Article
Energy & Fuels
Elnaz Shahrabi, Seyed Mehdi Hakimi, Arezoo Hasankhani, Ghasem Derakhshan, Babak Abdi
Summary: This study introduces an improved energy hub system model that optimizes energy planning and scheduling by incorporating various renewable energy sources and storage devices. The utilization of thermal energy storage in the energy hub system significantly reduces fuel consumption and CO2 emissions. The efficiency of the proposed Quantum Particle Swarm Optimization (QPSO) algorithm surpasses Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms in terms of convergence speed and global search ability for optimal scheduling and planning of the energy hub system in the presence of stochastic renewable energy systems.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Energy & Fuels
W. Abdulrazzaq Oraibi, B. Mohammadi-Ivatloo, S. H. Hosseini, M. Abapour
Summary: This paper presents a mixed-integer linear programming approach to restore prioritized loads and enhance the interconnection of renewable energy sources, energy storage systems, and electric vehicle charging stations in the distribution system. Stochastic optimization techniques are used to model uncertainties and demand response programs and interruptible loads are introduced to improve the resilient operation of the distribution system. The proposed planning model is tested on a benchmark test system and found to be feasible, effective, and efficient in normal and resilient operation.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Armin Cosic, Michael Stadler, Muhammad Mansoor, Michael Zellinger
Summary: Local and renewable energy communities have the potential to efficiently utilize distributed energy technologies at regional levels, but face limitations. By using mixed-integer linear programming, challenges in planning can be overcome, leading to economic and ecological benefits for participants.
Article
Green & Sustainable Science & Technology
Guangdi Li, Qi Tang, Bo Hu, Min Ma
Summary: In this paper, a new approach is proposed to improve the flexibility of a thermoelectric coupling energy system by utilizing the district heating network, and a probabilistic model is established for the spinning reserves capacity related to confidence level. The study found that there is a linkage between system costs, flexibility, and thermal characteristic index, and the optimal result can achieve overall system balance.
Article
Energy & Fuels
Muhyaddin Rawa, Yusuf Al -Turki, Khaled Sedraoui, Sajjad Dadfar, Mehrdad Khaki
Summary: In order to address worldwide environmental concerns, power system operators and planning entities are seeking new energy sources with lower emissions. Utilities are increasingly choosing renewable energy sources, and microgrids provide an ideal platform for incorporating them. This study presents a seasonal optimization framework for the short-term operation of a microgrid, taking into account energy storage and solar photovoltaic systems, and analyzing the impact of climate factors on resource scheduling.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Electrical & Electronic
Sajjad Fattaheian-Dehkordi, Mehdi Tavakkoli, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, Matti Lehtonen
Summary: The emergence of reformation and privatization in energy systems has led to the development of multi-agent distribution systems, where each agent operates its resources independently and the distribution network operator (DNO) controls the grid efficiently and reliably. This paper proposes a framework that incentivizes agent cooperation to mitigate operational effects of contingency conditions. The Stackelberg game is used to optimize resource scheduling in post-contingency conditions, and a step-wise strategy is provided to consider islanded areas in the grid.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Thermodynamics
Rizki Firmansyah Setya Budi, Sarjiya, Sasongko Pramono Hadi
Summary: Obtaining the optimal power purchase agreements (PPA) is crucial in deregulated generation expansion planning. A proposed model based on game theory and multi-scenario analysis was applied to the Bangka Belitung power system, resulting in an optimum PPA of 30% of generation cost and levelized electricity cost of 6.009 cents USD/kWh. The utility's inability to offer a PPA lower than 30% led to increased costs by renting diesel power plants, affecting the levelized cost of electricity to 8.629 cents USD/kWh.
Review
Green & Sustainable Science & Technology
Muhammad Usman Khan, Jonathan Tian En Lee, Muhammad Aamir Bashir, Pavani Dulanja Dissanayake, Yong Sik Ok, Yen Wah Tong, Mohammad Ali Shariati, Sarah Wu, Birgitte Kiaer Ahring
Summary: Anaerobic digestion produces biogas which is a low cost and environmentally friendly energy source, but it contains unwanted elements that need to be removed through upgrading technologies. Efforts are being made to improve existing methods and develop new technologies such as cryogenic separation and biological upgrading.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Green & Sustainable Science & Technology
Xiaohan Liu, Xiaolei Ma, Ruifeng Shi, Zhengke Liu
Summary: This paper presents a BEB scheduling problem with the deployment of photovoltaic and energy-storage system at bus-charging stations. A two-step solution approach is proposed and a case study shows that introducing such systems can significantly reduce charging costs and carbon dioxide emissions.
PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-ENGINEERING SUSTAINABILITY
(2022)
Article
Thermodynamics
Mengdi Huang, Jianxia Chang, Aijun Guo, Mingzhe Zhao, Xiangmin Ye, Kaixuan Lei, Zhiwen Peng, Yimin Wang
Summary: Power system flexibility is crucial for grid safety and stability. This study proposes a joint probability distribution considering the output of intermittent renewable energy sources and load forecast deviation, and builds an optimal dispatch model to improve the operation of flexible resources.
Article
Engineering, Electrical & Electronic
Yuzhou Zhou, Qiaozhu Zhai, Lei Wu
Summary: This paper proposes a new multistage generation scheduling method for regional microgrids with renewables and energy storage that can ensure robustness and nonanticipativity of scheduling solutions. A feasibility proposition and a scenario-based multistage robust scheduling model are established to address uncertainties and guarantee economic performance of scheduling results. Numerical tests demonstrate the efficacy of the proposed method.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Thermodynamics
Ning Zhang, Qiuye Sun, Lingxiao Yang
Summary: The paper proposes a two-stage multi-objective optimal scheduling strategy (TMOS) based on the innovative mathematical model of We-Energy (WE) in the integrated energy system (IES). TMOS considers the comprehensive impact of multiple significant operation indicators, leading to improved economic benefit and customer satisfaction. By adjusting the components of the WE to reduce the impact of RE prediction error, real-time power balancing can be achieved to ensure secure operation.
Article
Energy & Fuels
Qiuwen Li, Dong Mo, Xiangyun Kong, Yufu Lu, Yangdou Liang, Zhencheng Liang
Summary: As technology advances, the flexible load in the comprehensive energy system has been improved, providing more schedulable resources for the power system. This study investigates an integrated energy system that includes an electric vehicle power station and various flexible loads. By building an intelligent energy system model and an optimization model, the operational expenses of the system are minimized and the utilization of wind power is increased.
Review
Green & Sustainable Science & Technology
Fan Li, Dan Wang, Dong Liu, Songheng Yang, Ke Sun, Zhongjian Liu, Haoyang Yu, Jishuo Qin
Summary: This paper first summarizes the challenges brought by the high proportion of new energy generation to smart grids, then reviews the classification of existing energy storage technologies and their practical application functions in the smart grid environment. It analyzes the optimization planning and benefit evaluation methods for energy storage technologies in three different main application scenarios, and points out the advantages and shortcomings of current research. The paper also highlights pressing issues in energy storage planning and elucidates aspects that warrant attention in future application and promotion processes, resulting in a comprehensive understanding of energy storage technologies in smart grids.
Article
Computer Science, Information Systems
Ngakan Ketut Acwin Dwijendra, Indrajit Patra, N. Bharath Kumar, Iskandar Muda, Elsayed M. Tag El Din
Summary: This study conducted in Lima, Peru utilized a combination of spatial decision-making system and machine learning to identify potential sites for solar power plant construction. Data on solar radiation, precipitation, temperature, and altitude were collected through sundial measurements. The Gene Expression Programming (GEP) and Artificial Neural Networks (ANN) were used to predict the locations, with GEP proving to be the most suitable network with a test state's Nash-Sutcliffe efficiency (NSE) of 0.90 and root-mean-square error (RMSE) of 0.04. The final map based on the GEP model showed that 9.2% of the study area is suitable for construction, while the ANN model indicated only 1.7% suitability.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Energy & Fuels
Caroline Hachem-Vermette, Kuljeet Singh Grewal
Article
Construction & Building Technology
Kuljeet Singh, Caroline Hachem-Vermette
BUILDING AND ENVIRONMENT
(2019)
Article
Thermodynamics
Abhishek Kumar, Kuljeet Singh, Ranjan Das
APPLIED THERMAL ENGINEERING
(2019)
Article
Construction & Building Technology
Caroline Hachem-Vermette, Kuljeet Singh
SUSTAINABLE CITIES AND SOCIETY
(2019)
Article
Thermodynamics
Kuljeet Singh, Caroline Hachem-Vermette
Article
Construction & Building Technology
Caroline Hachem-Vermette, Kuljeet Singh
ENERGY AND BUILDINGS
(2019)
Article
Thermodynamics
Lal Kundan, Kuljeet Sing
Summary: An attempt was made to improve the heat transfer characteristics of the vapor compression refrigeration cycle using nanorefrigerant (R134a and Al2O3), which led to improved performance parameters, increased cooling capacity, reduced energy consumption, and more significant temperature changes.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY
(2021)
Article
Green & Sustainable Science & Technology
Caroline Hachem-Vermette, Kuljeet Singh
Summary: This study compares the energy performance and resources between traditionally built urban development building clusters and neighborhood units designed with sustainable principles. The findings suggest that diverse neighborhood units perform better in terms of energy consumption, with photovoltaic generation fulfilling a significant portion of the electrical energy demand.
Article
Construction & Building Technology
Kuljeet Singh, Caroline Hachem-Vermette
Summary: This research presents a novel open-source methodology for building cluster and neighborhood simulation by integrating individual archetypes building energy models. The methodology successfully merges various building Energy Plus models and can be implemented using programming languages such as Matlab or Python. The proposed workflow is validated for different building clusters and shows a maximum deviation of +/- 2.70% compared to individual BEM simulations. A sensitivity analysis is also performed to quantify the deviation between cluster and individual BEM simulations. The significance index suggests that building cluster simulations yield more adequate results than individual BEM simulations, with the highest significance found for high-rise building clusters.
ENERGY AND BUILDINGS
(2022)
Article
Green & Sustainable Science & Technology
Caroline Hachem-Vermette, Kuljeet Singh
Summary: This study developed building cluster archetypes and optimized their energy resources mix. The findings show that the optimal mix of energy sources varies depending on the type and density of the clusters, and energy sharing can reduce waste and resource requirements.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Energy & Fuels
Kuljeet Singh, Caroline Hachem-Vermette
Summary: This work proposes a technique to enhance the infrastructure and energy resilience of new developments during the planning stage. It introduces several parameters to quantify resilience and suggests solutions such as relocating populations and designing onsite energy resources to eliminate vulnerabilities. The results demonstrate significant improvements in both infrastructure and energy resilience.
Article
Thermodynamics
Eric Swayze, Kuljeet Singh
Summary: The present paper aims to develop a generalized decision-making framework for estimating the optimal sizing and economic and environmental viability of natural gas and solar-based trigeneration systems. It has been demonstrated on eight different building types in five North American locations. The proposed decision-making method can be applied to other locations to determine the most viable building applications for solar and natural gas-based trigeneration systems.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Caroline Hachem-Vermette, Kuljeet Singh
Summary: This research explores the impact of neighbourhood design on reducing energy vulnerability during power disruptions. It analyzes the architectural design and energy characteristics of buildings to identify potential local shelters for evacuations and available renewable energy sources during outages. Sustainable neighbourhoods in North American urban developments are considered, and various response scenarios are designed and analyzed. The study highlights the importance of neighbourhood design in different scenarios and provides recommendations for enhancing resilience, including prioritizing buildings with lower energy intensity for evacuations and adjusting shelter design guidelines.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Multidisciplinary Sciences
Zeeshan Haydar, Travis. J. Esau, Aitazaz. A. Farooque, Qamar. U. Zaman, Patrick. J. Hennessy, Kuljeet Singh, Farhat Abbas
Summary: A deep learning-supported machine vision control system has been developed to detect the height of wild blueberries and precisely adjust the harvester's head. The system performed well in weed-free areas but further work is required for weedy sections of the fields.
SCIENTIFIC REPORTS
(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.