Article
Green & Sustainable Science & Technology
Xin Wang, Jason Atkin, Najmeh Bazmohammadi, Serhiy Bozhko, Josep M. Guerrero
Summary: This paper introduces a system-level centralized operation management strategy based on MPC for MEA, aiming to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements, to maintain energy storage and prolong battery life cycle, with fewer switching activities to improve devices lifetime and avoid unnecessary transients.
Article
Energy & Fuels
Mathieu Lambert, Rachid Hassani
Summary: This paper proposes a new model for real-time diesel genset optimal dispatch and unit commitment in remote microgrids. The objective is to reduce fuel consumption while considering constraints specific to gensets. The model is deterministic and is a mixed-integer linear programming optimization problem. Four case studies were conducted to demonstrate the correct behavior of the model. The results show that the model effectively reduces fuel consumption by 4.3% compared to actual dispatch.
Article
Computer Science, Information Systems
Pedro Balaguer-Herrero, Jose Carlos Alfonso-Gil, Camilo Itzame Martinez-Marquez, German Martinez-Navarro, Salvador Orts-Grau, Salvador Segui-Chilet
Summary: This paper proposes a two-scale optimization algorithm (TSOA) for Model Predictive Control (MPC) aiming at solving resource optimization problems. The algorithm calculates the optimal resources at the first scale using a linear program, and deploys these resources at the second scale using a Mixed Integer Linear Program (MILP). Simulation results demonstrate the computational advantages of the proposed algorithm compared to direct problem discretization and optimization.
Article
Automation & Control Systems
Tobia Marcucci, Russ Tedrake
Summary: This article investigates how computations performed at one time step can be reused to speed up the solution to subsequent MIQPs in hybrid model predictive control, reducing the online computation burden.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Xu Cai, Xin Zhang, Xuyang Lou, Wei Wu
Summary: This paper presents a model predictive control strategy for piecewise affine systems with dead zone constraints using the mixed logical dynamical modeling approach. The proposed strategy involves transforming the systems into mixed logic dynamical models and applying a predictive control scheme based on this model. The effectiveness of the approach is demonstrated through numerical examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Robotics
Abhishek Cauligi, Preston Culbertson, Edward Schmerling, Mac Schwager, Bartolomeo Stellato, Marco Pavone
Summary: This study proposes a data-driven algorithm CoCo for quickly finding high quality solutions to MICPs through training a neural network classifier and applying logical strategies.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Robert A. E. Zidek, Ilya V. Kolmanovsky, Alberto Bemporad
Summary: This paper introduces a stochastic MPC model using a scenario tree to maximize the average time until constraint violation in a linear system, providing feedback by recalculating MILP solutions. The average time until constraint violation achieved by the SMPC strategy approaches the optimal value as the scenario tree density is increased.
Article
Operations Research & Management Science
Jacek Gondzio, E. Alper Yildirim
Summary: This paper investigates how to reformulate a standard quadratic program as a mixed integer linear programming problem, proposing two alternative formulations. By utilizing binary variables and valid inequalities, the formulations significantly outperform other global solution approaches in extensive computational results.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Energy & Fuels
Jessica Alice A. Silva, Juan Camilo Lopez, Nataly Banol Arias, Marcos J. Rider, Luiz C. P. da Silva
Summary: This paper proposes a stochastic mixed-integer nonlinear programming model for the optimal energy management system of unbalanced three-phase AC microgrids. The model considers random variables such as nodal demands, renewable generation, and voltage reference, aiming to provide resilient solutions via contingency constraints. By transforming the model into a mixed-integer linear programming model, it can be solved using off-the-shelf convex programming solvers. Tests on real microgrid data show that the model produces resilient day-ahead energy management solutions while minimizing costs and maximizing the use of renewable energy sources.
Article
Energy & Fuels
Laura Maier, Marius Schoenegge, Sarah Henn, Dominik Hering, Dirk Mueller
Summary: Model predictive control can reduce heating systems' operating costs and energy consumption, especially for heat pumps. This study develops two different air-source heat pump modeling approaches using the supply temperature as a control variable and compares them with a simplified linear model. The results show that both the piecewise linear model and the quadratic model have lower operating costs and energy demand compared to the simplified linear model, but they require longer computation times. Future work is recommended to apply this method to other types of heat pumps and coupled building energy systems to further validate its feasibility.
Article
Automation & Control Systems
Jiao Liu, Yong Wang, Bin Xin, Ling Wang
Summary: This article proposes a two-phase method based on biobjective optimization to address the issue of local convergence caused by integer restrictions in mixed-integer programming problems. By utilizing a measure function and removing integer restrictions, the MIP problem is transformed into a constrained biobjective optimization problem, leading to better solutions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Rubens J. M. Afonso, Roberto K. H. Galvao
Summary: This note addresses the problem of crossing a target set between sample instants under the influence of bounded unknown disturbances. The proposed solution utilizes mixed-integer linear programming and is less conservative compared to the standard approach of imposing pointwise-in-time constraints at the sample instants.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Review
Engineering, Electrical & Electronic
Faria Kamal, Badrul Chowdhury
Summary: This paper provides a comprehensive review of model predictive control (MPC) in networked microgrids (MGs) and highlights its application in grid-level control. From voltage regulation and frequency control to power flow management and economic optimization, MPC has emerged as a promising alternative to traditional methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Bahriye Akay, Dervis Karaboga, Beyza Gorkemli, Ebubekir Kaya
Summary: This paper reviews the use of Artificial Bee Colony algorithm for solving discrete numeric optimization problems, discussing various encoding types, search operators and selection operators integrated into ABC. It is the first comprehensive survey study on this topic and aims to benefit readers interested in utilizing ABC for binary, integer and mixed integer discrete optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Energy & Fuels
Yichen Zhang, Chen Chen, Tianqi Hong, Bai Cui, Zhe Xu, Bo Chen, Feng Qiu
Summary: Integration of grid supportive modes in inverter-based resources can enhance the frequency response of renewable-rich microgrids. To ensure frequency trajectory constraints under predefined disturbances, synthesis of grid supportive modes is challenging yet crucial. A numerical optimal control (NOC)-based control synthesis methodology is proposed to address this challenge. Linearized models are employed in control design, with linearization-induced errors analyzed and represented as interval uncertainties. The proposed control is validated on a modified 33-node microgrid, demonstrating its effectiveness and the significance of considering linearization-induced uncertainty.
Article
Automation & Control Systems
Ali Forootani, Massimo Tipaldi, Majid Ghaniee Zarch, Davide Liuzza, Luigi Glielmo
Summary: This paper formulates resource allocation problems via a set of parallel birth-death processes (BDP), integrating them into one Markov decision process to investigate revenue management as a stochastic decision-making problem. Stochastic Dynamic Programming is employed to solve the related optimization problem with the support of a Matlab-based application, and several simulations are performed to prove the effectiveness of the proposed model and optimization approach.
INTERNATIONAL JOURNAL OF CONTROL
(2021)
Article
Energy & Fuels
Muhammad Faisal Shehzad, Mainak Dan, Valerio Mariani, Seshadhri Srinivasan, Davide Liuzza, Carmine Mongiello, Roberto Saraceno, Luigi Glielmo
Summary: This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings, showing around 25-30% energy savings compared to a meta-heuristic genetic algorithm approach.
Article
Engineering, Electrical & Electronic
Tianqiao Zhao, Alessandra Parisio, Jovica Milanovic
Summary: This paper proposes a distributed control strategy for achieving fast frequency response of multiple BESS in low-inertia power systems with high penetration of renewable energy sources. The algorithm considers the locations of BESS and frequency-related constraints to improve cost-effective frequency response and coordinate flexibility of multiple storage systems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Chemistry, Physical
Muhammad Bakr Abdelghany, Muhammad Faisal Shehzad, Davide Liuzza, Valerio Mariani, Luigi Glielmo
Summary: Efficient energy production and consumption are crucial for reducing carbon emissions that impact climate change. The integration of renewable energy sources in electricity grids can help reduce environmental impact. Proper design and operations are essential for achieving optimal performance from hybrid systems. The paper presents a model predictive controller for grid-connected wind farms with hydrogen-based energy storage systems, considering operating and economical costs, local load demand, participation in the electricity market, and enforcing physical and system dynamics constraints. The controller is designed to handle various operating modes of the hydrogen-based energy storage system and reduce management costs while increasing equipment lifespan. A case study in north Norway demonstrates the effectiveness of the proposed strategy in managing the plant and preserving equipment from unnecessary commutation cycles.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Editorial Material
Energy & Fuels
Tomislav Dragicevic, Alessandra Parisio, Jose Rodriguez, Colin N. Jones, Daniel E. Quevedo, Luca Ferrarini, Matthias Preindl, Qobad Shafiee, Thomas Morstyn
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2021)
Article
Energy & Fuels
Mateo Beus, Matej Krpan, Igor Kuzle, Hrvoje Pandzic, Alessandra Parisio
Summary: The paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system, focusing on the development of the upper control level for frequency control. Two simulation case studies were conducted to assess the performance of the proposed control framework, with results showing that the Model Predictive Control (MPC) algorithm outperforms conventional PID controllers in controlling the UC bank for frequency control.
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2021)
Article
Automation & Control Systems
Tianqiao Zhao, Alessandra Parisio, Jovica V. Milanovic
Summary: This paper presents a control framework that enables distributed battery energy storage systems to track voltage setpoints in an optimal and coordinated manner. The framework uses an online convex optimisation framework to ensure scalability and timely service provision. The proposed approach adapts to time-varying network conditions and fulfils technical operating requirements.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Automation & Control Systems
V Mariani, G. Rizzo, F. A. Tiano, L. Glielmo
Summary: This paper presents the design of an MPC strategy for maximizing regenerative braking in a hybridized vehicle. The strategy utilizes a kit that includes in-wheel motors, PV panels, and a battery pack. The simulation results show the effectiveness of the strategy, recovering up to approximately 18% of the vehicle's kinetic energy, and suggest its potential as a baseline for implementing additional regenerative braking algorithm onto the real vehicle.
CONTROL ENGINEERING PRACTICE
(2022)
Review
Environmental Sciences
Abhaya Pal Singh, Amol Yerudkar, Valerio Mariani, Luigi Iannelli, Luigi Glielmo
Summary: This study presents a bibliometric analysis of the use of unmanned aerial vehicles (UAVs) in precision agriculture, specifically in precision viticulture (PV). The analysis reveals that researchers from the United States, China, Italy, and Spain are leading the application of UAVs in precision agriculture, with Italian researchers expanding their work in PV. The study also provides comprehensive information on popular journals, funding organizations, nations, institutions, and authors in this field.
Article
Energy & Fuels
Valerio Mariani, Federico Zenith, Luigi Glielmo
Summary: This paper proposes a Model Predictive Control algorithm to operate a Hydrogen-based Energy Storage System paired with a wind farm for smooth power injection. The algorithm utilizes advanced models and cost functions to optimize the performance and profitability. Numerical simulations demonstrate the effectiveness of the algorithm and provide insights into device sizing.
Article
Chemistry, Analytical
Abhaya Pal Singh, Amol Yerudkar, Davide Liuzza, Yang Liu, Luigi Glielmo
Summary: Efficient nitrogen management is crucial in modern agriculture for improving crop yield and reducing environmental impact. Developing an optimal decision support system can help maintain a balance between nitrogen supply and crop demand while increasing crop yield.
Article
Engineering, Electrical & Electronic
Zirong Zhou, Yang Liu, Jianquan Lu, Luigi Glielmo
Summary: This study investigates the cluster synchronization problem of Boolean Networks (BNs) under state-flipped control. The problem of cluster synchronization is transformed into a set stabilization problem, and a theorem is provided to determine whether cluster synchronization of BNs can be achieved under a given flip set. Additionally, a model-free reinforcement learning algorithm called Q-learning (QL) is developed to search control sequences for achieving cluster synchronization.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Review
Computer Science, Information Systems
Alexandre Serrano-Fontova, Haiyu Li, Zhiyu Liao, Magnus Rory Jamieson, Rosa Serrano, Alessandra Parisio, Mathaios Panteli
Summary: Power systems are increasingly affected by extreme events, exacerbated by climate change. This paper reviews and classifies fragility curves (FCs) used to model the vulnerability of power system components. Comparison of results obtained using different FCs highlights the importance of their modeling.
Proceedings Paper
Green & Sustainable Science & Technology
Amit Joshi, Hamed Kebriaei, Valerio Mariani, Luigi Glielmo
Summary: Deregulation of the power network facilitates increased decision making autonomy for end-users by integrating renewable energy resources and energy storage systems. Peak shaving, where end-users can play a significant role in improving grid resilience, is highlighted. A decentralized control algorithm is proposed for a grid-connected community microgrid to achieve equilibrium strategy in peak shaving.
2021 IEEE MADRID POWERTECH
(2021)
Proceedings Paper
Automation & Control Systems
Xiao Wang, Tongmao Zhang, Alessandra Parisio
Summary: This paper presents a distributed predictive control framework coordinating battery energy storage systems and HVAC systems in the distribution network for the provision of ancillary services to the power grid, while maintaining indoor thermal comfort and network voltage within acceptable limits. The optimization problem formulated for coordinating virtual storage plants (VSPs) is solved in a distributed manner using the ADMM method, demonstrating the effectiveness of the proposed approach in delivering the required services under system constraints.
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.
