• Energy Research

Abstract Modern wind turbines tend to large scale and capacity, which amplifies the uncertainty of the cost of wind power generation. This paper proposes an analytical framework for uncertainty in the cost of wind power. To do so, the levelized cost of the energy model is improved by considering inflation and the learning curve. On this basis, results from the quasi-Monte Carlo simulation are used for uncertainty analysis that is performed to qualify the uncertainty degree of the levelized cost of energy. Meanwhile, sensitivity analysis based on variance is conducted to study the impact of the uncertainty factors on the levelized cost of energy. Results reveal that through improving the cost model, the levelized cost of energy is changed from 54.11 $/MWh to 37.03 $/MWh in 2018, which is close to the real value of projects built in 2018. Meanwhile, the deterministic values of the levelized cost of energy are similar to P 50 , which means that only 50% credibility is guaranteed by the deterministic design. To achieve a 95% reliability when considering changes caused by uncertainty factors, the margin should be not less than 38% of the determined value. Finally, sensitivity analysis reveals that the interaction effects from scale parameter, shape parameter and air density make the total effect coefficient increased, while the scale parameter is the most influential factor.

Applications for alternative energy sources range from very small scale to large scale grid-coupled hybrid energy systems. In addition, hybrid energy systems are safer to generate power and cheaper to produce than single-source energy systems. The main goal of this study is to determine whether renewable energy hybrid system with horizontal axis wind turbine (HAWT) or vertical axis wind turbine (VAWT) is more efficient and cost effective in terms of energy, economics, and environmental performance. The use of the Multi Objective Genetic Algorithm (MOGA) in MATLAB software for the sizing of hybrid sustainable energy system with wind turbine (horizontal and vertical axis), solar photovoltaic, and grid connection is evaluated in this study. The results revealed that the cost of energy, NPC and system total cost for the case where HRES consists of HAWT is $0.02 /kWh, $85,905, and $332,240, respectively, while for the case when VAWT is used, these values are $0.06 /kWh, $129,932 and $502,511, respectively. The renewable fraction and CO2 emission saving are 80.5% and 73.2% for cases 1 and 2, respectively. The use of renewable energy sources will spread more widely and there will be less air pollution as a result of less reliance on grid electricity. The findings from both situations show that adopting HAWTS-based HRESs is more cost effective and efficient for electrifying rural areas. This study paves the way for researchers to focus on types of wind turbines while designing HRESs.

L'utilisation généralisée des sources d'énergie verte crée une demande importante de stockage d'énergie. Le photovoltaïque flottant hybride (FPV) et le stockage hydroélectrique par pompage (PHS) représentent l'une des solutions les plus fiables et les plus rentables, qui utilise le système photovoltaïque sur le plan d'eau combiné à une paire de lacs de hauteurs différentes. Cette étude se concentre sur le côté charge ainsi que sur le facteur de capacité PHS et vise à réduire le coût de l'énergie (COE) en augmentant le facteur de capacité PHS. Étant donné que la construction d'un PHS nécessite un investissement initial important, cela réduira également le COE et permettra l'acquisition de PHS pour la production d'électricité à grande échelle, ce qui garantira l'accès à l'électricité dans les grandes villes. Pour simuler le système FPV-PHS, l'algorithme génétique multi-objectif (MOGA) est utilisé. Une gestion suffisante de l'énergie est une condition préalable pour atteindre la fiabilité du système dans la meilleure conception et mise en œuvre possible du système d'énergie hybride. Compte tenu de la durée de vie du système de 60 ans, l'analyse du coût net actuel (NPC) montre que, parmi toutes les communautés évaluées, le système FPV-PHS a le NPC et le COE les plus bas. Pour la configuration optimale (FPV (bloc A) 105 MW, PHS 80 MW, FPV (bloc B) 357 MW et la centrale hydroélectrique actuelle), les coûts NPC et énergétiques attendus pour la mise en œuvre du système énergétique hybride (HRS) à l'emplacement choisi sont de 44 737 613 $ et 40 $ / MWh, respectivement. Le système FPV existant s'étend sur 4,65 km2 et réduit la fraction d'évaporation de 17 279 400 m3. Le système hybride FPV-PHS réduit les émissions annuelles de CO2 de 581 830 tonnes. Notre recherche révèle qu'un système hybride PV flottant et hydroélectrique de stockage par pompe offre une électricité propre plus stable, ce qui implique une grande importance pour l'infrastructure du réseau électrique. El uso generalizado de fuentes de energía verde crea una demanda significativa de almacenamiento de energía. La fotovoltaica flotante híbrida (FPV) y el almacenamiento hidroeléctrico por bombeo (PHS) representan una de las soluciones más fiables y rentables, que utiliza el sistema fotovoltaico en la masa de agua combinado con un par de lagos con diferentes alturas. Este estudio se centra en el lado de la carga, así como en el factor de capacidad de PHS y tiene como objetivo reducir el coste de la energía (COE) aumentando el factor de capacidad de PHS. Dado que la construcción de un PHS requiere una inversión inicial significativa, hacerlo también reducirá el COE y permitirá la adquisición de PHS para la producción de energía a gran escala, lo que garantizará el acceso a la energía en las grandes ciudades. Para simular el sistema FPV-PHS, se emplea el algoritmo genético multiobjetivo (moga). Una gestión de energía suficiente es un requisito previo para lograr la fiabilidad del sistema en el mejor diseño e implementación posible del sistema de energía híbrida. Teniendo en cuenta la vida útil del sistema de 60 años, el análisis del coste actual neto (CPN) muestra que, de todas las comunidades evaluadas, el sistema FPV-PHS tiene el CPN y el COE más bajos. Para la configuración óptima (FPV (Bloque A) 105 MW, PHS 80 MW, FPV (Bloque B) 357 MW y la central hidroeléctrica actual), los costos esperados de NPC y energía para implementar el sistema de energía híbrida (HRS) en la ubicación elegida son de $ 44,737,613 y $ 40/MWh, respectivamente. El sistema FPV existente abarca 4,65 km2 y reduce la fracción de evaporación en 17.279.400 m3. El sistema híbrido FPV-PHS reduce las emisiones anuales de CO2 en 581.830 toneladas. Nuestra investigación revela que un sistema híbrido fotovoltaico flotante e hidroeléctrico de almacenamiento por bomba ofrece una electricidad limpia más estable, lo que implica una gran importancia para la infraestructura de la red eléctrica. The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure. يخلق الاستخدام الواسع لمصادر الطاقة الخضراء طلبًا كبيرًا على تخزين الطاقة. تمثل الخلايا الكهروضوئية العائمة الهجينة (FPV) والتخزين المائي المضخوخ (PHS) أحد الحلول الأكثر موثوقية وفعالية من حيث التكلفة، والتي تستخدم النظام الكهروضوئي على المسطح المائي جنبًا إلى جنب مع زوج من البحيرات ذات الارتفاعات المختلفة. تركز هذه الدراسة على جانب الحمل بالإضافة إلى عامل قدرة الصحة العامة وتهدف إلى خفض تكلفة الطاقة (COE) من خلال رفع عامل قدرة الصحة العامة. نظرًا لأن بناء نظام الصحة العامة يتطلب استثمارًا مقدمًا كبيرًا، فإن القيام بذلك سيؤدي أيضًا إلى خفض مركز التميز وتمكين الاستحواذ على نظام الصحة العامة لإنتاج الطاقة على نطاق واسع، مما سيضمن الوصول إلى الطاقة في المدن الكبيرة. لمحاكاة نظام FPV - PHS، يتم استخدام الخوارزمية الجينية متعددة الأهداف (MOGA). تعد إدارة الطاقة الكافية شرطًا أساسيًا لتحقيق موثوقية النظام في أفضل تصميم وتنفيذ ممكن لنظام الطاقة الهجينة. بالنظر إلى عمر النظام البالغ 60 عامًا، يُظهر تحليل صافي التكلفة الحالية (NPC) أنه من بين جميع المجتمعات التي تم تقييمها، فإن نظام FPV - PHS لديه أدنى NPC و COE. بالنسبة للتكوين الأمثل (FPV (Block A) 105 ميجاوات، PHS 80 ميجاوات، FPV (Block B) 357 ميجاوات، ومحطة الطاقة الكهرومائية الحالية)، فإن تكاليف NPC والطاقة المتوقعة لتنفيذ نظام الطاقة الهجينة (HRS) في الموقع المختار هي 44،737،613 دولارًا و 40 دولارًا/ميجاوات ساعة، على التوالي. يمتد نظام FPV الحالي على مساحة 4.65 كم 2 ويقلل من جزء التبخر بمقدار 17,279,400 متر مكعب. يقلل نظام FPV - PHS الهجين من انبعاثات ثاني أكسيد الكربون السنوية بمقدار 581،830 طنًا. يكشف بحثنا أن نظام الطاقة الكهرومائية العائم الهجين ونظام الطاقة الكهرومائية لتخزين المضخات يوفر كهرباء نظيفة أكثر ثباتًا، مما يعني أهمية كبيرة للبنية التحتية لشبكة الطاقة.

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

The abundance of fossil fuels and their negative environmental effects, together with the substantial reduction in their investment prices, have made solar-biomass hybrid plants an increasingly appealing choice for supplying the world’s energy needs. This study evaluates the performance of a PV/biomass hybrid renewable energy system (HRES) that incorporates three distinct biomass processes, including pyrolysis, direct combustion, and gasification. The hybrid system is modeled employing the multi-objective genetic algorithm (MOGA). The most excellent layout is tabbed based on factors such as the largest proportion of green energy and the least amount of noxious emissions, as well as the minimum cost of energy (COE) and net present cost (NPC). The COE in the pyrolysis system is 17% and 38% lower than in scenarios 1 and 2, respectively. The decrease in NPC and overall system cost, which demonstrates 17% and 65% drops in NPC and 15% and 37.5% decreases in total system cost, respectively, as compared to scenarios 1 and 2. After comparing all the essential aspects, it is revealed that the HRES incorporating biomass pyrolysis is preferable to the most cost-effective option for making hybrid systems than other HRESs executed up of gasifier or direct combustion biomass technologies. This idea would improve the use of biomass resources in HRES by including the foremost biomass power production technology, making it simpler for researchers to identify the paramount hybrid renewable energy systems and create decisive HRES using biomass as the main source.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.