Ma. Del Carmen Toledo-Pérez, Rodolfo Amalio Vargas-Méndez, Abraham Claudio-Sánchez
et al.
In this article, a comprehensive review of electrical microgrids is presented, emphasizing their increasing importance in the context of renewable energy integration. Microgrids, capable of operating in both grid-connected and standalone modes, offer significant potential for providing energy solutions to rural and remote communities. However, the inclusion of diverse energy sources, energy storage systems (ESSs), and varying load demands introduces challenges in control and optimization. This review focuses on hybrid microgrids, analyzing their operational scenarios and exploring various optimization strategies and control approaches for efficient energy management. By synthesizing recent advancements and highlighting key trends, this article provides a detailed understanding of the current state and future directions in hybrid microgrid systems.
Luis Jesús Fernández, Efraim Centeno, Sonja Wogrin
Energy systems increasingly rely on the synergistic operations of the electricity and hydrogen markets pursuing decarbonization. In this context, it is necessary to develop tools capable of representing the interactions between these two markets to understand the role of hydrogen as an energy vector. This paper introduces a bi-level optimization model that captures the interactions between the electricity and hydrogen markets, positioning hydrogen generators as strategic electricity price makers in the power market. The model can be efficiently solved and applied to real-world scenarios by reformulating it as a Mixed Integer Linear Program. The case studies analyze spot market behaviors when hydrogen generators are modeled as price makers in the electricity market. First, single-period simulations reveal the effects of price-making, and next, a year-long simulation assesses broader implications. The findings demonstrate that conventional modeling assumptions, such as the price-taker hydrogen generators in the electricity market and constant production cost hypothesis, lead to non-optimal hydrogen generation strategies that raise electricity prices while reducing the profit of hydrogen generators and the hydrogen market social welfare. These results highlight the need for models that accurately reflect the interdependencies between these two energy markets.
Production of electric energy or power. Powerplants. Central stations
Apriliana Margawadi Kenanga, Widayat, Widodo Agung Suedy Sri
In Indonesia, coal-fired power plants (CFPP) account for nearly 70% of the nation’s electricity supply, contributing significantly to global warming. Biomass co-firing program for CFPP offers a potential solution. This study analyzes the impact of co-firing between blending of Low Rank Coal (LRC) with Medium Rank Coal (MRC) and wood pellet biomass, on the performance of 615 MW capacity CFFP. Using simulations with Cycletempo software, the research examines operational parameters such as plant efficiency, the performance of auxiliary equipment, and a techno-economic analysis focusing on carbon trading. The biomass co-firing tested reached up to 50%, with results indicating that increasing biomass content tends to reduce overall plant efficiency. Results indicate that at 100% load, the plant can accommodate up to 10% biomass co-firing, and at 75% load, it can handle up to 30%. Co-firing beyond 30% requires increased pulverizer power for stable operation. From techno-economic perspective, while co-firing reduces greenhouse gas emissions and generates benefits from carbon trading, the operational costs associated with fuel under co-firing are not yet fully profitable. This study offers guidance on increasing the co-firing ratio as part of the energy transition towards net zero emissions 2060 and provides recommendations for optimizing fuel mixtures in CFFP.
Vedant Singh, Aishwarya V.M., Sriprasath V.J.
et al.
Summary: Green hydrogen (GH) offers a sustainable fuel alternative for addressing global energy and climate goals. This study evaluates GH viability across five dimensions: technological advancement, economic feasibility and market potential, environmental impacts and resources dynamics, policy regulatory frameworks, and interdisciplinary collaborations. Recent advancements in electrolysis technologies and artificial intelligent driven process optimization have enhanced production efficiency and resource management. Despite increasing investments, GH faces challenges such as high electricity demand, water usage, and infrastructure constraints. A regional assessment using the Green Hydrogen Feasibility Index (GHFI) indicates that countries such as China, Germany, and the USA lead in readiness due to robust policies and investment, while others face implementation issues. The finding emphasizes the need for coordinated global regulation, infrastructure development, and digital integration to enhance GH scalability and sustainability. This work contributes a comprehensive framework for assessing GH deployment and highlights strategies to accelerate its role in achieving a net-zero energy future.
An EPD (Environmental Product Declaration) is a document that quantifies the environmental impact of a product throughout its life cycle, including greenhouse gases. Writing an EPD is a complex process involving several steps. Initially, factory production data, including recipe, electricity consumption, waste generation, and water consumption, must be collected. The second step involves inputting this data into a specialized software for a life cycle analysis. The final step is to generate an EPD from the software, which is then verified by a third party. This article will focus on describing the process of creating an EPD and the challenges for our industry. The various stages of a product’s life cycle will be explained, along with a description of the assumptions made during the creation of this type of document. To conclude, two concrete examples of EPDs will be presented, illustrating the drafting of this environmental tool.
Justina Krevnevičiūtė, Arnas Mitkevičius, Darius Naujokaitis
et al.
This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. By analyzing real-world weather scenarios, the approach aims to identify the highest accuracy forecasting model for the short-term 24-h forecast of wind farm power output. A more accurate forecast allows for more efficient resource planning and better distribution of resources on the electricity grids, thus ensuring a greener approach to energy production. The study shows that the proposed Hybrid (XGBoost + LTC) model predicts wind power generation with an nMAE of 0.0856, representing an improvement over standalone XGBoost and LTC models, and outperforming classical approaches such as LSTM and statistical models like ARIMAX in terms of forecasting accuracy.
Integrated Hydrogen–Energy Systems (IHES) have attracted widespread attention; however, distributed energy sources such as photovoltaics (PV) and wind turbines (WT) within these systems exhibit significant uncertainty and intermittency, posing key challenges to scheduling complexity and system instability. As a core mechanism for the integrated operation of IHES, electricity price regulation can promote the absorption of renewable energy, optimize resource allocation, and enhance operational economy. Nevertheless, uncertainties in IHES hinder the formulation of accurate electricity prices, which easily lead to delays in scheduling responses and an increase in cumulative operating costs. To address these issues, this study develops lifespan models for Proton Exchange Membrane Electrolyzers (PEMELs) and Proton Exchange Membrane Fuel Cells (PEMFCs), constructs dynamic equations for the demand side and response side, and proposes a fuzzy-weighted dynamic pricing strategy. Simulation results show that, compared with fixed pricing, the proposed dynamic pricing strategy reduces economic indicators by an average of 15.3%, effectively alleviates energy imbalance, and optimizes the energy supply of components. Additionally, it reduces the lifespan degradation of PEMELs by 21.59% and increases the utilization rate of PEMFCs by 54.8%.
Ultra-high temperature thermal energy storage (UHTES) and conversion is an emerging field of technology that enables much higher energy densities (>1 MWth) and conversion efficiencies than conventional thermal energy storage technologies. Our research group of Solar Energy Institute is currently developing a novel latent heat thermophotovoltaic (LHTPV) battery that utilizes Si-based alloys to store either surplus renewable electricity or concentrated sunlight in the form of latent heat at temperatures close to 1200 ºC and convert it back to electricity on demand. Determining the State of Charge (SoC) of this ultra-high temperature thermal battery is imperative to regulate its real-time operation and optimize its performance. However, using of several sensors within the storage system –as mainly done in low temperature phase change materials (PCMs) to quantify their SoC - becomes costly and challenging for this range of operating conditions. This study presents a numerical method, which is used to get an understanding of the physical processes taking place during the LHTPV operation and capture comprehensive data of time varying flow variables that can be difficult to record during real-time operation. Our results indicate that we can describe the system’s SoC by measuring the time-varying temperature at its sidewalls and the input/output heat flux values, without the need of knowing beforehand the thermophysical properties of the used materials. Based on these variables we can define several indicators that can help us obtain a better understanding of the required physical signals to be measured in order to determine its SoC, during real-time operation.
I define the fields that describe electrical macrostructure, and their rates of change, in terms of the microscopic charge density, electric field, electric potential, and their rates of change. To deduce these definitions, I lay some new foundations of a theory of how observable macroscopic fields are related to spatial averages of their microscopic counterparts. I find that the relationships between macroscopic fields are identical in form to the relationships between their microscopic counterparts, meaning that the $\vec{P}$ and ${\vec{D}}$ fields do not appear in them. Without invoking quantum mechanics, I derive the expressions for polarization current established by the Modern Theory of Polarization. I prove that the bulk-average electric potential, or mean inner potential, vanishes in a macroscopically-uniform charge-neutral material, and I show that when a crystal lattice lacks inversion symmetry, it does not imply the existence of macroscopic $\vec{E}$ or $\vec{P}$ fields in the crystal's bulk. I point out that symmetry is scale-dependent. Therefore, if anisotropy of the microstructure does not manifest as anisotropy of the macrostructure, it cannot be the origin of a macroscopic vector field. The macroscopic charge density vanishes in a material's bulk. Therefore, regardless of the microstructure, a macroscopic $\vec{E}$ field cannot emanate from the bulk. I find that all relationships between observable macroscopic fields can be expressed mathematically without introducing the polarization ($\vec{P}$) and electric displacement ($\vec{D}$) fields, neither of which is observable. I also show that most `quantum mechanical' aspects of the existing microscopic theory of electricity in materials are compatible with, or required features of, a statistical theory of classical particles whose charges and masses are comparable to those of electrons and nuclei.
Oscar Trull, J. Carlos García-Díaz, Angel Peiró-Signes
Transmission system operators have a growing need for more accurate forecasting of electricity demand. Current electricity systems largely require demand forecasting so that the electricity market establishes electricity prices as well as the programming of production units. The companies that are part of the electrical system use exclusive software to obtain predictions, based on the use of time series and prediction tools, whether statistical or artificial intelligence. However, the most common form of prediction is based on hybrid models that use both technologies. In any case, it is software with a complicated structure, with a large number of associated variables and that requires a high computational load to make predictions. The predictions they can offer are not much better than those that simple models can offer. In this paper we present a MATLAB toolbox created for the prediction of electrical demand. The toolbox implements multiple seasonal Holt-Winters exponential smoothing models and neural network models. The models used include the use of discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. Additionally, the results of its application in various electrical systems in Europe are shown, where the results obtained can be seen. The use of this library opens a new avenue of research for the use of models with discrete and complex seasonalities in other fields of application.
Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. In the past decade, deep learning has been applied to STELF, modeling and predicting electricity demand with high accuracy, and contributing significantly to the development of STELF. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies some research challenges and potential research directions to be further investigated in future work.
The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the Maximum Information Coefficient (MIC), which can find the correlations between the non-technical loss (NTL) and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.