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Results for: renewable energy

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2026 foroughian2026optimal DATABASE
Optimal operation of multi-carrier energy systems integrated with renewable energy sources and hydrogen storage systems.

Foroughian, Saina; Bijan, Zohreh Aghaie Joki; Karimi, Hamid; Hasanzadeh, Saeed

Scientific reports

Multi-energy systems are one of the main solutions to facilitate the integration of renewable energy resources in the smart energy system. To this end, this paper presents a comprehensive structure for the energy system that integrates the electrical, hydrogen, and water sections for sustainable management of modern energy systems. The presented model offers cooperative scheduling for neighbor multi-energy systems that provides the opportunity of local energy trading among them. Also, it focuses on the water system and seeks to supply potable water for the energy systems by a water well, desalination unit, and water storage tank. Besides, compressed air energy storage is developed to utilize the surplus generation of renewable energy to provide an efficient operation for the system. To control the uncertain nature of renewable generation, the energy systems can take part in the electrical and thermal demand-side programs to manage their consumption in response to the signal prices. The proposed model is tested on a standard case study, and the numerical results show that the cooperation among energy systems reduces their operating cost and unserved energy by $ 23.91 and 64.317 kWh compared to autonomous operation.
2026 altimania2026adaptive DATABASE
Adaptive multi-objective optimization of microgrid energy management using deep reinforcement learning considering battery degradation and renewable uncertainty.

Altimania, Mohammad Rashed M; Basem, Ali; Saydullaev, Bakhodir; Atamuratova, Zukhra; Madaminov, Bekzod; Umarov, Abdusalom; Benti, Natei Ermias; Chaka, Mesfin Diro

Scientific reports

Microgrids offer enhanced resilience and efficiency but require sophisticated energy management systems (EMS) to balance conflicting objectives like cost minimization, renewable energy utilization, and component longevity, especially under uncertainty. Traditional optimization methods often rely on precise forecasts and may struggle with real-time adaptation and complex trade-offs like battery degradation. This research aimed to develop a deep reinforcement learning (DRL) based EMS for optimizing microgrid operation considering operational cost, battery degradation, and renewable generation uncertainty. A deep Q-network (DQN) based reinforcement learning agent was trained to manage energy flows within a simulated microgrid comprising solar PV, battery storage, controllable loads, and a grid connection. The reward function incorporated operational costs, battery degradation, and renewable utilization objectives, with the agent learning control policies through environment interaction. The DRL-based EMS demonstrated effective adaptive control, achieving a 12.01% reduction in overall operational costs compared to the model predictive control benchmark. The DRL agent implicitly learned strategies that reduced battery degradation by 8.19% while increasing renewable energy utilization by 10.39%. Most notably, the approach maintained robust performance under uncertainty, with only 8.9% cost increase under severe forecast errors compared to 21.5% for conventional methods. This study demonstrates the efficacy of DRL for adaptive multi-objective microgrid energy management, successfully balancing economic operation, battery health preservation, and renewable energy integration under uncertainty.
2026 liu2026energy DATABASE
Energy transition in a complex economy:A multidimensional perspective.

Liu, Tian; Jin, Wei; He, He; Ling, Tao

Journal of environmental management , 404 : 129403

Energy transition through the phase-in of renewables is a crucial pathway for sustainable development. The deployment of renewable energy is increasingly influenced by a country's underlying economic structure and capabilities, captured by economic complexity. This study examines the dynamic and heterogeneous effects of multidimensional economic complexity on renewable energy development. Panel data from 69 countries/regions during 1999-2021 are analyzed using Quantile Regression for Panel Data to investigate the non-linear effects of the multidimensional economic complexity index (ECI) on energy transition and cross-country heterogeneity. The results indicate that trade complexity (trade ECI) hinders energy transition, with the strongest inhibitory effect observed in developed regions. Technology complexity (technology ECI) is found to facilitate energy transition, and this positive effect is strongest in developed regions. Research complexity (research ECI) exhibits a shifting trend, initially negative and subsequently positive, as the quartile of energy structure increases. The robustness tests further confirm the baseline findings. Furthermore, threshold analyses reveal that the negative effect of trade ECI emerges only when industrial structure exceeds a critical value, the positive effect of technology ECI appears after green innovation exceeds its threshold, and the positive influence of research ECI occurs once research and development (R&D) expenditure exceeds the corresponding threshold. This study contributes to the literature by constructing a multidimensional framework of economic complexity, highlighting cross-country heterogeneity between developed and developing economies, and identifying the threshold conditions under which multidimensional ECI either promotes or impedes the transition toward renewable energy.
2026 chandra2026automated DATABASE
Automated assessment of technological and financial drivers of greenhouse gas reduction in sustainable renewable energy systems.

Chandra, Subhash; Abdulhadi, Ali Raqee; Hdeib, Rouya; Beemkumar, N; Mahapatro, Abinash; Jacob, Ashwin; Al-Hedrewy, Marwea; Eshchanov, Temur; Madaminov, Bekzod

Scientific reports

This study analyzes the capacity of renewable energy facilities to reduce greenhouse gas emissions using feature-based analysis approaches. The main goal is to identify the technological, economic, and environmental elements that most substantially influence emission reduction, serving as a basis for strategic planning and policy development. The dataset includes multiple renewable energy sources and financial variables. Predictive modeling was conducted via CatBoost Regression (CAT R) and Random Forest Regression (RFR), along with hybrid optimization via Transit Search Optimization (TSP) and Arithmetic Optimization Algorithm (AOA). Among the assessed configurations, the CAAO configuration not only achieved the highest predictive performance but also converged faster, demonstrating computational efficiency advantageous for real-time and large-scale energy planning. Feature analysis utilizing SHAP values, K-fold cross-validation, and sensitivity evaluation via the FAST method revealed that energy storage efficiency is the predominant factor, followed by financial incentives, underscoring the significance of both technological and economic aspects in emission reduction strategies. These findings offer an initial investigation and pragmatic suggestions rather than conclusive determinations. The findings indicate that feature-oriented assessments, when integrated with sophisticated predictive modeling, may substantially improve renewable energy planning and facilitate the formulation of context-specific, low-carbon policies. Importantly, by jointly employing variance-based global sensitivity analysis (FAST) and explainable machine learning (SHAP), the study reconciles an apparent discrepancy between structural system drivers (e.g., energy storage capacity) and predictive policy drivers (e.g., financial incentives). This dual-perspective analysis demonstrates that while storage dominates the physical response of emission reduction, incentive mechanisms primarily govern short-term predictive variability, offering a nuanced interpretability framework rarely achieved by single-method studies.
2025 national renewable energy laboratory (nrel)2025renewable DATABASE
Renewable Energy Technical Potential and Supply Curves for the Contiguous United States: 2024 Edition

National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lopez, Anthony; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office; Zuckerman, Gabriel; Pinchuk, Pavlo; Gleason, Michael; Rivers, Marie; Roberts, Owen; Williams, Travis; Heimiller, Donna; Thomson, Sophie-Min; Mai, Trieu; Cole, Wesley

Renewable Energy

2023 ovaitt2023technoeconomic DATABASE
Techno-economic analysis of renewable energy generation at the South Pole

Susan Babinec; Ian Baring-Gould; Amy N. Bender; Nate Blair; Xiangkun Li; Ralph T. Muehleisen; Dan Olis; Silvana Ovaitt

arXiv Preprint

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. This study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all-diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.
2020 chen2020improving DATABASE
Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings

Yang Li; Chunling Wang; Guoqing Li; Jinlong Wang; Dongbo Zhao; Chen Chen

arXiv Preprint

Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. The simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.
2012 national renewable energy laboratory (nrel)2012longterm DATABASE
Long-Term Wind Power Variability

National Renewable Energy Laboratory (NREL), Golden, CO (United States); Wan, Yih; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office

Renewable Energy

2012 usdoe office of energy efficiency and renewable energy (eere)2012wind DATABASE
Wind Powering America Podcasts

USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office; None, None; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Renewable Energy