Renewable Systems Research Articles

High-Frequency Oscillation Suppression Strategy for Renewable Energy Integration via Modular Multilevel Converter—High-Voltage Direct Current Transmission System Under Weak Grid Conditions

To address the high-frequency resonance issues in renewable energy systems integrated via MMC-HVDC transmission under weak grid conditions, this paper establishes a wide-frequency sequence impedance model for renewable energy grid-side converters and MMC-HVDC sending-end converters. The impedance characteristics of the MMC-HVDC transmission system under two distinct control modes are compared, and the constant power control mode is selected for detailed analysis to better evaluate the effectiveness of suppression strategies. Based on this framework, the superposition theorem is employed to analyze the interaction mechanism between the impedance characteristics of the MMC-HVDC sending-end converter and the renewable energy grid-connected system. Since the aim of this study is to propose suppression strategies for the MMC-HVDC transmission system, a sensitivity analysis of its control parameters is conducted. The results identify the current loop and voltage feedforward control as the dominant factors influencing high-frequency oscillations. Accordingly, a coordinated control strategy combining current loop regulation and voltage feedforward compensation is proposed. An electromagnetic transient simulation model is developed in MATLAB/Simulink. The simulations demonstrate that the proposed strategy effectively suppresses oscillations in the MMC-HVDC system across high-frequency ranges. Furthermore, it avoids negative damping characteristics within a broad frequency band, significantly enhancing the steady-state performance of renewable energy systems integrated via MMC-HVDC transmission.

An enhanced renewable energy integration system with improved power system stability

An enhanced renewable energy integration system with improved power system stability

Enhancing Smart Home Efficiency with Heuristic-Based Energy Optimization

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2.

Application of Operators with Shift in the Modeling of Renewable Systems with Elements in Four States

In our recent works, we developed models for renewable systems with elements in three states. For the sake of clarity, states were interpreted as being healthy, being sick, and having immunity acquired after recovery. The states differ significantly in their processes of mortality, reproduction, and mutual influence. In this work, along with the three states listed, a fourth state is introduced, which we will interpret as the state of the elements of the system that have undergone vaccination. As a consequence of this, balance relations become more complicated: unknown functions with shifts and iterations from these shifts appear. A need arises to develop the applied mathematical methods. The content of the article is an expansion and development of our works on modeling systems with elements in several states based on the application of new results on solving functional equations with shifts.

Bio-inspired computational intelligence metaheuristic-based optimization and sensitivity analysis approach to determine techno-economic feasibility of hydrogen refueling stations for fuel cell vehicles

This study presents a comprehensive economic and technological evaluation of renewable hybrid power systems for hydrogen refueling stations (HRS) in Nizwa, Oman, leveraging cutting-edge optimization algorithms to determine the most cost-effective and efficient hybrid energy system configurations. Three hybrid energy systems of photovoltaic-wind turbine-battery (PV-WT-B), photovoltaic-wind-fuel cell-battery (PV-WT-FC-B), and wind turbine-battery (WT-B) were evaluated based on net present cost (NPC), levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). The study employs advanced optimization techniques, including the Mayfly Algorithm, Genetic Algorithm, CUKO Search, Gray Wolf Optimizer (GWO), Constrained Particle Swarm Optimization (CPSO), Harmony Search (HS), and Flower Pollination Algorithm to determine the most viable hybrid energy system for the HRS in Nizwa. The results indicate that CPSO consistently achieves the lowest NPC, LCOE, and LCOH, whereas HS and GWO yield higher costs due to convergence inefficiencies. Sensitivity analysis reveals a strong inverse correlation between PV capacity and cost metrics, highlighting the economic advantage of increased solar generation. Additionally, hybrid configurations integrating PV and wind turbine (PV-WT-B, PV-WT-FC-B) significantly reduce NPC compared to WT-B, reinforcing the role of solar energy in optimizing economic costs. Furthermore, fuel cell integration (PV-WT-FC-B) imposes additional economic burdens, making PV-WT-B the most viable solution for HRS deployment in Oman. More so, the annual worth and return-on-investment analysis demonstrated that the PV-WT-B is the preferred energy system to meet the needs of the HRS in terms of investment. The findings underscore the importance of renewable energy fraction and capacity factor in energy economics, demonstrating that higher PV integration enhances sustainability and cost-efficiency. This study provides a transformative framework for decarbonizing Oman’s transportation sector, offering insights into optimal hydrogen production strategies to advance the global clean energy transition.

The Integrated Site Suitability Criteria of Electric Vehicle Charging Stations

Electric vehicles (EVs) are a renewable energy-based transportation system that is growing significantly in many regions. Due to their minimal operating costs and better environmental impacts, EVs are becoming more prevalent. Moreover, EVs can overcome environmental issues by reducing greenhouse gas (GHG) emissions and improving air quality. Yet, the EVs market is struggling to locate charging stations (CSs) in some places. Accessibility to CSs influences the development of EVs. Hence, companies are investing in the cost of building CSs to promote the adoption of EVs. The electric vehicle charging stations (EVCS) should be strategically located in an ideal location so that EV users can travel to the stations within their driving range. Several competing criteria should be considered while determining the EVCS. This paper presents an overview of the main criteria and sub-criteria to select suitable locations for EVCS from the previous studies.

How renewable energy consumption and digitalization contribute to environmental sustainability: Evidence from One Belt One Road countries.

How renewable energy consumption and digitalization contribute to environmental sustainability: Evidence from One Belt One Road countries.

Robust operating strategy for voltage and frequency control in a non-linear hybrid renewable energy-based power system using communication time delay

Robust operating strategy for voltage and frequency control in a non-linear hybrid renewable energy-based power system using communication time delay

A novel foundation design for the hybrid offshore renewable energy harvest system

A novel foundation design for the hybrid offshore renewable energy harvest system

Hybrid renewable multi-generation system optimization: Attaining sustainable development goals

Hybrid renewable multi-generation system optimization: Attaining sustainable development goals

A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system

A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system

Expansion planning via decomposition to achieve fully renewable power and freshwater systems

Expansion planning via decomposition to achieve fully renewable power and freshwater systems

A novel flexible load regulation and 4E-F multi-objective optimization for distributed renewable energy power generation system

A novel flexible load regulation and 4E-F multi-objective optimization for distributed renewable energy power generation system

AI-DRIVEN OPTIMIZATION IN RENEWABLE HYDROGEN PRODUCTION: A REVIEW

This paper presents a comprehensive systematic review of artificial intelligence (AI)-driven optimization in renewable hydrogen production, emphasizing its pivotal role over the past decade in enabling the transition toward a sustainable, low-carbon energy future. As green hydrogen gains prominence as a clean energy carrier—particularly in hard-to-decarbonize sectors such as transportation, heavy industry, and grid balancing—the demand for efficient, scalable, and economically viable production methods has intensified. AI has emerged as a transformative enabler, offering innovative solutions to technical and economic barriers across various production pathways, including electrolysis (proton exchange membrane, alkaline, and solid oxide), biomass gasification, solar-to-hydrogen, and wind-to-hydrogen systems. This study employs a structured methodology based on a systematic literature review (SLR), drawing from over 150 peer-reviewed journal articles, patents, industry reports, and conference proceedings published between 2014 and 2024. Data were sourced from academic databases, leading energy organizations, and international technology forums. The review categorizes AI techniques—machine learning, deep learning, reinforcement learning, and optimization algorithms—and examines their applications in process control, predictive maintenance, energy forecasting, material discovery, cost reduction, and hybrid renewable system integration. Emerging trends include AI-powered digital twins, AI-quantum hybrid frameworks, and intelligent supply chain management. However, the widespread deployment of AI in hydrogen systems faces challenges, such as limited access to high-quality real-time datasets, lack of standardization, regulatory hurdles, and high computational demands. The paper concludes by identifying key research gaps and outlining future directions, including the development of lightweight, explainable AI models, cross-sectoral collaborations, and supportive policy frameworks. Ultimately, this review underscores the transformative potential of AI in accelerating the commercialization, optimization, and global adoption of renewable hydrogen technologies, laying the groundwork for a robust, intelligent, and decarbonized energy infrastructure.

A Case Study of Renewable Natural Gas Techno-Economics and Emissions at a Wastewater Treatment Plant

Renewable natural gas derived from biogas presents a viable pathway for decarbonizing natural gas systems. Wastewater treatment plants equipped with anaerobic digesters often flare or utilize biogas for heat and electricity generation, missing the potential of renewable natural gas. This study investigates the techno-economic feasibility and emission impact of a renewable natural gas system at a small wastewater treatment plant in the Southwestern United States. Due to a lack of existing data on biogas composition and contaminants from plants originating within the United States, samples were tested seasonally for a year, and gas components are reported in this analysis. Using hourly biogas production data, the analysis incorporates costs and renewable fuel credits at 2022 market prices and the cost to remove the biogas contaminants. The results show a 15-year net present value of USD 16.3 million, with a payback period of three years, surpassing the economic performance of combined heat and power systems previously assessed for the same facility. Additionally, renewable natural gas systems achieve a 22% reduction in site emissions compared to combined heat and power systems. These findings highlight renewable natural gas as a profitable and environmentally superior alternative for biogas utilization in small-scale wastewater treatment plants, contingent on access to renewable fuel credits.

Design and Simulation of a Renewable Energy-Based Hybrid Microgrid System

The transition to low-carbon and sustainable energy solutions has accelerated the advancement of hybrid microgrids, which integrate multiple renewable sources with energy storage and smart control mechanisms. This study focuses on the modeling and simulation of a hybrid microgrid incorporating solar photovoltaic (PV), wind energy conversion systems (WECS), and battery energy storage systems (BESS) within a unified control framework. The system's performance is analyzed in both grid-connected and islanded modes using MATLAB/Simulink, considering variations in solar irradiance, wind speed, and load fluctuations. To maintain voltage and frequency stability, Maximum Power Point Tracking (MPPT), droop control, and realtime energy management techniques are employed. Simulation results demonstrate the system’s ability to provide reliable and high-quality power while mitigating the variability of renewable energy sources. This research supports the development of resilient energy infrastructures, promoting decentralization and carbon-neutral power generation.

Techno‐Economic Comparison of Battery–Flywheel With Battery–Hydrogen Storage System in the Vicinity of Off‐Grid HRES for Four Climates: MCDM Method

ABSTRACTEnsuring sustainable power and heating in remote rural areas presents a considerable challenge. Renewable hybrid systems are typically recommended for this purpose; however, maintaining stability necessitates either a connection to the grid requiring electricity purchases from power plants, which are significant sources of pollution, or the deployment of extensive equipment to ensure system stability. This study examines four climatic regions in Iran, evaluating the selection between two storage systems, battery‐hydrogen and battery–flywheel, through simulation and two‐stage optimization. HOMER PRO software was utilized for both simulation and optimization, while the method based on the removal effects of criteria (MEREC) was employed for criteria weighting in decision‐making, in conjunction with the technique for order of preference by similarity to ideal solution (TOPSIS) method. The findings indicate that the battery‐hydrogen system is significantly more cost‐effective, achieving savings of up to $211,327 in net present cost (NPC) and $0.738 in cost of energy (COE). Furthermore, the battery–hydrogen system demonstrates a greater reliance on renewable energy sources, increasing by up to 23.6% compared to the battery–flywheel system.

Techno-Economic Analysis of Off-Grid Hybrid Renewable Energy System for Ethiopian Rural Electrification

This study presents a comprehensive plan for implementing off-grid hybrid renewable power systems in rural areas of Ethiopia, as a part of the government’s ambitious renewable energy development initiatives. The focus is on leveraging the country’s abundant solar, wind, and micro-hydro power resources through careful planning and analysis. Through field surveys, data collection on local population density, electricity demand, and available renewable energy potential, this study identifies key factors for designing effective off-grid hybrid systems. Utilizing NASA’s wind speed and solar radiation databases, alongside on-site measurements and government data on micro-hydro potential, the study modeled three different hybrid systems using HOMER software. The analysis considers various factors such as net present cost, capital investments, and energy costs to determine the feasibility of each hybrid system. For efficient solar and wind resource scenarios, the cost of energy can be as low as $0.122/kWh, while optimal solar radiation and micro-hydro conditions can lower it to $0.043/kWh. In cases where renewable resources may be limited, hybridizing with diesel generators can provide a cost-effective solution, albeit at a slightly higher cost of $0.14/kWh. Taking into account the sensitivity analyses of Photovoltaic (PV) and diesel prices, this study demonstrates that the proposed off-grid hybrid renewable systems can offer competitive energy costs compared to the current national tariff. With a focus on 180 rural communities within a mile radius, this holistic approach to renewable energy planning holds significant promise for sustainable and affordable power supply in Ethiopia’s rural areas.

Two-Level Optimal Scheduling of Electric–Aluminum–Carbon Energy System Considering Operational Safety of Electrolytic Aluminum Plants

In recent years, the mounting pressure on the integration of renewable power has emerged as a crucial concern within renewable power systems. This situation urgently necessitates an enhancement in the operational flexibility of the demand side. As an energy-intensive load, electrolytic aluminum plants have great potential to participate in the demand response. However, existing models for electrolytic aluminum load regulation lack verification of operational safety, and there is a lack of consideration of carbon trading mechanisms. To this end, this paper proposes a two-level optimization framework for electric–aluminum–carbon energy systems. More specifically, this work presents a safety-constrained electrolytic aluminum plant model, which considers operational states swinging with key parameters and limitations verified by the thermal dynamic simulations of electrolytic aluminum electrolyzers. In addition, green certificate and tiered carbon trading mechanisms are both introduced to the electric–aluminum–carbon energy. Case studies show that the proposed framework can significantly reduce the system emission by 21.9%, improve the overall economic efficiency by 16.5%, and increase the renewable integration rate by 4.5%, with an additional 8.6% of carbon reduction that can be achieved by adopting EU carbon price policies.

Systematic Optimize and Cost-Effective Design of a 100% Renewable Microgrid Hybrid System for Sustainable Rural Electrification in Khlong Ruea, Thailand

This study presents a systematic approach to designing and optimizing a 100% renewable hybrid microgrid system for sustainable rural electrification in Khlong Ruea, Thailand, using HOMER Pro software (Version 3.15.3). The proposed system integrates photovoltaic (PV) panels (20 kW), pico hydro (9.42 kW), and lithium-ion battery storage (264 kWh) to provide a reliable, cost-effective, and environmentally sustainable energy solution for a remote village of 306 residents. The methodology encompasses site-specific resource assessment (solar irradiance, hydro flow), load demand analysis, and techno-economic optimization, minimizing the net present cost (NPC) and cost of energy (COE) while achieving zero emissions. Simulation results indicate the optimal configuration (S1) achieves an NPC of USD 362,687 and COE of USD 0.19/kWh, with a 100% renewable fraction, outperforming the current diesel–hydro system (NPC USD 3,400,000, COE USD 1.85/kWh, 61.4% renewable). Sensitivity analysis confirms robustness against load increases (1–5%), though battery capacity and costs rise proportionally. Compared to regional microgrids, the proposed system excels in terms of sustainability and scalability, leveraging local resources effectively. The lifecycle assessment highlights the battery’s embodied emissions (13,200–39,600 kg CO2e), underscoring the need for recycling to enhance long-term sustainability. Aligned with Thailand’s AEDP 2018–2037 and net-zero goals, this model offers a replicable framework for rural electrification in Southeast Asia. Stakeholder engagement, including community input and EGAT funding, ensures practical implementation. The study demonstrates that fully renewable microgrids are technically feasible and economically viable, providing a blueprint for sustainable energy transitions globally.