Article
Green & Sustainable Science & Technology
Peilin Wang, Wenlin Yuan, Chengguo Su, Yang Wu, Lu Lu, Denghua Yan, Zening Wu
Summary: This study establishes an optimization model for the short-term generation scheduling of cascade hydropower plants in regional power grids. The model aims to minimize the peak-valley load difference of multiple power grids by considering hydraulic, electrical, and head constraints. The results of a real-world case study show that the proposed model is computationally efficient and performs well in peak shaving for multiple provincial power grids.
Article
Green & Sustainable Science & Technology
Hever Alcahuaman, Juan Camilo Lopez, Daniel Dotta, Marcos J. Rider, Scott Ghiocel
Summary: This paper proposes a methodology to optimize the reactive power capability of WPP, considering uncertain levels of wind power generation and voltage magnitudes at the PCC. The methodology determines the configuration of tap-changing transformers within the WPP to maximize the amount of reactive power that the WPP can either consume or inject to the network. Results show that the proposed MILP model is a scalable, flexible and accurate method for maximizing the reactive power capability of WPP.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Automation & Control Systems
Farshad Merrikh-Bayat, Parvin Mirhoseini
Summary: This paper studies the control of multi-input multi-output hybrid nonlinear dynamic systems. A theorem is presented and proved, which represents the necessary and sufficient condition for achieving uniform convergence of the state vector sequence towards the desired point. Based on this theorem, a linear programming method is proposed for calculating the control variables. Two well-known nonlinear hybrid control problems with multiple inputs are solved using the proposed method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Thermodynamics
H. Terfa, L. Baghli, R. Bhandari
Summary: This research proposes using distributed renewable energy micro-power plants to provide electricity in African countries. The laboratory is developing four types of micro-power plants, including wind turbine emulators and photovoltaic systems. The data flow between the micro-power plants and the simulated power grid is ensured through the use of embedded processors and a Firebase database.
Article
Energy & Fuels
Faegheh Moazeni, Javad Khazaei
Summary: The research investigates the economic relationship between wastewater treatment plants and smart grids, developing a cooperative optimization model to solve the economic dispatch problem of smart grids while considering the energy needs and effluent quality of wastewater treatment plants. Through case studies, it was found that the total operational cost and energy consumption of the integrated WWTP-smart grid only slightly increase as the wastewater flow rate and influent concentrations increase from minimum to maximum values.
Article
Chemistry, Physical
Awsan Mohammed, Ahmed M. Ghaithan, Ahmad Al-Hanbali, Ahmed M. Attia
Summary: In this paper, a multi-objective mixed-integer linear programming model is developed to design a hybrid PV-hydrogen renewable energy system considering minimizing total life costs and loss probability of power supply as the objective functions. The model is validated using an electrical testing lab in Saudi Arabia with hourly power demand. The results indicate the feasibility of combining an electrolyzer, hydrogen tank storage, and fuel cell with a renewable energy system, but the cost of energy generated is still high. Additionally, the findings demonstrate the potential of using hydrogen technologies as an energy storage solution for intermittent renewable energy systems and in various applications.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Energy & Fuels
Yasoda Kailasa Gounder, Sowkarthika Subramanian
Summary: This research presents an optimization method for residential community microgrid, which includes minimizing operating cost and emissions using mixed integer linear programming algorithm, creating flexible generation-demand model with demand response, and scheduling household appliances with a special knapsack method. The results demonstrate that this method can meet user demands and reduce operating costs.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Evangelos E. Pompodakis, Georgios C. Kryonidis, Emmanuel S. Karapidakis
Summary: This paper proposes a comprehensive optimization approach based on linear programming for the installation of hybrid power plants in non-interconnected islands. The approach considers the size, location, and technology of each power plant, and includes system constraints to ensure the secure operation of the grid. The method is validated in a real Greek Island.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
L. M. Leon, D. Romero-Quete, N. Merchan, C. A. Cortes
Summary: This paper presents an improved methodology for optimal sizing of small-scale microgrids with PV generation systems and hybrid energy storage systems. The methodology includes a new approach using battery aging model to determine replacement timing and allowing the inclusion of supercapacitors to reduce battery degradation. Testing the tool on microgrids for a hospital and a government building in Colombia demonstrated its usefulness in microgrid design.
Article
Thermodynamics
Wenlin Yuan, Xinqi Wang, Chengguo Su, Chuntian Cheng, Zhe Liu, Zening Wu
Summary: The paper proposed a coordination mode between a PV plant and a large-capacity hydropower plant, and utilized a stochastic optimization model to promote renewable energy consumption, achieving joint operation of a hydro-PV system.
Article
Mathematics, Applied
Kurt M. Anstreicher
Summary: This method utilizes the computational power of modern MILP solvers to test if a given matrix is copositive by solving a single mixed-integer linear programming problem. Numerical experiments demonstrate that the method is robust and efficient.
LINEAR ALGEBRA AND ITS APPLICATIONS
(2021)
Article
Computer Science, Software Engineering
Luca Pitzalis, Marco Livesu, Gianmarco Cherchi, Enrico Gobbetti, Riccardo Scateni
Summary: Conforming hexahedral meshes are preferred for simulation tasks due to their numerical properties, but automatic decomposition into a small number of hexahedral elements is challenging. Current methods rely on adaptive Cartesian grids and additional refinement rules for octrees to meet compatibility conditions and create conforming meshes. A novel approach introduced in this article formulates compatibility conditions as linear constraints in an integer programming problem, allowing for a broader solution space and meeting hexmeshing criteria at a coarser scale than octree-based methods.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Management
Enrique Benavent, Angel Corberan, Demetrio Lagana, Francesca Vocaturo
Summary: In this article, the authors focus on the periodic rural postman problem with irregular services (P RP P-IS). They propose a two-phase algorithm that combines heuristics and mathematical programming to solve this problem. The first phase uses two different procedures to construct feasible solutions, while the second phase combines the fragments of these solutions to determine a solution for the P RP P-IS. Extensive experiments are conducted to demonstrate the effectiveness of this approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Energy & Fuels
John L. Cox, William T. Hamilton, Alexandra M. Newman
Summary: There is growing interest in utility-scale solar power plants with storage that can dispatch renewable energy to the grid flexibly. Plant design has many degrees of freedom and hidden trade-offs in performance. Software tools can estimate or optimize the performance of a specific plant configuration under market and weather conditions, and the associated cost parameters and operating assumptions strongly affect plant performance estimates and decisions on optimal sizing. The study investigates the sensitivity to weather and market conditions, operating limitations, and capacity-based incentives using the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform. The results demonstrate changes in plant performance and optimal sizing based on these inputs and discuss their implications.
Article
Engineering, Electrical & Electronic
Willem Lambrichts, Mario Paolone
Summary: In this paper, an exact and linear measurement model for hybrid AC/DC micro-grids is proposed for recursive state estimation. The model includes an exact linear model of a voltage source converter and accounts for various losses. Synchronized measurements are provided by phasor measurement units and DC measurement units. An adaptive Kalman Filter is also proposed to improve the resilience and speed of state estimation.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Energy & Fuels
Gabriele Roggi, Alessandro Niccolai, Francesco Grimaccia, Marco Lovera
Article
Mathematics
Alessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Alessandro Gandelli, Riccardo Zich
Article
Energy & Fuels
Amir Mohammad Moradi Sizkouhi, Mohammadreza Aghaei, Sayyed Majid Esmailifar, Mohammad Reza Mohammadi, Francesco Grimaccia
IEEE JOURNAL OF PHOTOVOLTAICS
(2020)
Article
Energy & Fuels
Alessandro Niccolai, Alberto Dolara, Emanuele Ogliari
Summary: This article discusses the application of hybrid methods combining physical models and neural networks in photovoltaic prediction, comparing the results of different approaches and finding that hybrid methods have good forecasting effectiveness.
Article
Computer Science, Information Systems
Alberto Dolara, Sonia Leva, Giacomo Moretti, Marco Mussetta, Yales Romulo de Novaes
Summary: Electric mobility is a game changing technology for the long-term sustainability of the transportation sector. A model to simulate an Electric Vehicle for Formula SAE Electric competition has been proposed, with the implementation of all subsystems and hybrid storage of Li-ion batteries and Ultra-Capacitors for kinetic energy recovery. The use of a bidirectional DC-DC resonant converter for the Kinetic Energy Recovery System (KERS) is discussed, showing operational limits and the capability to operate under resonant mode in both boost and buck mode.
Article
Energy & Fuels
Alberto Dolara, Giulia di Fazio, Sonia Leva, Giampaolo Manzolini, Riccardo Simonetti, Andrea Terenzi
Summary: Organic photovoltaic modules have advantages over conventional PV technologies, but their performance under real environmental conditions still lags behind silicon-based modules.
IEEE JOURNAL OF PHOTOVOLTAICS
(2021)
Article
Mathematics
Alessandro Niccolai, Davide Caputo, Leonardo Chieco, Francesco Grimaccia, Marco Mussetta
Summary: This study focuses on the implementation of an automated inspection system in aerospace manufacturing using vision-based expert systems. By capturing images from different angles and applying machine learning architectures for defect detection, the reliability and accuracy of evaluations on aircraft parts have been enhanced.
Article
Mathematics
Alessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Riccardo Zich, Alessandro Gandelli
Summary: Reflectarray antennas are low-profile high-gain systems widely used in the aerospace industry. The design complexity and the need for high scanning capabilities have led to the development of an optimization environment that can be applied with evolutionary optimization algorithms.
Article
Energy & Fuels
Francesco Grimaccia, Marco Montini, Alessandro Niccolai, Silvia Taddei, Silvia Trimarchi
Summary: The aim of this study is to develop a model for a proprietary SO2 removal technology using machine learning techniques and artificial neural networks. The results show that the targets can be accurately predicted with a purely data-driven approach, providing an optimal control strategy for managing the SO2 removal system and maximizing plant productivity.
Article
Chemistry, Multidisciplinary
Francesco Grimaccia, Alessandro Niccolai, Marco Mussetta, Giuseppe D'Alessandro
Summary: This paper proposes an energy management system based on an Artificial Neural Network (ANN) to enhance the energy baselines in thermal power plants. It utilizes artificial intelligence techniques to analyze real electrical absorption data and track inefficiencies. The results show that the neural technique efficiently defines more accurate energy baselines and provides a valuable tool for managing large energy plant assets.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Carla Sahori Seefoo Jarquin, Alessandro Gandelli, Francesco Grimaccia, Marco Mussetta
Summary: Understanding the changes in energy consumption and their causes and timing is important for power network decision makers, and this is where energy forecasting plays a crucial role. This research proposes a probabilistic approach to capture the spatial, temporal, and probabilistic dimensions of energy forecasts. Different models based on artificial neural networks are used to generate forecasts, and the singular value decomposition technique is used to generate temperature scenarios for the probabilistic forecast. The methodology is applied to energy demand forecasts in university campus buildings, but can also be extended to energy communities.
Review
Energy & Fuels
Ana Cabrera-Tobar, Francesco Grimaccia, Sonia Leva
Summary: As telecommunication networks play an increasingly critical role in societal functioning, ensuring their resilience in the face of energy disruptions is crucial. This review paper comprehensively analyzes the strategies and challenges associated with achieving energy resilience in telecommunication networks and discusses how these strategies can be implemented and the challenges involved.
Article
Computer Science, Information Systems
Enrico Giglio, Gabriele Luzzani, Vito Terranova, Gabriele Trivigno, Alessandro Niccolai, Francesco Grimaccia
Summary: The paper focuses on the development of a methodology for energy management using photovoltaics and storage systems in multi-story buildings. It combines digital solutions to improve energy efficiency and reduce costs while stabilizing the grid.
Article
Multidisciplinary Sciences
Venkataramana Veeramsetty, Dongari Rakesh Chandra, Francesco Grimaccia, Marco Mussetta
Summary: This paper proposes a method that combines Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques to improve the forecasting capability of electric power substations. Based on numerical results, the method accurately predicts loads while reducing the dimensionality of input data, thus minimizing computational effort.
Article
Computer Science, Information Systems
Alvise Baggio, Francesco Grimaccia
