Global energy systems remain dominated by fossil fuels, accounting for over 80% of primary supply and driving severe climate impacts through greenhouse gas emissions. The transition to renewable sources such as solar and wind is hindered by their intermittency — daily generation can fluctuate by more than 70%, with strong seasonal variability — leading to continued reliance on fossil-based backup generation. Achieving near-complete energy autonomy while maintaining economic viability therefore remains a major challenge. This study evaluates the techno-economic feasibility of hybrid solar–wind–battery–hydrogen systems across nine configurations using a Rule-Based Heuristic Dispatch Algorithm (RB-HDA). System performance was assessed through four key metrics: demand met, fossil-fuel reliance, and economic feasibility via Levelized Cost of Energy (LCOE) and Levelized Cost of Hydrogen (LCOH). Hybrid solar–wind–battery systems met 99.89% of demand with an LCOE of 0.39–2.32 AUD/kWh, but remained limited by seasonal deficits. Integrating hydrogen storage improved resilience to 99.999% demand met with only one fossil-fuel backup hour annually, achieving an LCOH of 0.04 AUD/kg while maintaining an LCOE of 2.32 AUD/kWh. The results demonstrate hydrogen’s role as a pivotal enabler of long-term energy autonomy and a scalable, high-reliability alternative to fossil-based generation. • Nine hybrid solar–wind–battery–hydrogen configurations tested with dispatch modeling. • Rule-based hybrid energy system achieves 99.99% demand with 1,h fossil-fuel energy backup. • Hydrogen production reaches 101.2 million kg at LCOH as low as 0.04 AUD/kg. • Scale-dependent economics: best case LCOE 2.32 AUD/kWh vs worst case 6.77 AUD/kWh. • Findings support resilient urban planning for renewable-hydrogen systems.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Planning hybrid renewable energy systems under uncertain grid interconnection conditions

Power reliability is a critical factor in the operational efficiency of grid-connected sugar cane processing industries, where frequent grid fluctuations or outages can disrupt production and result in economic losses. This study presents the integrated design of a hybrid renewable energy system that combines solar photovoltaic (PV), wind, and biomass technologies to enhance energy reliability and sustainability in these industries. The proposed system takes advantage of the abundant solar irradiance, wind potential, and biomass residue specifically bagasse, a byproduct of sugar cane processing available in sugar-producing regions. By utilizing MATLAB/Simulink software and optimization techniques, the system is designed to ensure a reliable power supply, reduce dependence on the grid, and minimize environmental impact. Technical feasibility is evaluated under various operational scenarios. Results indicate that the hybrid system significantly enhances power reliability. This research presents a scalable framework for integrating renewable resources into agro-industrial energy systems, promoting both energy security and sustainable development.

Advances in hybrid renewable energy systems coupled with mechanical energy storage: Progress and challenges

Towards sustainable transportation: Optimizing hybrid renewable energy systems for EV charging in Bangladesh

Techno-economic feasibility of hybrid renewable energy systems for continuous demand coverage at the provincial level in South Korea

Techno-Economic Analysis and Optimization of Grid-Connected Hybrid Renewable Energy Systems with Hydrogen Storage and Machine Learning-Based Solar Forecasting

This study proposes and optimizes a novel hybrid renewable energy system (HRES) designed for achieving near-zero energy performance in coastal buildings. The selected case is a large hotel located on Kish Island, Iran, where electricity demand is supplied through a synergistic combination of photovoltaic (PV) panels, vertical-axis wind turbines (VAWTs), and oscillating water column (OWC) wave energy converters (WECs) integrated with a hydrogen-based energy storage system (HESS). The system is modeled using TRNSYS for dynamic simulation and coupled with a multi-objective optimization framework employing genetic algorithm. The optimization minimizes the total cost rate, loss of power supply probability (LPSP), and CO 2 emissions simultaneously. The main innovation of this study lies in the comprehensive integration of solar, wind, and wave energy with hydrogen storage within a single building-scale system, which has not been previously explored in such detail for coastal environments. The results reveal that the optimized configuration, consisting of 500 PV modules, 13 VAWTs, and 35 OWCs, achieves a CO 2 emission of 84.45 tons per year, an LPSP of 0.179, and an operational cost rate of 472 EUR/hour, representing an 81% reduction in annual carbon emissions compared to grid-dependent operation. The findings demonstrate that combining diverse renewable resources with hydrogen storage not only enhances system reliability and autonomy but also offers a scalable framework for developing cost-effective and low-carbon near-zero energy buildings in coastal and island regions. • Customized hybrid renewable system for a coastal hotel using PV, VAWT, and OWC. • TRNSYS linked with OpenStudio/EnergyPlus for real-time building energy evaluation. • H 2 storage improves autonomy and reduces grid dependency. • NSGA-III optimization integrated with TRNSYS for adaptive system optimization. • Optimal setup: 500 PVs, 13 VAWTs, 35 OWCs; LPSP 0.179, CO 2 84.45 t/yr, cost 472 €/h.

Technical and economic analysis of improving reliability and cost effectiveness of ultra twisted sliding mode control based on advanced transformers in isolated hybrid renewable energy systems

Population growth and economic development have increased the world’s energy consumption, making it more difficult to manage peak loads and lower the cost of home energy management systems (HEMS). This has led to a need for smart, flexible solutions that incorporate renewable resources to improve sustainability and economic efficiency. To optimize power flow in a hybrid renewable energy system (HRES), this study suggests a fuzzy logic-based energy management system (Fuzzy-EMS) that is improved with reinforcement learning and optimized using the Starfish Optimization Algorithm (SFOA). It incorporates solar photovoltaic (PV), wind turbines (WT), battery storage systems (BSS), and electric vehicles (EVs). Adaptive handling of uncertainties in renewable generation and load demand using a Takagi–Sugeno fuzzy reinforcement learning model with triangular membership functions and 81 rules, real-time energy trading with the upstream grid, and a multi-objective framework that balances cost minimization and renewable utilization maximization are among the main contributions. Cost reductions of 35.2%, 23.8%, and 26.43% under fixed pricing, real-time pricing (RTP), and day-ahead pricing (DAP) models, respectively, are examples of how MATLAB simulations outperform well-known techniques. Furthermore, the system outperforms diesel-based systems by lowering operating costs and carbon emissions by 11.87–18.7% and increasing the use of renewable energy by up to 70% in hybrid scenarios, resulting in a net present cost (NPC) of $269,246 and a levelized cost of electricity (LCOE) of $0.281 over a 20-year period.

The increasing demand for sustainable energy in residential buildings and public concerns on greenhouse gas (GHG) emissions has driven the integration of smart homes with hybrid renewable energy systems (HRESs). This research proposes an optimal scheduling strategy for home energy consumption in a grid-connected HRES that comprises a grid, wind turbines, photovoltaics and battery storage systems. The objective of the study is to reduce the net energy cost, scheduling inconvenience cost (SIC), GHG cost and battery degradation cost. An ant colony optimization algorithm is utilized in the MATLAB environment, with load profiles and meteorological data of Upington, South Africa, obtained from NASA and a residential consumption dataset to accomplish the objectives of the study. The outcomes of the study show that case study 3 is the most feasible configuration based on a net energy revenue cost of $9.8382, GHG cost of $0.0627, battery degradation cost of $0.461 and SIC of $0.66. Simulation results demonstrate that energy purchased from the grid has been reduced by 98% and 48% relative to case studies 1 and 2. The results of the study can assist households to improve the sustainability and resilience of the power system in residential environments where the grid supply is unstable and electricity costs are high.

This study investigates the feasibility of a hybrid renewable energy system consisting of solar photovoltaic, wind turbine, and battery storage to supply individual street lighting on the Merah Putih Bridge, Ambon. Renewable resource assessment was conducted using measured wind data and NASA solar radiation data for the period 2018–2020. The results indicate that the study location possesses favorable renewable energy potential, with an average wind speed of approximately 5.57 m/s and solar radiation exceeding 4 kWh/m²/day throughout the year. Seasonal analysis demonstrates complementary behavior between solar and wind resources, supporting the suitability of hybrid energy implementation. The hybrid system was designed to supply a 60 W LED lamp operating for 12 hours per day, corresponding to a daily energy demand of 720 Wh. The proposed configuration includes a 200 W photovoltaic module, a 400 W wind turbine, and a 12 V 100 Ah battery. Performance analysis shows that the system can generate approximately 3150 Wh/day, ensuring reliable operation with sufficient energy reserve and more than one day of battery autonomy. Economic evaluation indicates a total investment cost of approximately 1350 USD, with a Levelized Cost of Energy of 0.13 USD/kWh and a payback period of 13.7 years under a local electricity tariff of 0.086 USD/kWh. Lifecycle cost comparison demonstrates that the hybrid system is competitive with grid extension alternatives, particularly when infrastructure costs are considered. Additionally, the system can reduce approximately 0.98 tons of CO₂ emissions annually per lighting unit. The results confirm that the proposed hybrid solar–wind–battery system is technically feasible, environmentally sustainable, and suitable for decentralized bridge lighting applications.

The article has considered renewable energy sources with high energy potential, which in the near future will become the fastest growing source of electricity. Generation sources include solar, wind, and biomass resources, which contribute to economic growth and reduce pollution. Optimizing the renewable and sustainable energy project is a key factor as a reliable alternative to conventional hydrocarbons, as well as as an energy source. It can play a significant role in the future of renewable and sustainable energy in Iraq. In the work, Helioscope and HOMER Pro software are used to create a small model connected to a network and to estimate energy consumption for optimization purposes. The results have showed an internal rate of return of 12 %, as well as about 8.5 % return on investment, and the share of the renewable energy component is almost 99.7 %. The proposed method proved to be effective in terms of using renewable energy. The research can be applied in any country, especially in the neighboring countries of Iraq.

Fossil fuels are finite. Global reserves are diminishing, and their contribution to increased climate risks signifies a demand for alternatives. Renewable sources have solidified their position as the future of energy, with global initiatives underway to transition towards a net zero future. This transition comes with challenges, one being the rising incidence of negative energy prices from increased renewable penetration. This paper provides insight into the operation of a Hybrid Renewable Energy System within a region of high negative price market saturation and contributes to developing optimization strategies for transitioning regions, using South Australia as a case study. A Mixed-Integer Linear Programming model is developed with two primary objectives: maximizing Self-Sufficiency and minimizing Net Operating Costs. The model is executed under four weather scenarios and three battery sizes and evaluated for Self-Sufficiency, Net Operating Cost, and Curtailment. Results found the Low Solar/High Wind scenario produced the greatest performance due to its steady renewable profile, whereas battery scaling demonstrated diminishing returns. This indicates battery scaling alone is insufficient in tackling transitional challenges. The study highlights the demand for more extensive strategies to support net zero transitions and minimise the impact and occurrence of negative market prices.

Background: Hybrid Renewable Energy Systems (HRES) are considered an attractive option for rural electrification in off-grid areas, where solar, wind and biomass/micro-hydro resources can be integrated with storage technologies to improve the reliability and lessen dependence on diesel. With the rapidly evolving optimisation techniques, energy management schemes and the methodologies of developing smart microgrids, it requires a fresh review of these topics. Objectives: This review investigates acceptable technical, economic, environmental and policy pathways of HRES; discusses successes and barriers encountered in the past; suggests a better operation regimen to meet next-generation rural microgrids. Method: A systematic literature search was performed using predefined keywords in hybrid systems, rural electrification, energy storage and optimisation. The study selection process was conducted according to PRISMA-compliant protocols and included studies published from 2020 to 2025. The selection criteria were multi-source HRES with quantitative performance information. Exclusion criteria removed single-source systems and studies lacking technical indicators. Comparative analysis considered reliability metrics, levelized cost of energy, renewable penetration, emission reduction, and control strategies. Findings: Recent studies show a 30–40%increase in reliability, 10–25% less expensive and 40–60% emission reduction relative to diesel ones. Primary bottlenecks are due to high up-front costs of capital, battery degradation and high dependence on the financing environment, as well as climate sensitivity. Innovative concepts such as AI-based control, hybrid battery–hydrogen storage, and IoT-enabled monitoring facilitate the optimisation of overall system performance. Significance: This review presents more insight into self-adaptive HRES with Digital Twin technology, which fills in the gap of previous reviews by focusing on life-cycle optimal operation, climate resilient HRES, predictive maintenance and community-centric operation and offers novel understandings to scalable rural electrification. Keywords: Hybrid Renewable Energy Systems, Off-Grid Electrification, Digital Twin, Optimisation, Rural Energy Access

Petrochemical hybrid renewable energy systems (PHRESs), integrating renewable and fossil energy sources, have garnered more and more attention for sustainable manufacturing. However, achieving concurrent optimization of energy supply reliability and carbon mitigation in these complex systems remains a critical challenge. This study proposes an innovative bilateral optimization framework coupling supply-side energy management with demand-side flexibility. On the supply side, a scenario-based two-stage stochastic programming method synergizes with energy storage systems to address renewable energy intermittency, considering a time-of-day tariff from the grid. On the utilization side, heat energy-based and shaft work-based energy interchangeability are introduced and leveraged to enable both qualitative and quantitative flexibility in process unit requirements and thus obtain energy consumption relaxation models for relaxing the design boundaries of PHRESs. These dual strategies are then coupled in a two-stage mixed-integer programming model framework for the optimal design of PHRESs. Applied to a large-scale refinery incorporating carbon taxation and dynamic electricity price, the proposed methodology demonstrates superior performance through five comparative cases. Compared to the Base Case, the Optimal Case using the proposed method can reduce the total annual cost by 14.82%, and stochastic programming reveals over a 40% probability of carbon mitigation in the uncertain space.

This work proposes an enhanced sliding mode control (ESMC) strategy for parallel-integrated boost converters (PIBC) employed in a single-stage power conversion system for hybrid solar-wind energy applications. The key novelty lies in the adaptive edge layer-based ESMC, which effectively suppresses chattering without sacrificing the fast dynamic response characteristic of conventional sliding mode control (SMC). By directly interfacing photovoltaic (PV) and wind energy sources through a single-stage converter, the proposed architecture eliminates intermediate power conditioning stages, thereby reducing system complexity and improving conversion efficiency. The proposed ESMC ensures robust voltage regulation and improved power quality under fluctuating renewable inputs and load disturbances, addressing a critical challenge in hybrid renewable energy systems (HRES). Unlike traditional SMC and sinusoidal pulse-width modulation (SPWM)-based approaches, the proposed method adaptively responds to input variations while maintaining stable and reliable operation. The effectiveness and robustness of the ESMC-based PIBC are validated through simulation and experimental investigations, demonstrating its suitability as a high-performance and scalable solution for efficient power conversion in HRES.

The Philippines, which is rich in natural resources, has significant biomass potential. Among the country’s renewable energy sources, biomass is currently the slowest-growing in terms of power generation. Various types of biomass resources with full or partial use in Laguna Province include bagasse, sweet sorghum, coconut, rice husk, corn cobs, and municipal solid waste. Additionally, the adoption and implementation of HRESs (hybrid renewable energy systems) are mainly achieved through large-scale projects. This paper intentionally showcases highly optimized hybrid configurations for off-grid microgrids to promote rural electrification in Laguna, with a focus on various technoeconomic parameters, specifically the minimization of net present costs and the levelized cost of electricity across all simulations. Each off-grid scenario was compared with scenarios featuring hybrid renewable energy systems incorporating a biomass generator. Laguna, one of the few provinces in the Philippines with all forms of renewable energy systems present, each with high renewable energy potential and renewable fraction values, was selected as the primary study site in this paper. After optimizing and analyzing technoeconomic parameters such as the net present cost and the levelized cost of electricity, a hybrid biomass-solar-wind energy system is proposed to power off-grid areas in Laguna, thereby supporting rural electrification and decarbonization goals. Scenario simulations and comparisons using hybrid optimization demonstrate that adding battery backup systems improves both economic and environmental performance. This paper highlights two key benefits of including a biomass generator: (1) a 17.0% reduction in long-term carbon emissions for the entire system and (2) approximately 9.4% savings in operation and maintenance costs after seven years. The optimization results support the goal of providing Laguna with power through off-grid, decentralized, community-based hybrid renewable energy systems.

Optimization of power supply for an isolated site from a hybrid renewable energy system, case study: Power supply of the island of El Aouana in Jijel, Algeria