Article
Thermodynamics
Oscar Trull, J. Carlos Garcia-Diaz, Alicia Troncoso
Summary: Transmission System Operators provide forecasts of electricity demand, which are crucial for producers and sellers in planning and pricing. A new forecasting method based on Holt-Winters modelling is applied to predict holidays, showing improved accuracy compared to regular methods. The new proposal reduces forecasting error during holidays from 9.5% to under 5%.
Article
Economics
Katarzyna Maciejowska, Weronika Nitka, Tomasz Weron
Summary: Recent years have seen rapid development of renewable energy sources globally, with predictions of RES and demand levels recognized as key factors for future electricity prices. Bias in forecasts of fundamental variables published by TSOs can be improved with simple regression models, resulting in more accurate price predictions and increased revenues.
Article
Economics
Cinzia Bonaldo, Massimiliano Caporin, Fulvio Fontini
Summary: This study evaluates the risk premia in the electricity markets of Italy, France, Switzerland, and Germany by applying the hedging pressure theory. The findings suggest that future prices do not converge to the underlying day-ahead prices in all countries. Additionally, the risk premia increase as contracts approach delivery, except for in Italy and Switzerland where there is an inversion of the sign. Risk premia are negative at the beginning and positive as the delivery period approaches in these two countries.
Article
Computer Science, Information Systems
Xinghua Liu, Longyu Zu, Xiang Li, Huibao Wu, Rong Ha, Peng Wang
Summary: This paper proposes a model of electricity hydrogen integrated energy system with virtual energy storage, which aims to enhance the penetration rate of renewable energy. By optimizing scheduling strategy and integrating virtual energy storage system, the model can improve system stability and reduce equipment cost and carbon emissions, while promoting the integration of renewable energy.
Article
Construction & Building Technology
Aleksey Kychkin, Georgios C. Chasparis
Summary: This paper examines short-term forecasting of electricity-load consumption in residential buildings, introducing three new forecasting models and exploring ensemble models and adaptive model switching strategies. Simulation results on validated data demonstrate the superiority of the SPR forecasting model in reducing forecast errors compared to standard techniques.
ENERGY AND BUILDINGS
(2021)
Article
Chemistry, Multidisciplinary
Guodong Guo, Yanfeng Gong
Summary: In recent years, the increasing winter load peak has put a lot of pressure on power grid operation. Demand response on the load side can help alleviate power grid expansion and promote renewable energy consumption. However, large-scale electric air conditioning/heat pump loads responding to the same electricity price curve can lead to new peak loads and regulation failures. This paper presents a grouping coordinated preheating framework based on a demand response model, facilitating information interaction between a central controller and each regulation group. By integrating the room thermal parameter model and the performance map of inverter air conditioner/heat pump into the demand response model, this framework adopts coordination mechanisms to prevent regulation failure, applies an edge computing structure to consider user preferences and plans, and proposes a grouping and parallel computing structure to enhance computation efficiency.
APPLIED SCIENCES-BASEL
(2022)
Article
Thermodynamics
Mikhail Skalyga, Mikael Amelin, Qiuwei Wu, Lennart Soder
Summary: Combined heat and power (CHP) plants in district heating systems can produce both heat and electric power simultaneously. They can participate in electricity markets, but operational decisions need to be made under uncertain electricity prices. This paper develops a distributionally robust short-term operational model for CHP plants in the day-ahead electricity market, considering the heating network and temperature dynamics. A case study shows the reliability and profit gain of the proposed operational strategy for the CHP producer.
Article
Energy & Fuels
Marcelo Salgado-Bravo, Matias Negrete-Pincetic, Aristides Kiprakis
Summary: A flexibility estimation model is proposed to evaluate the immediate aggregate flexibility response in a day-ahead scheme. The model schedules appliances and calculates aggregated flexibility based on energy and flexibility prices. It uses an alternative flexibility scenario approach to evaluate immediate flexibility available for the next 15 minutes or 4 hours. New flexibility constraints are introduced for specific appliances, and the model is tested under different energy price schemes to observe the influence on flexibility offered.
Article
Thermodynamics
Ahmad Ghasemi, Houman Jamshidi Monfared, Abdolah Loni, Mousa Marzband
Summary: A new retail electricity pricing method is proposed in this study using a CVaR optimization framework to determine the next day's energy management planning of Micro-grid and retail electricity prices. The optimization under risk-averse conditions resulted in a significant reduction in the standard deviation of optimal retail prices and the expected cost, as well as a decrease in peak demand value.
Article
Business, Finance
Takuji Matsumoto, Derek Bunn, Yuji Yamada
Summary: Short-term risk management is crucial in power trading with the introduction of more weather risk by intermittent renewable generators. This paper analyzes a flexible hedging product, day-ahead cap futures, and parametrically predicts the probability distribution of day-ahead prices using the multifactor Generalized Additive Model. The higher-order moment model is shown to be superior in terms of fairness, underwriting risk, and variance reduction compared to lower-order models like the normal distribution.
QUANTITATIVE FINANCE
(2022)
Article
Energy & Fuels
Hasnain Iftikhar, Josue E. Turpo-Chaparro, Paulo Canas Rodrigues, Javier Linkolk Lopez-Gonzales
Summary: In this study, the forecast of hourly electricity demand is analyzed using novel decomposition methods and various time series models. The results demonstrate the efficiency and precision of the proposed decomposition combination forecasting technique. The suggested forecasting approach shows better performance compared to the best models proposed in the literature and standard benchmark models.
Article
Engineering, Electrical & Electronic
Takuji Matsumoto, Derek Bunn, Yuji Yamada
Summary: This paper develops an analytical framework for evaluating designs for imbalance settlement mechanisms and examines the case of the Japanese electricity market. The study finds that virtual trading can benefit market participants and system operators, and emphasizes the importance of market transparency for maximizing benefits.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Energy & Fuels
Juan Manuel Roldan-Fernandez, Manuel Burgos-Payan, Jesus Manuel Riquelme-Santos
Summary: The integration of residential photovoltaic systems for self-consumption is expected to bring various economic, technical, and social benefits, including local job creation and reduction in electricity prices and CO2 emissions. Regulatory barriers in Spain prevented the development of renewable self-consumption until October 2018 when a new regulation allowed self-consumption of electricity without charges. A study showed that residential self-consumption in the Iberian electricity market could potentially reduce the cost of market-traded energy by almost 2%.
Article
Management
Burak Buke, Mesut Sayin, Fehmi Tanrisever
Summary: This paper proposes a new multi-level iterative heuristic to clear the Turkish day-ahead electricity market auctions. It achieves an average optimality gap less than 0.09% and an average solution time of just 14 seconds, outperforming a commercial solver that takes an average of 18 minutes to find the optimal solution.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Energy & Fuels
Hasnain Iftikhar, Josue E. Turpo-Chaparro, Paulo Canas Rodrigues, Javier Linkolk Lopez-Gonzales
Summary: This research introduces a new decomposition-combination technique that leverages various nonparametric regression methods and time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. The experimental findings demonstrate the effectiveness and accuracy of the proposed method, making it comparable to standard benchmark models. The authors recommend applying this technique to other complex energy market forecasting challenges.
Article
Engineering, Multidisciplinary
Xiqiang Wu, Younggi Park, Ao Li, Xinyan Huang, Fu Xiao, Asif Usmani
Summary: The study successfully applies artificial intelligence and big data to predict the location and size of tunnel fires in a numerical model, achieving an accuracy of 90% through training with temperature measurements from multiple sensors. Sensitivity analysis is conducted to optimize database configuration and sensor arrangement for fast and reliable fire prediction.
Article
Construction & Building Technology
Lei Xu, Maomao Hu, Cheng Fan
Summary: This study presents three Bayesian deep neural network models for probabilistic building electrical load forecasting. The models utilize deep neural networks and the dropout technique to quantify model uncertainties. Experimental results show that all three models perform satisfactorily, with the Bayesian LSTM model achieving the best performance and satisfactory load forecasting accuracy with only 10-hour lagged electrical loads.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Construction & Building Technology
Zhihao Jiang, Jinqing Peng, Rongxin Yin, Maomao Hu, Jingyu Cao, Bin Zou
Summary: This study develops a novel flexible load characteristics model for split-type air conditioners (ACs) by combining stochastic, grey-box modeling, and random forest methods. The model accurately predicts the energy consumption of split-type ACs and explores the demand response potential. The results show that the model has a prediction error of 2.8% and effectively characterizes the energy performance of ACs.
ENERGY AND BUILDINGS
(2022)
Article
Construction & Building Technology
Hanbei Zhang, Fu Xiao, Chong Zhang, Rongling Li
Summary: This study develops a coordinated optimal load scheduling strategy for building cluster load management that considers dynamic electricity price and marginal emission factor simultaneously. The strategy effectively solves the conflicts of minimizing electricity cost, carbon emissions, and peak load while ensuring user satisfaction. The results show that the strategy can achieve a compromise between conflicting objectives in different correlation scenarios.
ENERGY AND BUILDINGS
(2023)
Article
Thermodynamics
Zhengyi Luo, Jinqing Peng, Yutong Tan, Rongxin Yin, Bin Zou, Maomao Hu, Jinyue Yan
Summary: Leveraging the flexibility of static batteries and residential flexible loads is an effective way to reduce the impacts of intermittent renewable energy power generation on the utility grid. A novel forecasting and flexibility-based operation strategy was proposed for the PV-battery-FL system to manage distributed energy resources. The strategy fully harnesses the flexibility of batteries and residential flexible loads, and outperforms traditional strategies in terms of grid-friendliness and performance.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Thermodynamics
Zhijie Chen, Fangzhou Guo, Fu Xiao, Xiaoyu Jin, Jian Shi, Wanji He
Summary: This study developed a data-driven benchmarking methodology to detect anomalous operations with degraded energy performance from a large number of bus ACs. By using a Long-Short-Term-Memory (LSTM) autoencoder-based similarity measurement method, similar operation data in other ACs can be identified for benchmarking. A LSTM network-based reference model is then developed to predict the power consumption of the target AC using these similar data. Statistical analysis-based trend and change detection algorithms are used to identify anomalies in power consumption ratio (PCR) over a few days. Two fault experiments were conducted to validate the methodology, showing its potential for health monitoring of bus ACs in a city fleet.
INTERNATIONAL JOURNAL OF REFRIGERATION
(2023)
Article
Construction & Building Technology
Tianhang Zhang, Zilong Wang, Yanfu Zeng, Xiqiang Wu, Xinyan Huang, Fu Xiao
Summary: A novel framework of Artificial-Intelligence Digital Fire (AID-Fire) was proposed for real-time identification of building fire evolution, showing promising results in a full-scale fire test room.
JOURNAL OF BUILDING ENGINEERING
(2022)
Review
Construction & Building Technology
Xiaoyu Jin, Chong Zhang, Fu Xiao, Ao Li, Clayton Miller
Summary: Data related to building energy use is crucial for research and applications in building energy efficiency. However, most cities lack comprehensive and publicly accessible building energy use datasets, hindering urban building energy modeling, energy planning, performance benchmarking, and policymaking. This review paper provides insights based on a comprehensive analysis of worldwide open datasets and their applications, including detailed information on 33 building energy datasets and their subdomains, as well as proposed policy implications. The review also discusses privacy solutions and offers valuable conclusions to support city-level building energy data disclosure, modeling, and policymaking.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Maomao Hu, Bruce Stephen, Jethro Browell, Stephen Haben, David C. H. Wallom
Summary: Data-driven forecasting techniques are widely used for accurate load forecasting in buildings. However, most studies lack reliable tests and interpretation of the results. In this study, the impact of building load dispersion level on forecast accuracy is investigated, and it is found that conventional shallow ML models outperform deep learning models for load forecasting.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Ao Li, Chong Zhang, Fu Xiao, Cheng Fan, Yang Deng, Dan Wang
Summary: Data-driven models are widely used in smart building energy management. This paper investigates the performance of three conventional model update methods and five emerging continual learning methods using a 2-year dataset. The results show that continual learning methods are more effective in ensuring long-term accuracy while reducing computation time and data storage expenses.
Article
Thermodynamics
Yanxue Li, Zixuan Wang, Wenya Xu, Weijun Gao, Yang Xu, Fu Xiao
Summary: An efficient and flexible energy management strategy is crucial for energy conservation in the building sector. This study proposes a hybrid model-based reinforcement learning framework that uses short-term monitored data to optimize indoor thermal comfort and energy cost-saving performance. Simulation results demonstrate the efficiency and superiority of the proposed framework, with the D3QN agent achieving over 30% cost savings compared to measurement results.
Article
Construction & Building Technology
Maomao Hu, Ram Rajagopal, Jacques A. de Chalendar
Summary: Building energy flexibility is a cost-effective solution to incorporate intermittent renewable energy sources into energy networks. Adjusting temperature set-points can unlock the energy flexibility potential of central air conditioning systems in complex buildings. This study investigates the impacts of temperature set-point adjustment on zone-level thermal and energy performance in three university buildings. Key findings include heterogeneities in energy use and flexibility across buildings, air handling units, and zones, as well as the limited and heterogeneous effects of temperature set-point adjustment on indoor air temperature. The proposed virtual zonal power meter enables targeted demand flexibility strategies and balances energy reduction with costs to occupants.
ENERGY AND BUILDINGS
(2023)
Article
Computer Science, Artificial Intelligence
Xinqi Zhang, Jihao Shi, Xinyan Huang, Fu Xiao, Ming Yang, Jiawei Huang, Xiaokang Yin, Asif Sohail Usmani, Guoming Chen
Summary: This study proposes a deep probabilistic graph neural network that models the spatial dependency of sensors to improve leakage detection. The results demonstrate that the model achieves competitive detection accuracy and provides more comprehensive leakage detection information. Additionally, the model's posterior distribution enhances leakage localization accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Thermodynamics
Zhengyi Luo, Jinqing Peng, Maomao Hu, Wei Liao, Yi Fang
Summary: A model-based optimal dispatch framework was proposed to optimize the operation of residential flexible loads. The framework considered the operating characteristics of flexible loads and the energy-related occupant behavior. By developing models and using data mining methods, the framework effectively reduced daily electricity costs, CO2 emissions, and the average ramping index of household power profiles.
BUILDING SIMULATION
(2023)
Article
Construction & Building Technology
Jerome Le Dreau, Rui Amaral Lopes, Sarah 'Connell, Donal Finn, Maomao Hu, Humberto Queiroz, Dani Alexander, Andrew Satchwell, Doris Osterreicher, Ben Polly, Alessia Arteconi, Flavia de Andrade Pereira, Monika Hall, Tugcin Kirant-Mitic, Hanmin Cai, Hicham Johra, Hussain Kazmi, Rongling Li, Aaron Liu, Lorenzo Nespoli, Muhammad Hafeez Saeed
Summary: This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases. The paper proposes targeted research directions to address challenges and promote greater energy flexibility deployments.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
