sanggil Kang
Summary: This article proposes a novel NILM model that solves the device classification problem in low-frequency data by extracting features using LSTM and improving the feature representation ability through feedback. Experiments demonstrate that the method performs well on imbalanced data classes and can be applied in real-world situations.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Automation & Control Systems
Fengji Luo, Gianluca Ranzi, Weicong Kong, Gaoqi Liang, Zhao Yang Dong
Summary: Residential demand response is a promising approach to improve grid energy efficiency, and this article proposes a personalized recommendation system based on collaborative filtering to recommend energy-efficient household appliance usage plans to users. The system classifies users as “highly responsive” or “less responsive” based on their DR degree analysis, infers user lifestyles from appliance usage profiles, and makes appliance-use recommendations to target users. Experiments using a residential data simulator validate the proposed system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Environmental Sciences
Giulia Ulpiani, Gianluca Ranzi, Mat Santamouris
Summary: The study conducted a 15-year retrospective analysis on 14 sites in Greater Sydney and Illawarra, highlighting the impact of urbanization, global warming, and bushfires on local meteorology and air pollution. Results indicate a significant degradation in air quality due to climate warming, emphasizing the urgent need for control measures.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Construction & Building Technology
Giulia Ulpiani, Gianluca Ranzi, Jie Feng, Mattheos Santamouris
Summary: Daytime radiative cooling shows promising theoretical performances under ideal conditions, but faces limitations in real-world situations with low atmospheric transparency and reduced sky access. New methods have been proposed to enhance its applicability in different climates, weathers, and terrains. Therefore, more experimental demonstrations are needed for further research and development in this area.
ENERGY AND BUILDINGS
(2021)
Article
Engineering, Multidisciplinary
Chunming Zhang, Fengji Luo, Mingyang Sun, Gianluca Ranzi
Summary: This paper investigates the robustness of AMI systems under Distributed Denial-of-Service (DDoS) attacks and proposes an optimal defense strategy. By establishing state transition models and dynamic differential systems, the evolution of network states can be effectively described to ensure system stability.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Energy & Fuels
Zehua Zhao, Fengji Luo, Yongxi Zhang, Gianluca Ranzi, Sheng Su
Summary: This paper proposes an integrated, multi-objective Home Energy Management System (HEMS) that optimally schedules controllable electric household appliances to balance home energy cost, user satisfaction, and occupant comfort. The HEMS models the operational relationship between non-thermostatically controlled appliances and the air conditioner, and an advanced multi-objective optimizer is used to solve the management model. Simulations are conducted to validate the proposed method.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Zehua Zhao, Fengji Luo, Chunming Zhang, Gianluca Ranzi
Summary: This paper introduces a new peer-to-peer electricity market prototype that aims to facilitate energy trading among energy prosumers and consumers while considering their social relationship preferences. By establishing a data-driven social network model and an auction-based electricity trading mechanism, participants can make end-to-end energy trading decisions efficiently. Numerical simulations based on real-world datasets validate the proposed system's effectiveness.
IET RENEWABLE POWER GENERATION
(2022)
Article
Energy & Fuels
Jie Feng, Kai Gao, Yue Jiang, Giulia Ulpiani, Djordje Krajcic, Riccardo Paolini, Gianluca Ranzi, Mattheos Santamouris
Summary: Optimized random silica-polymer materials have demonstrated that high emissivity and strong selectivity are essential for achieving high cooling power under complex thermo-radiative balance in different climates.
SOLAR ENERGY MATERIALS AND SOLAR CELLS
(2022)
Article
Green & Sustainable Science & Technology
Youjun Deng, Yongxi Zhang, Fengji Luo, Gianluca Ranzi
Summary: This paper investigates the utilization of second life battery energy storage systems (SL-BESSs) for home energy management. The study establishes the model of SL-BESS and satisfaction indices to verify the dwelling satisfaction conditions. A many-objective home energy management system is proposed to identify operation plans for controllable home energy resources, considering factors such as energy cost, SL-BESS's lifetime preservation, satisfaction indices, and load peak-to-average ratio.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Chemistry, Multidisciplinary
Md Mahfuzur Rahman, Gianluca Ranzi
Summary: This paper presents a state-of-the-art review of research on the long-term behavior of composite steel-concrete slabs. It discusses the identification of time-dependent response of concrete, experimental work on composite slabs, and theoretical models for predicting shrinkage-induced behavior.
APPLIED SCIENCES-BASEL
(2022)
Correction
Energy & Fuels
Zehua Zhao, Fengji Luo, Yongxi Zhang, Gianluca Ranzi, Sheng Su
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Yu He, Fengji Luo, Gianluca Ranzi
Summary: This paper proposes a transferrable model-agnostic meta-learning approach for short-term load forecasting for single households. The approach reduces computation and communication costs on the household side and achieves superior forecasting performance, even with limited training data.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Runze Deng, Fengji Luo, Jiajia Yang, Da-Wen Huang, Gianluca Ranzi, Zhao Yang Dong
Summary: This paper proposes a new trading framework for enabling energy trading between end energy customers and a small-capacity Renewable Energy Plant (REP) in urban environments. The energy trading process is designed on a community basis to relieve the communication burden of end customers, and homomorphic encryption technology is integrated into the framework to protect customer privacy.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Teng Yu, Fengji Luo, Canglong Pu, Zehua Zhao, Gianluca Ranzi
Summary: This paper introduces a new dual-blockchain system for P2P energy trading, where a primary blockchain stores transaction data and a secondary blockchain handles compute-intensive tasks. An Improved Optimistic Rollup mechanism is proposed to facilitate the cooperation between the two blockchains, ensuring efficient and secure P2P energy trading transactions.
Article
Energy & Fuels
Fengji Luo, Ali Dorri, Gianluca Ranzi, Junbo Zhao, Raja Jurdak
Summary: The prevalence of distributed generation leads to the emergence of the Virtual Power Plant (VPP) paradigm. A VPP aggregates distributed energy resources and can operate like a traditional power plant. This study systematically presents a building VPP (BVPP) architecture that communicates with autonomous building energy management systems (BEMSs) to aggregate building-side energy resources. Case studies demonstrate the effectiveness of BVPP in various operational conditions.
IET ENERGY SYSTEMS INTEGRATION
(2022)
Article
Energy & Fuels
Zehua Zhao, Fengji Luo, Gianluca Ranzi, Fei Gao
Summary: This study examines the emerging application of Mobile Battery Energy Storage Systems (MBESSs) within the broader framework of Battery Energy Storage Systems (BESSs). By transporting lightweight BESSs, energy backup support can be provided to multiple geographical locations. An integrated distribution planning framework is proposed to optimize the MBESS carrier's driving paths and the distribution plans for the BESSs, aiming to maximize the total profit for the MBESS service provider.
IET ENERGY SYSTEMS INTEGRATION
(2022)
