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

In recent years, hybrid renewable energy system (HRES) is gaining attention on the field of sustainable development due to its capability in supplying energy in rural and remote areas with higher reliability, affordable cost and lower carbon footprint. In this study, the author implemented hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) in optimally sizing HRES that is composed of PV modules, wind turbine, battery and diesel generator. The main objective is to minimize levelized cost of energy (LCOE) and loss of power supply probability (LPSP). For the 20 independent runs of PSOGSA, it produced an average value of 0.2358 $/kWh for LCOE and 10.63% of LPSP for the proposed HRES in Pamarawan Island, Philippines. Compared to PSO and GSA, PSOGSA gained the lowest LCOE. The study shows the suitability of PSOGSA in optimally designing HRES on island countries like the Philippines.

Hybrid renewable energy system (HRE) sizing is vital for maximizing both economic and environmental performance in grid-connected systems. This study develops a comparative optimization framework for an HRE serving a camp in the southeastern region of Libya, integrating photovoltaic (PV) arrays, wind turbines (WT), battery storage systems (BT), fuel cells (FC), hydrogen tanks (HT) and an Electrolyzer (EL). Three objective functions, Levelized Cost of Energy (LCOE), Grid Reliance Fraction (GRF) and Pollutant Emission Coefficient (PEC), are employed to quantify techno-economic efficiency and environmental impact. To this end, three widely used metaheuristic algorithms; Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO), are employed under identical settings to identify optimal system configurations. Each algorithm is run for a fixed number of iterations, and their diversity and convergence behaviors are monitored to ensure a fair benchmark. The comparative results show that PSO yields the most cost-effective design, achieving an LCOE of $0.1448/kWh, a reduction of 13.5% and 1.3% relative to GA and GWO, respectively and a GRF of 15.73%. These findings demonstrate that PSO strikes the best balance between cost, reliability and computational efficiency for this HRE case study. The framework and insights presented here can guide planners and engineers in selecting the most suitable optimization method for sustainable energy-system design.

Optimal sizing of photovoltaic/wind/battery hybrid renewable energy system including electric vehicles using improved search space reduction algorithm

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

The integration of renewable energy sources is essential for meeting the growing energy demands while mitigating environmental impacts, particularly in regions like Saudi Arabia. This study explores the potential of a solar-wind hybrid energy system integrated with hydrogen fuel cell storage to address the limitations of standalone solar and wind power generation in Saudi Arabia. Using MATLAB and Simulink, we model and simulate energy production from solar photovoltaic (PV) panels and wind turbines in Riyadh and Neom, under real historical climate conditions. The study focuses on optimizing the energy mix between solar and wind, while minimizing the required hydrogen storage capacity to ensure a stable and cost-effective energy supply. The results demonstrate that Neom’s constant wind profile, with an average wind speed of 2.95 m/s, promotes consistent energy generation and minimizes hydrogen storage requirements compared to Riyadh, where fluctuating wind speeds (average 3.31 m/s) lead to higher storage costs. Additionally, under standard test conditions (1000 W/m2 and 25 °C), the 100 kW PV system generated a maximum output of 88 kW, whereas in real conditions, both regions yielded roughly 52 kW due to lower irradiance and MPPT limitations. This study highlights the benefits of hybrid renewable systems for improving energy security and reducing reliance on fossil fuels in Saudi Arabia, while also offering insights into cost-effective storage solutions for regions with variable renewable energy resources.

A flower pollination optimization algorithm for an off-grid PV-Fuel cell hybrid renewable system

Economic aspect of hybrid renewable energy system for Base Transceiver Station (BTS) in Indonesia is analyzed in this paper. This analysis is very useful to examine the feasibility of the hybrid renewable system, compare to existing power supply of utility or diesel generator. Firstly, the design for hybrid renewable energy system for on-grid and off-grid BTS is proposed, to be the basis for economic analysis. Economic analysis is conducted based on Levelized Cost of Energy (LCOE) and Life Cycle Cost (LCC). It can be shown that the resulted LCOE is still higher than utility charge, but much lower than diesel generator operation. Although hybrid renewable energy system has higher initial cost, but in the longtime span it has lower future cost.

The hybrid renewable energy system (HRES) provides locally available green power for remote areas where reach of utility grid is difficult or not possible. The main aim of this paper is to minimize the levelized cost of energy (LCOE) of hybrid renewable energy system (HRES) consisting of wind turbine (WT), solar photovoltaic (PV), diesel generator (DG) and battery. A mathematical model is develop using a recently proposed novel, population based, meta-heuristic which derives its inspiration from politics. The performance of political optimizer (PO), particle swarm optimization (PSO) and interior point algorithm (IPA) is compared for the optimization of the levelized cost of energy (LCOE). The performance of the traditional gradient based solver IPA is compared with PSO and PO for HRES with and without battery integration considering seasonal load variation over the year.

Optimum unit sizing of hybrid renewable energy system utilizing harmony search, Jaya and particle swarm optimization algorithms

Background/Objectives: PV array being shaded partially by buildings, trees or passing clouds is common. This makes the P-V curve of the PV system complex with more than one peak. MPPT algorithm capable of consistently detecting the global peak within a short duration of time is essential. Methods/Statistical Analysis: Lately Particle Swarm Optimization (PSO) algorithm has been used for Maximum Power Point (MPP) tracking due to its ability to locate the MPP irrespective of its location in the P-V curve. This paper evaluates and compares the performance of the basic PSO algorithm and the modified PSO algorithms for ten different shading patterns. Findings: The basic PSO algorithm is compared with three modified PSO algorithms - PSO algorithm with random numbers eliminated, PSO algorithm with linearly varying constants and PSO algorithm with fixed maximum iterations. The basic PSO algorithm gives good results but random numbers in the algorithm tends to make the convergence time random for the same shading pattern and makes hardware implementation difficult. The PSO algorithm with random numbers eliminated overcomes this disadvantage and is found to give good results. But the convergence time is a little higher and varies with shading pattern. The PSO algorithm with fixed maximum iterations gives good performance with shorter and fixed convergence time. Application/Improvements: PSO algorithm with fixed maximum iterations thus improves the responsiveness of the algorithm to rapidly changing patterns of shading. Keywords: Maximum Power Point Tracking, Partial Shading, Particle Swarm Optimization, PV Array

Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria

Communities in Newfoundland and Labrador continue to rely heavily on grid electricity, which is often expensive and vulnerable to weather-related disruptions. In this context, hybrid renewable energy systems offer a practical way to improve energy security while lowering emissions. The transition to clean energy is crucial for mitigating climate change, particularly in Canada, where fluctuating temperatures and environmental shifts pose significant challenges. This study evaluates the techno-economic feasibility of a hybrid renewable energy system designed for residential use in Stephenville, Newfoundland and Labrador, integrating wind turbine technology (Enercon E-44), solar technology (Canadian Solar Dymond), and grid electricity. Using HOMER Pro, the system was optimized based on NASA wind data (average speeds: 7.2 m/s in winter, 5.32 m/s in summer). Results show a levelized cost of energy (LCOE) of $0.0356/kWh, a net present cost of $1.56 million, and annual CO 2 reductions of 222,514 kg, with 60.1% renewable penetration. Computational fluid dynamics (CFD) analysis in ANSYS Fluent, focusing on the NACA 63-415 airfoil, confirmed the turbine’s aerodynamic efficiency across seasonal winds. This study highlights hybrid renewable systems as cost-effective, sustainable solutions, aligning with Canada’s net-zero goals while ensuring energy security.

ABSTRACTDistributed Generation (DG) sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to their environmental friendliness and also the limited sources of fossil fuels. In general, the optimal location and size of DG units have profoundly impacted on the system losses in a distribution network. In the present article, the Particle Swarm Optimization (PSO) algorithm is employed to find the optimal location and size of DG units in a distribution system. The optimal location and size of DG units are determined on the basis of a multi-objective strategy as follows: (i) the minimization of network power losses, (ii) the minimization of the total costs of Distributed Energy Resources (DERs), (iii) the improvement of voltage stability, and (iv) the minimization of greenhouse gas emissions. The related distribution system was assumed to be composed of the fuel cells, wind turbines, photovoltaic arrays, and battery storages. The electrical, cooling, and heating loads were also considered in this article. The heating and cooling requirements of the system consist of time varying water heating load, space heating load, and space cooling load. In this study, the waste and fuel cell were used to produce the required heating and cooling loads in the distribution system. In addition, the absorption chiller was used to supply the required space cooling loads. A detailed performance analysis was carried out on 13 bus radial distribution system to demonstrate the effectiveness of the proposed methodology.

Concept of renewable hybrid energy systems have attracted many utilities and implemented by them too. With such energy systems, customers are not only supplied more economically but also more reliably. Moreover, optimal placements of energy storage systems (ESSs) increase the reliability of such hybrid renewable system up to a great extent. Therefore, this paper presents a new approach for optimal placement and sizing of energy storage systems (ESSs) in hybrid renewable radial distribution system to improve the reliability of such system without violating the system constraints. The cost of energy not supplied (CENS) associated with power service interruption and power shortage is also been considered in the objective function during placement of ESSs. Hence, the proposed optimal placement planning of ESSs is presented with an aim of minimizing the objective function includes cost of energy not supplied (CENS), investment cost and operational cost of ESSs, and power loss in distribution system. It is to be noted that the Particle swarm optimization (PSO) technique is adopted to minimize the objective function. The presented methodology is demonstrated by considering several case studies on 11 kV, 30 bus radial distribution system. Further, a rigorous sensitivity analysis is performed by limiting the number of applied ESSs and varying the maximum capacity of participating ESSs.

As global energy demand and warming increase, there is a need to transition to sustainable and renewable energy sources. Integrating different systems to create a hybrid renewable system enhances the overall adoption and deployment of renewable energy resources. Given the intermittent nature of solar and wind, energy storage systems are combined with these renewable energy sources, to optimize the quantity of clean energy used. Thus, various optimization strategies have been developed for the integration and operation of these hybrid renewable energy systems. Existing studies have either reviewed hybrid renewable energy systems or energy storage systems, however, these studies ignored energy storage systems integrated with hybrid renewable energy systems. This study offers a comprehensive analysis of the optimization methods used in hybrid renewable energy systems (HRES) integrated with energy storage systems (ESS). We examined the optimization models used in the integration of HRES and ESS, their objectives, and the common constraints. Based on our review, capacity and CO2 emissions constraints were frequently used in hybrid optimization techniques that are effective approaches for integrating HRES and ESS. This research supports the move towards sustainable, clean energy solutions by combining an analysis of energy storage techniques with the optimization of hybrid renewable energy systems.