Article
Chemistry, Multidisciplinary
Wei Lou, Shenglong Zhu, Jinjin Ding, Taiyun Zhu, Ming Wang, Licheng Sun, Feili Zhong, Xiaodong Yang
Summary: This paper proposes a transparent demand-response framework for the adaptation of a high proportion of renewable energy in a smart grid, enabling the proactive participation of demand-side flexible multi-energy resources.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Analytical
Hafiz Abdul Muqeet, Haseeb Javed, Muhammad Naveed Akhter, Muhammad Shahzad, Hafiz Mudassir Munir, Muhammad Usama Nadeem, Syed Sabir Hussain Bukhari, Mikulas Huba
Summary: A microgrid is a distributed generation system connected to AC, DC, or hybrid loads and energy storage systems. Campus microgrids, which usually include distributed generation resources, energy storage, and electric vehicles, are an important load type. The main objective of a microgrid is to provide a sustainable, economical, and reliable energy system. Numerous studies have been conducted in recent years to review various aspects of microgrid energy management systems, such as energy sustainability, demand response strategies, and control systems.
Article
Chemistry, Analytical
Li Bin, Muhammad Shahzad, Haseeb Javed, Hafiz Abdul Muqeet, Muhammad Naveed Akhter, Rehan Liaqat, Muhammad Majid Hussain
Summary: This article proposes a strategic proposition for an energy management system for a campus microgrid to minimize operating costs and increase self-consuming energy. The simulation results show that the proposed model significantly reduces electricity costs for the microgrid.
Article
Engineering, Electrical & Electronic
Bala Sai Kiran Patnam, Naran M. Pindoriya
Summary: This article provides an overview of mathematical modeling and optimization of demand response algorithms, categorizing literature into single home, aggregated home, network level, and market-level demand response. It discusses the mathematical formulation and associated issues at each level, as well as detailed information on modeling, implementation, and uncertainty handling in DR. Additionally, it explores the integration of battery energy storage systems, electric vehicles, and renewable energy sources in the context of demand response.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Energy & Fuels
Amin Amin, Oudom Kem, Pablo Gallegos, Philipp Chervet, Feirouz Ksontini, Monjur Mourshed
Summary: The research team developed a cloud-based framework to optimize electricity consumption and generation in buildings, aiming to improve the participation of EU households in demand response programs. Through electro-thermal simulations and consideration of low-voltage grid constraints, their approach demonstrated success in real-world testing.
Article
Chemistry, Multidisciplinary
Fahad R. Albogamy
Summary: This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal energy management (EM). The ECS, based on the GWDO algorithm, determines the optimal operation schedule of household appliances and batteries charge/discharge. Simulation results validate the applicability of the proposed model in EM problems.
APPLIED SCIENCES-BASEL
(2023)
Article
Thermodynamics
Sarah O'Connell, Glenn Reynders, Marcus M. Keane
Summary: This paper evaluates the quality of demand response services, quantifies uncertainty for multiple systems, and assesses the variability of flexibility provided by different sources. The study found that aggregation of data reduced uncertainty for energy services and introduced the concept of flexibility quality, highlighting the differences in flexibility provided by various sources. Overall, the impact on occupant comfort was minimal.
Article
Green & Sustainable Science & Technology
Mengmeng Zhao, Xiaoying Wang, Junrong Mo
Summary: By connecting geographically distributed datacenters, workload and energy consumption can be balanced to reduce electricity costs. Optimizing workload and energy management strategies, considering factors like renewable energy, can further reduce energy consumption and operating costs, as well as improve the stability of the regional power grid system.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Article
Energy & Fuels
S. Balavignesh, C. Kumar, Soichiro Ueda, Tomonobu Senjyu
Summary: The introduction of advanced technologies has led to a significant increase in automated appliances in the housing sector. Building new administrative structures to meet electrical needs has become crucial for ensuring the safety of residential devices. Demand Side Management (DSM), a key component of micro-grid and Smart Grid technology, is one approach to achieve this. DSM involves carefully controlling requirements while maintaining client trust. Most of the research in DS management focuses on helping households manage their power plans.
Article
Energy & Fuels
Kalim Ullah, Quanyuan Jiang, Guangchao Geng, Rehan Ali Khan, Sheraz Aslam, Wahab Khan
Summary: This research analyzes the reconfiguration of microgrid-based distribution networks in the smart distribution grid, considering demand response programs and power-sharing to optimize costs and reduce power losses. The ideal distribution network configuration and interconnection switches between microgrids and the main grid are determined. The results show that using demand response programs and power-sharing can effectively reduce costs and power losses.
Article
Green & Sustainable Science & Technology
Fahad R. Albogamy, Ghulam Hafeez, Imran Khan, Sheraz Khan, Hend Alkhammash, Faheem Ali, Gul Rukh
Summary: The study introduces an efficient energy management model based on ant colony optimization algorithm, which reduces energy costs, alleviates peak to average ratio, and decreases carbon emissions by scheduling loads and charging/discharging electric vehicles.
Article
Green & Sustainable Science & Technology
Xiaomin Wu, Weihua Cao, Dianhong Wang, Min Ding, Liangjun Yu, Yosuke Nakanishi
Summary: An improved optimization model for demand response in a remote off-grid microgrid in Dongfushan Island, China is proposed in this study. The model considers different electricity prices under different seasonal meteorological conditions, aiming to develop energy dispatch and economic benefits. By using an improved Pareto optimum and distributed learning algorithm, the model maximizes satisfaction, reduces electricity bills for consumers, and increases profits for retailers. Simulation results show that the proposed method can lower electricity bills for consumers and help retailers save on generation costs and increase renewable energy utilization.
Article
Construction & Building Technology
Mahmoud M. Gamil, Tomonobu Senjyu, Hiroshi Takahashi, Ashraf M. Hemeida, Narayanan Krishna, Mohammed Elsayed Lotfy
Summary: This study introduces four sizing scenarios of a residential microgrid in a northern Egyptian city surrounded by rural areas to explore the optimal scheduling strategy, comparing techno-economic and ecological performance of different sizing strategies based on renewable sources. The proposed optimization algorithm shows that the PV/WG/Biomass/two-ways-grid-connection microgrid is the best investable-reliable sizing option with reduced costs and significant environmental impact.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Energy & Fuels
Muhammed Ali Beyazit, Akin Tascikaraoglu, Joao P. S. Catalao
Summary: This study proposes an energy management approach that utilizes an energy credit mechanism to store excess PV energy in a shared energy storage system and exploit vehicle batteries. The proposed approach effectively reduces energy costs and peak demand.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Green & Sustainable Science & Technology
G. M. Cabello, S. J. Navas, I. M. Vazquez, A. Iranzo, F. J. Pino
Summary: This paper reviews the ongoing research studies and microgrid pilot projects in Spain, focusing on the renewable energy potential in the country. It highlights the main investigation trends in the field, including the use of technologies, control methods, and operational challenges. The study finds that photovoltaic and wind power are the most favored energy generation technologies, and batteries are the most widely used energy storage systems. Advanced control strategies such as MPC or fuzzy logic are replacing traditional strategies for higher efficiency. The paper provides a comprehensive analysis of the potential of renewable energy penetration through smart grids in Spain, examining the equipment and control strategies implemented in various facilities.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Engineering, Environmental
Coen van der Giesen, Christoph J. Meinrenken, Rene Kleijn, Benjamin Sprecher, Klaus S. Lackner, Gert Jan Kramer
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2017)
Article
Computer Science, Interdisciplinary Applications
Ali Mehmani, Souma Chowdhury, Christoph Meinrenken, Achille Messac
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2018)
Article
Engineering, Electrical & Electronic
Sanjmeet Abrol, Ali Mehmani, Mark Kerman, Christoph J. Meinrenken, Patricia J. Culligan
PROCEEDINGS OF THE IEEE
(2018)
Article
Electrochemistry
Menglian Zhen, Jie Sun, Christoph J. Meinrenken, Tao Wang
JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE
(2019)
Article
Energy & Fuels
Christoph J. Meinrenken, Ali Mehmani
Article
Energy & Fuels
Yonghua Song, Yi Ding, Pierluigi Siano, Christoph Meinrenken, Menglian Zheng, Goran Strbac
Article
Environmental Studies
Christoph J. Meinrenken, Zhenyu Shou, Xuan Di
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2020)
Article
Multidisciplinary Sciences
Christoph J. Meinrenken, Daniel Chen, Ricardo A. Esparza, Venkat Iyer, Sally P. Paridis, Aruna Prasad, Erika Whillas
SCIENTIFIC REPORTS
(2020)
Article
Construction & Building Technology
Christoph J. Meinrenken, Sanjmeet Abrol, Gaurav B. Gite, Christopher Hidey, Kathleen McKeown, Ali Mehmani, Vijay Modi, Elsbeth C. Turcan, Wanlin Xie, Patricia J. Culligan
Summary: Research has shown that feedback on electricity usage can reduce demand, with self-comparisons and high variety in feedback messages being the most effective in prompting reductions. Residents tend to conform to their neighbors' average usage, rather than exhibiting anti-conform boomerang behavior.
ENERGY AND BUILDINGS
(2021)
Article
Energy & Fuels
Lechen Li, Christoph J. Meinrenken, Vijay Modi, Patricia J. Culligan
Summary: This study develops a neural network model based on Convolutional Long Short-Term Memory, employing autoregressive features selection, exogenous features selection, and a default state to improve short-term residential electricity load forecasting accuracy. The model demonstrates up to 25% accuracy improvement in forecasting load across three spatial granularities in a multi-family residential building.
Article
Construction & Building Technology
Lechen Li, Christoph J. Meinrenken, Vijay Modi, Patricia J. Culligan
Summary: This study analyzed the impact of COVID-19 pandemic on residential electricity usage in New York City, predicting potential increases in electricity consumption under stay-at-home orders and warm summer weather. The study also highlighted the potential grid management challenges that could arise from the projected increase in peak demand.
ENERGY AND BUILDINGS
(2021)
Article
Multidisciplinary Sciences
Christoph J. Meinrenken, Daniel Chen, Ricardo A. Esparza, Venkat Iyer, Sally P. Paridis, Aruna Prasad, Erika Whillas
Summary: Product carbon footprints (PCFs) are increasingly important for sustainability decisions by companies and consumers. Life cycle assessment (LCA) helps companies achieve greater carbon reductions by improving the product's value chain. The Carbon Catalogue provides detailed information on greenhouse gas emissions and meta data for each product in the dataset.
Article
Multidisciplinary Sciences
Christoph J. Meinrenken, Noah Rauschkolb, Sanjmeet Abrol, Tuhin Chakrabarty, Victor C. Decalf, Christopher Hidey, Kathleen McKeown, Ali Mehmani, Vijay Modi, Patricia J. Culligan
Article
Green & Sustainable Science & Technology
Yash Amonkar, Nafisa Chowdhury, Yiran Song, Jane Siyuan Wu, Parth Vaidya, Christoph J. Meinrenken
JOURNAL OF ENVIRONMENTAL ACCOUNTING AND MANAGEMENT
(2019)
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.