Optimization Of Energy Systems Research Articles

IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring

- Electric energy monitoring in industrial loads like pressure die casting is extremely vital especially due to their energy handling requirements. This work details the conception and implementation of an Internet of Things based Smart Energy Meter designed specifically for Industrial purposes. The system can measure three-phase voltage, current (up to 100A) and furnace temperatures with the aid of Resistance Temperature detectors. Using the ESP32 microcontroller and the Blynk Internet of things platform, the meter enables the collection, storage, and displaying of the data on the Cloud in real time. In order to enhance accuracy, the hardware architecture combines ZMPT101B voltage sensors, SCT-013-000 current sensors and a MAX31865 Resistance Temperature detector module. The software part of the system uses the Open-Energy Monitor library which contains various algorithms for energy calculations and transfers data over Serial peripheral interface/Wi-Fi protocols. The system has been tested in an industrial setting and the results have shown an accuracy of measurement within a tolerance of 1% for electrical and thermal characteristics plus a normal operation for variable conditions. The presented results prove the possibility of intra- system energy optimization and the industrial prospects of its application. Promising research directions include the addition of predictive maintenance features based on machine learning as well as more efficient scaling to support many different industrial applications. Key Words: Smart Energy Meter, IoT, ESP32, RTD Sensors, Energy Monitoring, Industrial Automation

Reducing Operational Costs in Sulselbar’s 150 kV System with Electromagnetic Field and Sine-Cosine Optimization

Dynamic economic dispatch (DED) is a crucial task in modern power systems, requiring efficient optimization to minimize generation costs while satisfying operational constraints. This study introduces a Hybrid Electromagnetic Field Optimization with Sine-Cosine Algorithm (EMFO-SCA) tailored to address the unique challenges of the Sulselbar 150 kV power system. Specifically, the algorithm is designed to handle non-linear cost functions, complex constraints, and dynamic load variations across a 24-hour scheduling period. EMFO-SCA achieves a balanced integration of global exploration (via Electromagnetic Field Optimization) and local exploitation (via the Sine-Cosine Algorithm), resulting in robust optimization performance. Applied to a system with seven active generator buses, EMFO-SCA demonstrates an average operational cost reduction of 0.27% compared to the Kho-Kho Algorithm (KKA). This improvement translates to measurable cost savings while maintaining strict adherence to generation limits, ramp rate constraints, and power balance at all intervals. For instance, during peak demand at 521.52 MW (hour 12), the method effectively minimizes costs without compromising operational reliability. The dual-phase design of EMFO-SCA enables faster convergence and higher accuracy than conventional methods, making it a scalable solution for real-world DED challenges. By optimizing power generation schedules dynamically and reliably, this study establishes EMFO-SCA as a significant advancement in energy system optimization, with clear potential for practical deployment in similar power systems.

Optimization and economic analysis of hybrid renewable energy system

Optimization and economic analysis of hybrid renewable energy system

Techno-economic dataset for energy market and capacity payment co-optimization in the Dominican Republic's power market.

The electric power industry has an impact on fossil fuel consumption, which must be considered in decarbonization strategies. Energy systems optimization modelling can be applied to evaluate policy scenarios in the power sector to accelerate energy transitions. These modelling tools need data to simulate different scenarios in the power system to clarify the design of energy policies. For this reason, collecting and processing technical and economic data is needed to guarantee quality input for the modelling tools. This article presents a dataset for an optimization model of the generation mix and the energy demand in the power system of the Dominican Republic to determine the capacity value of variable renewable energy (VRE), i.e., wind and solar, that can serve as an incentive for these technologies. While the data corresponds to the Dominican Republic's power system, the method of collecting and processing data can be implemented in other countries. The data collected is an open-access database of the independent system operator, the power sector regulator, and utilities, as well as websites and databases of international organizations.

Advances in Artificial Intelligence and Computational Methods: Enhancing Modeling, Prediction, and Optimization

bstract: This area of artificial intelligence and computational methods has witnessed tremendous development, especially with the solution of highly complex problems across scientific disciplines. This review brings together the latest advancements in the integration of AI with traditional computational techniques, focusing on their applications in numerical simulations, system optimization, and predictive modeling. Key contributions include AI-augmented finite difference methods for solving partial differential equations, advancements in material and energy systems optimization, and innovative approaches to highperformance computing. Future directions emphasize scalability, interdisciplinary applications, and the integration of emerging technologies such as quantum computing and reconfigurable hardware.

Modelling and optimization of a hybrid renewable energy systems for rural electrification in Nigeria: A review

Modelling and optimization of a hybrid renewable energy systems for rural electrification in Nigeria: A review

Paving the Ecological Foundation of New Industrialization with Green and Low-Carbon Development

Since its inception, the new industrialization has incorporated green and low-carbon into its characteristics. Against the backdrop of the interweaving of global economic development and environmental protection, green development is an inevitable choice for China to break through resource and environmental constraints and promote the development of new industrialization. This article first analyzes the connotation and characteristics of new industrialization and green low-carbon, and then introduces the practical experience of green development in the European Union, the United States, and China. The European Union focuses on transforming energy structure and promoting innovation in green technologies, the United States focuses on supporting green low-carbon development through legislation and building market mechanisms, while China actively explores from top to bottom through policy guidance, industrial transformation, and energy system optimization. Based on domestic and foreign experiences, this article proposes to strengthen the division of responsibilities, improve supply levels, optimize energy structure, and other aspects to cultivate a green background for new industrialization.

Contextual Background Estimation for Explainable AI in Temperature Prediction

Accurate weather prediction and electrical load modeling are critical for optimizing energy systems and mitigating environmental impacts. This study explores the integration of the novel Mean Background Method and Background Estimation Method with Explainable Artificial Intelligence (XAI) with the aim to enhance the evaluation and understanding of time-series models in these domains. The electrical load or temperature predictions are regression-based problems. Some XAI methods, such as SHAP, require using the base value of the model as the background to provide an explanation. However, in contextualized situations, the default base value is not always the best choice. The selection of the background can significantly affect the corresponding Shapley values. This paper presents two innovative XAI methods designed to provide robust context-aware explanations for regression and time-series problems, addressing critical gaps in model interpretability. They can be used to improve background selection to make more conscious decisions and improve the understanding of predictions made by models that use time-series data.

Multi-objective optimization of hybrid energy systems using gravitational search algorithm

The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system’s economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability.

Hybrid CNN-Multivariate LSTM Model for Accurate Short-Term Electricity Price Forecasting and Energy System Optimization

Hybrid CNN-Multivariate LSTM Model for Accurate Short-Term Electricity Price Forecasting and Energy System Optimization

Interfacing TRNSYS with MATLAB for Building Energy System Optimization

This contribution investigates whether the use of the MATLAB OptimizationToolbox on a parameter identification problem for a TRNSYS model provides better performance in iteration time. It presents the development of a framework connecting the MATLAB Optimization Toolbox with TRNSYS on the one hand and coordinating the optimization process of a TRNSYS model by GenOpt through MATLAB on the other hand. A benchmark framework in MATLAB was created to link TRNSYS and MATLAB and to configure the optimization process of GenOpt and the MATLAB Optimization Toolbox. Using this framework, a comprehensive comparison of the optimization solvers in GenOpt and the MATLAB Optimization Toolbox for the identification of the overall heat transfer coefficient of a TRNSYS heat exchanger model regarding the optimization time and number of iterations is presented as a use case. The results for the given problem show that GenOpt gives slightly better results in optimization time, whereas MATLAB has more potential and flexibility.

Recent Developments of Solar Charge Controllers Technologies: A Bibliometric Study

A solar photovoltaic system is a renewable energy that depends on solar irradiance and ambient temperature. A solar charge controller is required to ensure the energy received by the photovoltaic cell or module is maximized at the output. This study presents the first bibliometric analysis based on a thorough evaluation of the most frequently cited articles on the solar charge controller to forecast future trends and applications. This paper performs a statistical analysis using the Scopus database and extracts the 100 most cited papers. It has been illustrated that the solar charge controller literature expanded swiftly from 2012 to 2023, with 2019 receiving the most publications and papers published in 2018 receiving more citations compared to works published in other years. In recent years, battery storage, electric vehicles, energy storage, controllers, photovoltaic systems, and power management systems have garnered great interest. The solar charge controller's functional evaluation and determination of the optimal renewable energy system might boost its potential. Our analysis of highly cited articles on solar charge controllers emphasizes a selection of features, including control methods and systems, issues, challenges to establishing current constraints, research gaps, and the need to find solutions and resolve problems. Every aspect of the features of this overview is anticipated to contribute to an upsurge through the invention of advanced solar charge controllers and control methods for future photovoltaic systems.

An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand.

Data-driven distributionally robust optimization of low-carbon data center energy systems considering multi-task response and renewable energy uncertainty

Data-driven distributionally robust optimization of low-carbon data center energy systems considering multi-task response and renewable energy uncertainty

4E analysis and multi-objective optimization of a sustainable hybrid energy and carbon capture system in LNG-powered vessels

4E analysis and multi-objective optimization of a sustainable hybrid energy and carbon capture system in LNG-powered vessels

Optimization of tribrid energy systems for cost-effective and high-efficiency electricity generation

Optimization of tribrid energy systems for cost-effective and high-efficiency electricity generation

Dual-objective optimization of solar-driven energy system for rural households in solar-rich areas

Dual-objective optimization of solar-driven energy system for rural households in solar-rich areas

Retraction notice to “Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids” [Eng. Sci. Technol. Int. J. 51 (2024) 101649]

Retraction notice to “Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids” [Eng. Sci. Technol. Int. J. 51 (2024) 101649]

Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects

Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects

Effects of electroosmosis and entropy generation on a particulate-fluid suspension through peristaltic motion of Prandtl fluid: a numerical investigation

In thermodynamics, engineering, and environmental science, entropy generation are useful for comprehending complicated phenomena like heat transfer and fluid dynamics, analysing irreversibility, and optimising energy systems. Particulate-fluid flow with chemical reactions are employed in pharmaceutical manufacturing, catalytic converters, and combustion processes. This study provides insights into mass and thermal transmission analysis and influences of electroosmosis on flow of the Prandtl fluid in peristaltic micro-channel. The flow is also investigated considering the multiphase phenomenon influenced by chemical reaction. The flow problem modelling utilises lubrication theory with lengthy wavelengths and creeping flow assumptions. Furthermore, using Debye linearisation, the Poisson equation is simplified. The consequent structures for coupled ordinary differential equations have been handled by numerical solutions computed by using a highly efficient shooting technique with the help of NDSolve tool in Mathematica. It is concluded graphically that the profile of velocity declines with the increasing variation of porosity factor while enhances with the rise in Helmholtz-Smoluchowski velocity parameter. Thermal distribution increases with higher values of solid particle concentration and porous surface. It is evident that the concentration profile diminishes with the porosity coefficient but rises with the Helmholtz-Smoluchowski velocity factor.