Search Results (55)

This paper compares two common dispatch policies—Load-Following (LF) and Cycle-Charging (CC)—for a photovoltaic Battery Energy Storage System (PV–BESS) microgrid (MG) with a 12 kW diesel generator, using a full-year of real 15 min PV and load data from an industrial use case in Germany. A forward time-step simulation enforces the battery State-of-Energy (SoE) window (total basis [20, 100] %, DoD = 80%) and computes curtailment, generator use, and unmet energy. Feasible designs satisfy a Loss of Power Supply Probability (LPSP) ≤ 0.03. Economic evaluation follows an Equivalent Annual Cost (EUAC) model with PV and BESS Capital Expenditure/Operation and Maintenance (CAPEX/O&M) (cycle life dependent on DoD and 15-year calendar life), generator costs, and fuel via SFC and diesel price. A value of lost load (VOLL) can be applied to unserved energy, with an optional curtailment penalty. Across the design space, a clear cost valley appears toward moderate storage and modest PV, with the baseline optimum at ≈56 kWp PV and 200 kWh BESS (DoD = 80%). Both policies meet the reliability target (in our runs LPSP ≈ 0), and their SoE trajectories are nearly identical; CC only lifts the SoE slightly after generator-ON events by using headroom to charge, while LF supplies just the residual deficit. Sensitivity analyses show that the optimum is most affected by diesel price and discount rate, with smaller shifts for ±10% changes in SFC. The study provides a transparent, reproducible workflow—grounded in real data—for controller selection and capacity planning. Full article

The use of non-renewable energy resources is one of the main drivers of climate change. In response, the United Nations established the seventh Sustainable Development Goal, “Affordable and clean energy”, which promotes the transition toward renewable and environmentally friendly sources such as wind and solar energy. However, the intermittent nature of these resources poses challenges for maintaining a stable, continuous power supply, highlighting the need for hybrid technology approaches, such as Hybrid Renewable Energy Systems (HRES), which integrate complementary renewable sources with energy storage. In this context, this study applies a Particle Swarm Optimisation (PSO)-based approach to determine the optimal sizing and operating strategy for a hybrid system comprising photovoltaic, wind, battery storage, and diesel backup units under various synthetic load profiles. The results indicate that diesel-assisted configurations achieve lower levelized costs of energy (0.23–0.35 USD/kWh) and maintain high reliability (LPSP < 0.25%), although at the expense of higher fuel consumption and CO₂ emissions. Conversely, fully renewable configurations present higher energy costs (0.29–0.44 USD/kWh), but reduce annual CO₂ emissions by up to 50% and create more employment opportunities, particularly in regions with abundant wind resources such as La Guajira, Colombia. Full article

Developing sustainable and high-performance sorbents for efficient CO₂ capture is essential for mitigating climate change and reducing industrial emissions. In this study, phosphorus-doped porous carbons (LPSP-T) were synthesized via a one-step activation–doping strategy using lotus petiole biomass as a precursor and sodium phytate as a dual-function activating and phosphorus-doping agent. The simultaneous activation and phosphorus incorporation at various temperatures (650–850 °C) under a nitrogen atmosphere produced carbons with tailored textural properties and surface functionalities. Among them, LPSP-700 exhibited the highest specific surface area (525 m²/g) and a hierarchical porous structure, with abundant narrow micropores (<1 nm) and phosphorus-containing surface groups that synergistically enhanced CO₂ capture performance. The introduction of P functionalities not only improved the surface polarity and binding affinity toward CO₂ but also promoted the formation of a well-connected pore network. As a result, LPSP-700 delivered a CO₂ uptake of 2.51 mmol/g at 25 °C and 1 bar (3.34 mmol/g at 0 °C), along with a high CO₂/N₂ selectivity, fast CO₂ adsorption kinetics and moderate isosteric heat of adsorption (Q_st). Furthermore, the dynamic CO₂ adsorption capacity (0.81 mmol/g) was validated by breakthrough experiments, and cyclic adsorption–desorption tests revealed excellent stability with negligible loss in performance over five cycles. Correlation analysis revealed pores < 2.02 nm as the dominant contributors to CO₂ uptake. Overall, this work highlights sodium phytate as an effective dual-role agent for simultaneous activation and phosphorus doping and validates LPSP-700 as a sustainable and high-performance sorbent for CO₂ capture under post-combustion conditions. Full article

This study presents a novel multi-objective optimization (MOO) model for the design of an off-grid hybrid renewable energy system (HRES) to support sustainable agriculture and rural development in Sub-Saharan Africa (SSA). Based upon a case study selected in Linia (Chad), three system architectures are compared under different levels of the reliability requirements (LPSP = 1%, 5%, and 10%). A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to optimize the Levelized Cost of Energy (LCOE), CO₂ emissions mitigation, and social impact, referring to the Human Development Index (HDI) enhancement and the job creation (JC) opportunity, using the MATLAB R2024b environment. The calculation results show that among the three configuration schemes, the PV–Wind–Battery configuration obtains the optimal techno–economic–environmental coordination, with the lowest LCOE (0.0948 $/kWh) and the largest CO₂ emission reduction (9.58 × 10⁸ kg), and the Wind–Battery system gets the most social benefit. The method developed provides users with a decision-support method for renewable energy systems (RES) integration into rural agricultural settings, taking into consideration financial cost, environmental sustainability, and community development. This information is important for policymakers and practitioners advocating for decentralized, socially inclusive clean energy access initiatives in underserved regions. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households. Full article

This paper aims to find an efficient optimization algorithm to bring down the cost function without compromising the stability of the system and respect the operational constraints of the Hybrid Renewable Energy System. To accomplish this, MATLAB simulations were carried out using three optimization techniques: Grey Wolf Optimization (GWO), Teaching–Learning-Based Optimization (TLBO), and Multi-Objective Particle Swarm Optimization (MOPSO). The study compared their outcomes to identify which method yielded the most effective performance. The research included a statistical analysis to evaluate how consistently and stably each optimization method performed. The analysis revealed optimal values for the output power of photovoltaic systems (PVs), wind turbines (WTs), diesel generator capacity (DGs), and battery storage (BS). A one-year period was used to confirm the optimized configuration through the analysis of capital investment and fuel consumption. Among the three methods, GWO achieved the best fitness value of 0.24593 with an LPSP of 0.12528, indicating high system reliability. MOPSO exhibited the fastest convergence behaviour. TLBO yielded the lowest Net Present Cost (NPC) of 213,440 and a Cost of Energy (COE) of 1.91446/kW, though with a comparatively higher fitness value of 0.26628. The analysis suggests that GWO is suitable for applications requiring high reliability, TLBO is preferable for cost-sensitive solutions, and MOPSO is advantageous for obtaining quick, approximate results. Full article

This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective particle swarm optimization (MOPSO), the study simultaneously optimizes three key objectives: economic performance (maximizing net present value, NPV), system reliability (minimizing loss of power supply probability, LPSP), and operational efficiency (reducing curtailment). The optimized HPP (283 MW wind, 20 MW solar, and 500 MWh BESS) yields an NPV of $165.2 million, a levelized cost of energy (LCOE) of $0.065/kWh, an internal rate of return (IRR) of 10.24%, and a 9.24-year payback, demonstrating financial viability. Operational efficiency is maintained with <4% curtailment and 8.26% LPSP. Key findings show that grid imports improve reliability (LPSP drops to 1.89%) but reduce economic returns; higher wind speeds (11.6 m/s) allow 27% smaller designs with 54.6% capacity factors; and tax credits (30%) are crucial for viability at low PPA rates (≤$0.07/kWh). Validation via Multi-Objective Genetic Algorithm (MOGA) confirms robustness. The study improves hybrid power plant design by combining weather predictions, policy changes, and optimizing three goals, providing a flexible renewable energy option for reducing carbon emissions. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

Autoimmune pancreatitis is a rare condition of pancreatic inflammation with two classic subtypes. The emergence of a third subtype, ICI-induced pancreatitis, highlights the need for knowledge of each type to ensure accurate diagnosis and treatment. Abbreviations: AIP—Autoimmune pancreatitis; AIP-1—Type 1 autoimmune pancreatitis, also known as lymphoplasmacytic sclerosing pancreatitis (LPSP); AIP-2—Type 2 autoimmune pancreatitis, also referred to as idiopathic duct-centric pancreatitis (IDCP); AIP-3—Type 3 autoimmune pancreatitis, also known as immune checkpoint inhibitor (ICI)-induced autoimmune pancreatitis; IgG4-RD—Immunoglobulin G4-related disease. Full article

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. The proposed system integrates wind turbines (WTS), photovoltaic (PV) panels, Biomass Gasifiers (BMGs), batteries, electrolyzers (ELs), and fuel cells (FCs), aiming to minimize Annual System Cost (ASC), minimize Loss of Power Supply Probability (LPSP), and maximize renewable energy fraction (REF). Results demonstrate that the PSO-optimized system achieves an ASC of USD6,336,303, an LPSP of 0.01%, and a REF of 90.01%, all of which are reached after 25 iterations. When compared to the Genetic Algorithm (GA) and hybrid GA-PSO, PSO improved cost-effectiveness by 3.4% over GA and reduced ASC by 1.09% compared to GAPSO. In terms of REF, PSO outperformed GA by 1.22% and GAPSO by 0.99%. The PSO-optimized configuration includes WT (4669 kW), solar PV (10,623 kW), BMG (2174 kW), battery (8000 kWh), FC (2305 kW), and EL (6806 kW). Sensitivity analysis highlights the flexibility of the optimization framework under varying weight distributions. These results highlight the dependability, cost-effectiveness, and sustainability for the proposed system, offering valuable insights for policymakers and practitioners transitioning to renewable energy systems. Full article

The variability of renewable energy sources, storage limitations, and fluctuations in residential demand affect the reliability of sustainable energy systems, resulting in energy deficits and the risk of service interruptions. Given this situation, the objective of this study is to diagnose and optimize the reliability of a residential microgrid based on photovoltaic and wind power generation and battery energy storage systems (BESSs). To this end, genetic algorithms (GAs) and ant colony optimization (ACO) are used to evaluate the performance of the system using metrics such as loss of load probability (LOLP), loss of supply probability (LPSP), and availability. The test system consists of a 3.25 kW photovoltaic (PV) system, a 1 kW wind turbine, and a 3 kWh battery. The evaluation is performed using Python-based simulations with real consumption, solar irradiation, and wind speed data to assess reliability under different optimization strategies. The initial diagnosis shows limitations in the reliability of the system with an availability of 77% and high values of LOLP (22.7%) and LPSP (26.6%). Optimization using metaheuristic algorithms significantly improves these indicators, reducing LOLP to 11% and LPSP to 16.4%, and increasing availability to 89%. Furthermore, optimization achieves a better balance between generation and consumption, especially in periods of low demand, and the ACO manages to distribute wind and photovoltaic generation more efficiently. In conclusion, the use of metaheuristics is an effective strategy for improving the reliability and efficiency of autonomous microgrids, optimizing the energy balance and operating costs. Full article

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management. Full article

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO₂-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. Full article

Globally, the integration of electric vehicles (EVs) in the transportation sector represents a significant step towards achieving environmental decarbonization. This shift also introduces a new demand for electric power within the utility grid network. This study focuses on the design and development of a grid-connected renewable energy system tailored to meet the EV load demands in Taif, Kingdom of Saudi Arabia (KSA). The integration of renewable energy sources, specifically solar photovoltaic (SPV) and wind turbines (WT), is explored within the context of economic feasibility and system reliability. Key considerations include optimizing the system to efficiently handle the fluctuating demands of EV charging while minimizing reliance on conventional grid power. Economic analyses and reliability assessments are conducted to evaluate the feasibility and performance of the proposed renewable energy system. This article discusses the technical sizing of hybrid systems, energy reduction, and net present cost for the selected location. A rigorous sensitivity analysis is performed to determine the impact of major variables such as inflation rate, real discount rate, solar irradiation, and Lack of Power Supply Probability (LPSP) on system performance. The results demonstrate that the Pufferfish Optimization Algorithm (PFO) significantly outperforms other metaheuristic algorithms documented in the literature, as well as the HOMER software. The study found that the grid-connected renewable energy system is the best option for operating EV charging stations at the selected location. The findings underscore the potential for sustainable energy solutions in urban environments like Taif, highlighting the importance of integrating renewable energy technologies to meet growing energy demands with enhanced economic efficiency and system reliability. This initiative seeks to pave the way for a greener and more resilient energy infrastructure, aligning with global efforts towards sustainable development and clean transportation solutions. Full article