Optimization Of Energy Systems Research Articles

Optimizing solar-wind hybrid energy systems for sustainable charging stations and commercial applications: A two-stage framework with ebola-inspired optimization

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %.

EnergyScope Pathway: An open-source model to optimise the energy transition pathways of a regional whole-energy system

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility.This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model’s results with similar studies and found consistency in terms of technico-economic estimations.

Effects of dispatch algorithms and fuel variation on techno-economic performance of hybrid energy system in remote island

A stand-alone hybrid energy system is needed to mitigate the growing demand for future energy and to reduce emissions generated by conventional fuel sources. This work shows and proposes an optimum hybrid energy system configuration with less environmental pollution for a coastal region of Bangladesh with a load demand of 292.20 kWh/d and a peak load of 41.14 kW. This research presents the cost of energy (COE) and net present cost (NPC) for a PV-wind hybrid system with five alternative fuel generator technologies. For each possible configuration, the effects of three alternative dispatch algorithms, load following (LF), cycle charging (CC), and combined dispatch (CD), are examined. All three algorithms were validated using a genetic algorithm (GA), Cuckoo search algorithm (CUSA), Constrained Particle Swarm Optimization (CPSO), Harmony search algorithm (HSA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). Furthermore, a sensitivity analysis is also conducted by utilizing the gasoline price, discount rate, battery cost, PV cost, and inflation rate. The results show that the PV-wind-natural gas-based system provides the minimum COE (0.196 USD/kWh) and NPC (270,483 USD) when the CD algorithm is followed. For the same optimal hybrid energy system, the COE is 0.201 USD/kWh, 0.0998 USD/kWh, 0.101 USD/kWh, 0.101 USD/kWh, and 0.0987 USD/kWh respectively for GA, CUSA, CPSO, HSA, and NSGA-II. However, COE increases by 8 % (0.212 USD/kWh) and 15 % (0.225 USD/kWh) when the CC and LF algorithms are followed. In addition, a comparison of all configurations reveals that the PV-wind-biomass configuration with the CD algorithm excludes biomass generators to lower the COE, making the system emission-free.

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

As the world continues to grapple with the challenges posed by climate change and the depletion of conventional energy sources, renewable energy systems such as solar photovoltaic (PV) technology have gained significant prominence. Hybrid solar PV systems, which combine multiple energy sources and storage solutions, offer a promising avenue to improve the reliability and efficiency of renewable energy generation. This research focuses on the mathematical modeling, non-linearity characteristic analysis, and the development of efficiency enhancement strategies for hybrid solar PV energy systems. The study begins with the development of comprehensive mathematical models that accurately represent the complex interactions within hybrid solar PV systems. These models consider various factors, including solar irradiance, temperature variations, load profiles, and the dynamic behavior of energy storage components. The incorporation of non-linear characteristics, often overlooked in conventional models, allows for a more realistic representation of system performance. To enhance the efficiency of hybrid solar PV systems, a range of strategies are proposed and evaluated. These strategies encompass advanced control algorithms, optimized sizing and placement of energy storage elements, and the integration of emerging technologies such as artificial intelligence and machine learning. The goal is to mitigate non-linear effects, maximize energy utilization, and improve system response to dynamic operating conditions.
 The findings from this research provide valuable insights into the design, operation, and performance optimization of hybrid solar PV energy systems. By addressing non-linearity characteristics and developing efficient strategies, this study contributes to the advancement of renewable energy technologies and fosters the transition towards a more sustainable and resilient energy future.

Multi-agent game operation of regional integrated energy system based on carbon emission flow

In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision-making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon.

A method to bridge energy and process system optimization: Identifying the feasible operating space for a methanation process in power-to-gas energy systems

The growing adoption of renewable energy is driving the integration of new, complex process technologies into energy systems, presenting operational optimization challenges. Simple models are necessary for computational tractability, yet detailed models are essential for feasible operating decisions. This work introduces a method for optimally operating an energy system with integrated complex process technologies. The proposed method bridges the mathematical modeling and optimization at energy and process system levels. Simple surrogate models and detailed process models are used to represent complex process technologies at their respective system levels. The resulting operational problems are solved through a sequential iterative approach. Applied to a power-to-gas energy system with an integrated methanation process, our method outperforms a benchmark method by capturing interactions between energy and process system levels, and among different process variables. The proposed method integrates more detailed constraints and ramping costs, yielding an optimal, feasible operating strategy within tractable computational time.

Enhancing Energy sector efficiency: A study on supercomputer performance in optimizing energy systems

Enhancing Energy sector efficiency: A study on supercomputer performance in optimizing energy systems

Enhancing Measurement Quality of Voltage Divider Circuit and ACS712 DC Current Sensor in PPNS Baruna 01 Crewboat Solar Power Plant

This research explores the crucial role of precise measurement in enhancing solar power generation efficiency within the maritime context, with a specific focus on the solar power plant aboard the PPNS Baruna 01 Crewboat. It investigates the relationship between light intensity and solar cell voltage output, affirming the direct correlation between sunlight availability and voltage generation. Additionally, the study introduces the application of simple linear regression techniques to improve measurement accuracy, particularly in voltage divider circuits and ACS712 current sensors. Results demonstrate a substantial reduction in measurement errors, from 34.88% to 0.45% for voltage measurement and from 23.71% to 2.69% for current measurement. These advancements hold significant implications for the maritime industry, promoting the adoption of clean and renewable solar energy sources on vessels, potentially reducing reliance on fossil fuels, and contributing to environmental sustainability. Moreover, the research underscores the importance of data analysis in optimizing energy systems and provides a foundation for further developments in renewable energy technology and system optimization.

Design optimization of a multi-source renewable energy system using a novel method based on selective ensemble learning

This study conducts a comparative assessment to optimize the design of a Hybrid Renewable Energy System (HRES) consisting of PV panels, wind turbines (WTs), and hydrogen storage (PV/WT/FC). The study focuses on Dakhla City, leveraging its favorable weather conditions to evaluate the potential of renewable energy sources. A novel approach, Selective Ensemble Marine Predators (SEMPA), is introduced. SEMPA utilizes a selective ensemble learning strategy to enhance the performance of the Marine Predators Algorithm (MPA) and determine the optimal system size with the lowest Total Net Present Cost (TNPC) and improved reliability. To benchmark SEMPA's performance, we compare it against established methods, including MPA, Particle Swarm Optimization (PSO), and Harmony Search (HS). Results demonstrate that the integrated PV/WT/FC system provides a reliable and cost-effective energy solution for the southwest region of Morocco, particularly in Dakhla. SEMPA outperforms HS, PSO, and traditional MPA, achieving stable convergence after 30 iterations. The system exhibits a superior Cost of Energy (COE) at 0.3127 $/kWh, a TNPC of 0.9689 M$, and a low Loss of Power Supply Probability (LPSP) at 0.04747.

Optimization of renewable energy systems for off-grid communities

Optimization of renewable energy systems for off-grid communities

Optimization of a Hybrid Renewable Energy System Based on Meta-Heuristic Optimization Algorithms

Optimization of a Hybrid Renewable Energy System Based on Meta-Heuristic Optimization Algorithms

Design and optimization of a cold energy and waste heat utilization system for LNG-powered ships with post-combustion carbon capture

The introduction of dual-carbon targets has accelerated LNG fuel adoption on vessels and driven the advancement of carbon capture technologies. This study?s aim is a 37000-deadweight tonnage liquified natural gas dual-fuel powered ship, for which chemical absorption carbon capture is applied, utilizing flue gas and liquified natural gas to supply the process?s heat and cold energy. Then a system with efficient utilization of energy and carbon capture for the LNG dual-fuel ship is designed, coupling the waste heat onboard with transcritical CO2 and organic Rankine cycle on the principle of energy cascade utilization. The system is simulated using Aspen HYSYS and the exergy analysis is carried out for this system. Then the working fluid is optimized for the system. After that, through the genetic algorithm, the system?s operating parameters are further optimized. Additionally, the system?s economic analysis is also performed. It is shown that the scheme?s exergy efficiency reaches 39.98%, and the expected cost-recovery cycle is 4.75 years.

Advancing solar energy forecasting with modified ANN and light GBM learning algorithms

<abstract> <p>In the evolving field of solar energy, precise forecasting of Solar Irradiance (SI) stands as a pivotal challenge for the optimization of photovoltaic (PV) systems. Addressing the inadequacies in current forecasting techniques, we introduced advanced machine learning models, namely the Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and the Linear Support Vector Machine with Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency and predictive accuracy, specifically engineered to overcome common pitfalls such as overfitting and data inconsistency. The RELAD-ANN model, with its multi-layer architecture, sets a new standard in detecting the nuanced dynamics between SI and meteorological variables. By integrating sophisticated regression methods like Support Vector Regression (SVR) and Lightweight Gradient Boosting Machines (Light GBM), our results illuminated the intricate relationship between SI and its influencing factors, marking a novel contribution to the domain of solar energy forecasting. With an R<sup>2</sup> of 0.935, MAE of 8.20, and MAPE of 3.48%, the model outshone other models, signifying its potential for accurate and reliable SI forecasting, when compared with existing models like Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, and 1-dimensional Convolutional Neural Network, while the LSIPF model showed limitations in its predictive ability. Light GBM emerged as a robust approach in evaluating environmental influences on SI, outperforming the SVR model. Our findings contributed significantly to the optimization of solar energy systems and could be applied globally, offering a promising direction for renewable energy management and real-time forecasting.</p> </abstract>

Quantum optimization methods in energy systems

This paper discusses the application of quantum algorithms to the optimization of energy systems, focusing on solving the routing problem in the context of energy. A quantum approach that utilizes the principles of superposition and inversion with respect to the mean to efficiently find optimal energy routing is proposed. Research is carried out to develop a special quantum oracle to represent the structure of energy networks and energy flows between nodes. The application of inversion with respect to the mean provides efficient convergence to optimal solutions in the space of possible routes. We implement the quantum algorithm in the form of a quantum circuit, visualize the results and provide an analysis of the optimal energy routes. The work represents an important step in the development of quantum optimization methods for solving complex problems in the field of energy and may have prospects in practical applications in the future.

Energy supply for the building stock of tomorrow – A case study

Reducing energy consumption and using renewable energy sources for the building operation is crucial to achieve lower overall greenhouse gas (GHG) emissions. In this case study we conduct a building energy system optimisation for a new residential district in Darmstadt, Germany under different boundary conditions using the optimisation software Sympheny. Boundary conditions of the year 2023 lead to similar results to the owner’s plan, which is mainly based on natural gas-fired cogeneration plants. However, different scenarios with predicted emission factors and energy prices for 2050 lead to a shift towards the usage of renewables. As planners tend to use current boundary conditions for their calculations, we assess the possibility of switching to a renewable heat supply after the expected life span of the cogeneration plants. The obstacles identified mainly concern the temperature level, but also the space that will not be accessible at that time. The results emphasise (1) that the decarbonisation of space heating in Germany goes hand in hand with the need to decarbonise the electricity grid and (2) the need for planners to start considering future developments as part of building energy system planning.

A Unified Framework for Integrated Energy Systems Optimization With Day-Ahead and Real-Time Coordination

A Unified Framework for Integrated Energy Systems Optimization With Day-Ahead and Real-Time Coordination

A digital twin concept for optimizing the use of high-temperature heat pumps to reduce waste in industrial renewable energy systems

In light of the global industrial sector's steadfast pursuit of sustainable solutions to mitigate carbon emissions and efficiently minimize energy inefficiencies, the significance of novel approaches to energy optimization becomes increasingly evident. This research presents a novel digital twin concept that is designed to enhance the efficiency and effectiveness of high-temperature heat pumps in industrial renewable energy systems. Through the utilization of real-time data and complex computer modeling techniques, our proposed digital twin model seamlessly presents a comprehensive perspective on energy flows. This approach identifies inefficiencies and delivers practical insights to mitigate waste. The incorporation of this approach into pre-existing eco-conscious renewable energy systems has the potential to greatly enhance the effectiveness, predictability, and long-term viability of industrial processes. The empirical findings, obtained from multiple case studies conducted in industrial settings, provide evidence of the potential benefits in terms of energy waste reduction, and maximize the durability of systems. The results of our initial studies provide a foundation for the utilization of digital twin technologies in the field of industrial renewable energy systems optimization, representing a significant advancement towards a more environmentally sustainable industrial landscape.

A comparative study on hybrid GA-PSO performance for stand-alone hybrid energy systems optimization

A comparative study on hybrid GA-PSO performance for stand-alone hybrid energy systems optimization

Efficient energy transport in constant-voltage triboelectric nanogenerator-based power units

The energy transport in TENG-based power units is investigated and a real-time intelligent energy optimization system (RIEOS) is built to achieve efficient energy transport from a system integration optimization perspective.

Editorial: Key technologies of smart energy system optimization

Editorial: Key technologies of smart energy system optimization