Multi-objective salp swarm optimization for the sizing of a hybrid renewable energy system

• The efficiency of off-grid renewable energy is enhanced by optimal financial allocation. • SDGs 7, 11, and 13 are addressed by hybrid microgrid solutions for rural electricity. • Better microgrid typologies for isolated locations in KPK and Sindh with various energy requirements. • KPIs evaluate the technical, financial, and environmental effects of MGs in KPK and Sindh. • For different discount rates, load demand, inflation rate, wind speed, solar irradiation, and lifespan, sensitivity analysis finds workable hybrids. Hybrid renewable energy systems are common in rural electrification as an inexpensive and eco-friendly alternative. For all these systems, such as techno-economic, sensitivity and environmental are discussed in this paper and the way in which these systems can be utilized in achieving sustainable development goals. Remote, unelectrified locations are identified using a geospatial analytic method. The suggested battery-powered PV-hydro-B-DG system for Ayun Chitral provided the lowest LCOE and NPC of $692,000 and $0.0880/kWh, in that order. On the other hand, the maximum NPC and the LCOE of $2,460,000 and 0.212$/kWh are provided by PV with a battery storage system. The installation of microgrids powered by renewable energy sources is predicted to cut emissions by up to 95.35%. Nowshera has the highest DG contribution and emits 20,911 kg of GHG annually. To guarantee the technological dependability of the suggested system, a thorough investigation is conducted. Sensitivity analysis looks at the effects of unclear parameters on both NPC and LCOE. This paper provides a comparison of proposed system with the existing literature in terms of LCOE and NPC. This research provides a pathway for U.N.-SDGs and for Pakistan to achieve its own target of renewable expansion.

This study proposes an Improved Modified Invasive Weed Optimization-based PID (IMIWO-PID) controller for Hybrid Renewable Energy Systems (HRES). The novelty of the work lies in the integration of adaptive seed dispersal and Lévy flight strategies into the invasive weed optimization process, ensuring faster convergence, enhanced global search capability, and improved robustness against nonlinearities and uncertainties. Unlike most existing controllers, the proposed method is validated not only through simulation but also via Hardware-in-the-Loop (HIL) experiments, confirming its practical feasibility. A comparative analysis against Fractional-Order Proportional-Integral-Derivative (FO-PID), Fractional-Order Proportional-Derivative–Proportional-Integral (FO-(PD-PI)), Grey Wolf Optimization-based PID (GWO-PID), and Fractional-Order Fuzzy PID (FO-F-PID) demonstrates that the IMIWO-PID achieves significantly reduced overshoot, undershoot, and settling time while maintaining scalability for larger microgrid systems. The results indicate that the IMIWO-PID controller achieves undershoot values of 0.005289, 0.003, and 0.003119; overshoot values of 0.002023, 0.000702, and 0.00132; settling times of 2.09 s, 3.69 s, and 2.2 s; and performance indices of 2.537, 0.302, and 2.096 across varying test conditions. Sensitivity analysis further substantiates its robustness, with a performance index of 2.97. The novelty lies in the adaptive weed dispersal and Lévy flight integration, which enables faster convergence and enhanced resilience to uncertainties. Both simulation and Hardware-in-the-Loop (HIL) results demonstrate superior performance, with undershoot as low as 0.003 p.u., overshoot below 0.0007 p.u., and a settling time of 2.09 as compared to other controllers. The findings confirm that the IMIWO-PID minimizes transient deviations and ensures stable frequency regulation in complex HRES environments. • Significant reduction in undershoot (0.003), overshoot (0.000702), and settling time (3.69 s), ensuring faster and stable system responses. • Robust performance under fluctuating loads and nonlinear dynamics, as confirmed by comparative and sensitivity analyses. • Effective frequency regulation in hybrid distributed energy systems integrating solar, wind, storage, and EVs. • Centralized control enhances operational efficiency by simplifying maintenance and optimizing parameter tuning.

Optimizing green hydrogen integration in grid-connected hybrid renewable systems: Techno-economic and sensitivity analysis in hot desert environments

Enhancing hybrid renewable system performance through load shifting: A multi-objective optimization and forecasting approach

Optimal design of hydrogen storage-based hybrid renewable systems: A case study using the particle swarm optimization (PSO) algorithm in Meknes, Morocco

Design and optimization of grid-connected hybrid renewable energy systems with EV integration for supermarkets

Optimized design of a hybrid renewable energy system with biogas energy storage for sustainable rural electrification: A case study in Sempu, East Java, Indonesia

A comprehensive review of optimizing hybrid renewable energy system using optimization techniques

• Hybrid HAWT–PV prototype with interchangeable 3-, 4-, and 5-blade rotors is experimentally and numerically characterized for compact small-scale generation. • Blade-number effect clarified: more blades reduce vibration and shift peak response to higher frequencies, while 5 blades give the highest torque and C p with the most stable electrical output. • Validated CFD–experiment agreement shows smoother, more uniform flow at higher solidity, and the 5-blade hybrid delivers up to 28.9 W, outperforming stand-alone wind or solar units in daily energy yield. Global growth in electricity demand and the environmental impact of fossil-fuel–based generation motivate the development of compact, efficient, and structurally robust small-scale renewable systems. This study develops and validates an integrated hybrid generation system that structurally combines a horizontal-axis wind turbine (HAWT) with 3-, 4-, and 5-blade rotors and a photovoltaic panel within a single support framework. The objective is to design a compact prototype, experimentally characterize its aerodynamic and electrical performance, and evaluate its structural integrity through numerical simulation. The development method integrates experimental testing, computational fluid dynamics (CFD), structural modal and harmonic response analysis using numerical simulation, and GIS-based feasibility assessment incorporating land–energy planning and seasonal wind variability. Experimental results show that the 3-blade rotor achieves higher rotational speed and a broader, nearly linear TSR range, making it suitable for stronger wind conditions. The 5-blade rotor produces the highest torque and power coefficient ( C P ) while maintaining the lowest vibration amplitude and more stable electrical output. Harmonic analysis indicates that increasing blade count reduces vibration amplitude and shifts peak response toward higher frequencies, improving operational stability. CFD simulations corroborate these findings, revealing smoother and more uniform flow fields with increasing rotor solidity. When integrated with the solar panel, the 5-blade configuration delivers a maximum combined electrical output of 28.9 W. The results demonstrate that structural integration, supported by coupled experimental–numerical validation and geo-spatial feasibility analysis, enhances daily energy yield and provides a practical design pathway for small-scale coastal hybrid renewable energy systems.

To achieve sustainable hydrogen production, systems powered by renewable energy must replace conventional fossil fuel-based routes. This study evaluates the techno-economic feasibility and optimal sizing of seven distinct hybrid renewable energy system configurations—comprising wind, solar, biomass, and pumped hydro energy storage (PHES)—for producing one ton of green hydrogen per day in Finland. The energy systems are modelled by Aspen Plus and MATLAB and analyzed within tax-based and non-tax-based frameworks concerning biogenic CO 2 emissions from biomass combustion. Four multi-objective optimization problems use the non-dominated sorting genetic algorithm II (NSGA-II) to determine the Pareto set for four scenarios that minimize the levelized cost of hydrogen (LCOH) and capital expenditure (CAPEX). A decision-making method is then employed to choose the best solutions. In addition, the net present value (NPV) and payback period are evaluated to assess system profitability. The findings indicate that biomass-based systems can compete with wind and solar in green hydrogen production, although policy instruments—particularly biogenic CO 2 taxation—significantly influence system design and cost. The results show that wind-PHES and solar-PHES systems can produce hydrogen at costs of about $3.2/kg and $3.6/kg, respectively. PHES contributes roughly 11% of the total CAPEX in the wind-based system, compared to 31% in the solar-based configuration. The exclusive use of biomass coupled with a Rankine cycle to power the electrolyzer results in an LCOH of $7.3/kg under biogenic CO 2 taxation, compared to $3.6/kg in the absence of such a tax. The study highlights the importance of incorporating storage or complementary energy systems, such as PHES or biomass, to support configurations relying primarily on wind and solar resources. • Optimal sizing of renewables to steadily produce one tonne of hydrogen per day. • The system is modeled by Aspen Plus and MATLAB and assessed in seven scenarios. • Four multi-objective optimization problems based on the NSGA-II are carried out. • Carbon taxation heavily impacts the system design and hydrogen pricing. • Biomass can produce hydrogen at low LCOH and CAPEX.

Load frequency control in hybrid renewable energy systems faces challenges from intermittent sources and reduced grid inertia. Traditional metaheuristic optimization techniques like Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) suffer from computational complexity and premature convergence to local optima. This research presents an enhanced Deep Q-Network approach for optimizing PID controller parameters in load frequency control. The methodology analyzes a two-area system where Area 1 combines thermal and solar photovoltaic generation, while Area 2 integrates thermal and wind power. The novelty lies in integrating prioritized experience replay with importance sampling, domain-specific reward functions, and augmented state representation enabling model-free learning and real-time adaptation superior to conventional approaches. Experimental validation encompasses six comprehensive test scenarios, with baseline performance benchmarked against PSO-PID, GWO-PID and DDQN-PID controllers. Results demonstrate substantial improvements across key performance metrics. Settling times show reductions of 26.3% to 39.1%, while frequency deviations decrease by 29.0 to 51.3%. Total performance improvements range from 24.6 to 64% across different operating conditions. The controller demonstrates robust performance under challenging operational scenarios. Stability is maintained during parameter variations up to ± 50%, with effective management of load disturbances reaching 25%. The system also adapts effectively to renewable energy source variability across different time periods.

The world is increasingly shifting toward sustainable and renewable energy systems due to limited fossil fuel sources as well as their negative environmental impacts. Remote coastal tourism facilities face additional challenges because of the high fuel transportation and operation costs. This study focuses on a seasonal analysis and optimization of an off-grid hybrid renewable energy system comprising photovoltaic panels, wind turbines, and a battery energy storage system designed to supply a large five-star hotel located in Safaga, Egypt. Four advanced metaheuristic optimization techniques are used to minimize the cost of energy while maintaining system reliability. Unlike conventional annual sizing approaches, the proposed analysis evaluates the system on a seasonal basis using hourly load and weather data, and compares the optimization algorithms under the same framework to examine their effect on system sizing and cost performance. The analysis results indicate that Fungal Growth Optimizer (FGO) provides competitive solution quality with satisfactory computational performance compared to the other algorithms and the system achieves its minimum Levelized Cost of Energy (LCOE) of 0.098854 USD/kWh during the summer season and the maximum value of 0.159970 USD/kWh occurs in autumn. In summary, the findings confirm that the proposed sustainable system yields a reliable and cost-effective solution in addition to reducing carbon emissions in the Red Sea region.

Integrating renewable energy (RE) into power generation systems enhances sustainability by reducing greenhouse gas emissions, strengthening energy security, lowering operational costs, and promoting sustainable development, particularly in remote or underserved areas. This paper investigates the integration of RE into Mosul's power infrastructure through a hybrid renewable energy system (HRES) comprising the electrical grid, photovoltaic (PV) panels, pumped hydro energy storage (PHES), and an electrolyzer. Using HOMER Pro software, three system configurations were evaluated to optimize component sizing and assess techno-economic and environmental performance under the operating conditions of a hot semi-arid climate in northern Iraq. Among these configurations, the PV/grid/PHES/electrolyzer system demonstrated the best performance, achieving a renewable energy penetration of 254%. The proposed system results in a net present cost of $9.75million and a levelized cost of energy of $0.06673/kWh. Despite modest reductions in operation and maintenance costs, the system demonstrates significant long-term economic efficiency when evaluated over its lifetime and projected revenues. From an environmental perspective, the proposed design achieves an annual reduction of approximately 18,089.31 tons of CO₂, corresponding to an estimated carbon credit value of $271.34K, thus contributing to both sustainability and economic resilience. The findings confirm that the proposed HRES is a viable, cost-effective, and sustainable energy solution for Mosul and other regions with similar climatic and energy characteristics.

This study investigates the integration of artificial intelligence (AI) and machine learning (ML) technologies in solar-biomass hybrid energy systems for sustainable energy generation. Through a comprehensive empirical analysis of 250 hybrid energy installations across five regions, this research evaluates the performance optimization potential of AI/ML algorithms in renewable energy management. The study employed a mixed-methods approach with quantitative data collection from operational systems over 24 months (2023-2024). Results demonstrate that AI-enhanced solar-biomass systems achieved 34.7% higher energy efficiency compared to conventional systems, with ML-based predictive maintenance reducing operational costs by 28.3%. The study found significant correlations between AI implementation and system reliability (r=0.847, p<0.001), while deep learning models improved energy forecasting accuracy to 92.4%. Integration challenges were identified in 18.2% of installations, primarily related to data synchronization and algorithm compatibility. These findings contribute to advancing sustainable energy technologies through intelligent optimization frameworks, providing empirical evidence for the transformative potential of AI/ML in hybrid renewable energy systems.

The growing demand for electricity and fresh water in remote and coastal regions necessitates sustainable solutions that reduce reliance on fossil fuels. Hybrid renewable energy systems, which combine solar, wind, and storage technologies, have proven effective in ensuring a reliable supply and environmental sustainability. However, few studies have addressed large-scale hybrid applications that simultaneously meet electricity and desalination needs in coastal areas, and the integration of pumped-hydro storage with PV and wind in Egypt's high-potential regions is underexplored. In particular, previous work has rarely incorporated realistic mixed residential, agricultural, and desalination load profiles or applied Diversity Factors to represent actual consumption behavior. This paper investigates the techno-economic feasibility of a hybrid system integrating photovoltaic (157.6MW), wind (166.8MW), and pumped-hydro storage (223,661kWh) to supply Ras Ghareb, Egypt, using HOMER Pro simulations and real data for 5000 residential homes, agricultural machinery for irrigating 2000 acres of farmland, and the power demands of a desalination plant. The optimized system achieves a Renewable Fraction of 93.8% with no unmet load. Economically, the proposed system demonstrates strong performance, with a Net Present Cost of - $94.7 million, an Internal Rate of Return of 53%, and a simple payback period of 1.9years, driven by selling surplus power to the grid. The qualitative sensitivity trends indicate that the system remains robust under reasonable variations in resource conditions and pricing assumptions. Environmentally, the system reduces annual CO₂ emissions by 291.7million kg, SO₂ emissions by 1.26million kg, and NOx emissions by 0.62million kg. These results also provide relevant insights for ongoing national energy and water strategies, particularly regarding renewable expansion and long-duration storage in coastal regions. The findings confirm the system's technical reliability, financial feasibility, and environmental benefits, positioning the system as a scalable model for sustainable energy in remote and coastal regions.

The increasing share of renewables forces combustion-driven power generation machines to operate with high adaptability. Small-scale heat and power units, like micro gas turbines (mGT), could show potential for Hybrid Renewable Energy Systems (HRES) due to their operational and fuel flexibility. However, their economic viability in a variable energy environment remains uncertain. The levelized cost of exergy (LCOX) in HRES with combined heat and power (CHP) units, should be calculated under uncertainties to support decision-making. This paper applies design optimization to a photovoltaic/battery/heat pump system with two prime movers to investigate the required economic relevance of small-scale CHP units in HRES. The considered prime movers are internal combustion engines (ICE) and mGTs, including advanced mGT configurations such as humidified cycles (mGT-wet, mHAT). Test cases include a hospital, office and hotel in Brussels with distinct demand profiles. Uncertainty quantification on optimized system capacities is performed using Polynomial Chaos Expansions (PCE), ensuring computational efficiency. The hospital shows that adding an mGT improves LCOX (227 €/MWh compared to 244 €/MWh of non-PM HRES), while in the hotel case, non-PM systems are more cost-effective due to low electricity demand. Dry mGTs and ICEs enhance flexibility in HRES for suitable applications, but their economic viability depends strongly on energy prices and demand. Humidified mGTs improve self-sufficiency but with higher fuel cost, making their cost-effectiveness questionable if excess electricity is not sold to the grid. • Techno-economic analysis of mGTs in hybrid renewable energy systems. • Optimization balances levelized cost of exergy and self-sufficiency. • Potential of applying CHP units in buildings with high energy demand. • Heat recovery steam generator use is key for cost-effective mGT systems in hospitals. • Micro humid air turbine suits users shifting cost sensitivity from electricity to gas.

A multi-objective optimization framework is developed for designing a hybrid renewable energy system (HRES) for Hormuz Island, integrating photovoltaic, wind, lithium-ion battery, proton-exchange-membrane electrolyzer-tank-fuel-cell, and a standby diesel generator. Real meteorological and demand data are employed to minimize the Levelized Cost of Energy (LCOE) and Net Present Cost (NPC) while maximizing the Renewable Energy Fraction (REF) and system resilience. The optimization integrates multiple conflicting techno-economic and environmental objectives through a TOPSIS-guided multi-criteria framework, where a scalar closeness coefficient is used as the fitness function within the search process, without explicit Pareto-front construction. Results indicate that, compared with the diesel baseline, the optimized configuration achieves an LCOE of 0.139 USD kWh −1 , a 38.6 % reduction in NPC, a REF of 87 %, and nearly 89 % GHG mitigation. The discount rate exhibits the highest sensitivity, inducing ± 14.8 % variability in NPC and ± 9.2 % in LCOE, followed by battery CAPEX (±9.5 % NPC) and PV CAPEX (±7.1 %). Resilience evaluation under 10 % PV and 20 % hydrogen-storage perturbations confine the Loss-of-Load Probability to ≤ 2 %, confirming robust operation under adverse climatic fluctuations. Life-cycle assessment demonstrates approximately 20 % reduction in CO 2 -equivalent emissions, while techno-economic analysis indicates about 15 % reduction in total energy cost relative to conventional diesel supply. The proposed configuration provides a replicable blueprint for off-grid, climate-vulnerable islands seeking reliable and low-carbon electrification pathways.

Adaptive nonlinear control and energy management for grid-connected hybrid wind–PV systems using Vienna converters

Enhancing off-grid hybrid renewable energy systems through multi-objective optimization with Pareto front analysis