Utilization Of Energy Sources Research Articles

Optimal control algorithms for Gabon’s Smart Grid: Enhancing efficiency and sustainability

The adoption of smart grid technologies offers Gabon several opportunities to enhance energy efficiency and sustainability. This study investigates the use of optimum control algorithms to increase grid stability and enhance the utilization of renewable energy sources. Mathematical models and simulations are used to assess genetic algorithms in light of Gabon's unique energy situation. Systems known as electrical smart grids supply electricity to users directly from power plants in an effort to reduce costs, cut down on blackouts, and improve energy efficiency. Smart grids, or SGs, are well-known pieces of equipment because of their amazing capabilities, which include bi-directional communication, stability, power failure detection, and interconnectivity with appliances for monitoring. Many modern data and security management systems, including modeling, monitoring, optimization, and/or artificial intelligence, lead to SGs. Energy was once cheap, easily managed, and dependent only on fundamental elements. Demand has significantly increased as a result of the current circumstances, which has also increased Gabon's electricity expenses, the likelihood of a contingency, and the complexity of the electrical network. In Gabon, smart grids have shown to be the most practical and clever way to lessen these problems in recent years. An electrical network with information technology integrated is called a smart grid. According to this study, using genetic algorithms in a smart grid could lower Gabon's overall electricity prices. In order to meet demand, the proposed approach makes use of off-grid battery banks and renewable energy sources. The goal of GA optimization is the short-term time averaged power cost; factors that affect this include grid energy to satisfy load, battery discharge, and other factors. MATLAB software is used to solve the optimization problem over a 24-hour period of renewable input, real-time energy pricing, and load. The results are presented.

Cylindrical Composite Structural Design for Underwater Compressed Air Energy Storage

Abstract The utilization of renewable energy sources is pivotal for future energy sustainability. However, the effective utilization of this energy in marine environments necessitates the implementation of energy storage systems to compensate for energy losses induced by intermittent power usage. Underwater compressed air energy storage(UWCAES) is a cost-effective and emission-free method for storing energy underwater. This technology has proven to be effective and viable, and it offers significant benefits in terms of energy efficiency and sustainability. In this paper, a cylindrical composite structure UWCAES tank is designed. At first, the materials and shapes of the different forms of air containers were evaluated, and the relationship between container diameter, length, and number of air containers was analyzed on the basis of the range of energy storage densities for different kinds of systems. Subsequently, the materials to be applied in the tanks were investigated and selected according to the actual working conditions of the system. Eventually, finite element models and prediction formula were developed and the influence of changes in the thicknesses of the steel reinforcement were discussed. The results demonstrated that the storage tank possesses adequate environmental resistance and load-bearing capacity, which can provide a reference for its practical engineering implementation.

Heating performance of PCM radiant floor coupled with horizontal ground source heat pump for single-family house in cold zones

As the energy demand for space heating increases, the necessity for innovative and energy-efficient heating technologies has become more urgent. This study proposes and evaluates a heating system that integrates a phase change material radiant floor with a horizontal ground source heat pump (PCM floor-HGSHP, Case 1), intending to maintain indoor comfort temperatures and enhance energy efficiency through the utilization of renewable energy sources. Despite the considerable potential of PCM floors, their combination with HGSHP requires further research to fully unlock their benefits. The study involved simulating the PCM floor using a validated TRNSYS component and developing an advanced rule-based control strategy with time-of-use tariffs as a key input to optimize system operation. To elucidate and quantify the advantages of the PCM floor-HGSHP system and verify its applicability in cold regions of China, a single-family house was used as a case study building, four representative cities were selected, and two comparative simulation cases were defined, along with three key performance indicators. The results indicate that, compared with the radiant floor heating system with HGSHP (RFHS-HGSHP, Case 2), the incorporation of PCM in the floor of Case 1 improves the indoor temperature stability by 8.6 %, enhances the load flexibility by 18.1 %, and reduces the total operating costs by 13.8 %. Furthermore, relative to the PCM floor with air-to-water heat pump system (PCM floor-AWHP, Case 3), the proposed system which substitutes the AWHP with the HGSHP, extends the duration of indoor comfort temperatures by 9.6 % and decreases the system electricity consumption by 33.7 %. The proposed system demonstrates excellent thermal and energy performance across all selected cities, proving to be both technically feasible and significantly beneficial for maintaining indoor comfort temperatures and reducing operational costs. This study offers valuable insights and technical support for designing and optimizing residential heating systems in cold regions, thereby promoting the development and application of renewable energy sources.

An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies

The utilization of conventional energy sources commonly leads to heightened energy consumption and the generation of specific forms of environmental pollution. As an innovative power management and dispatch system, virtual power plants (VPPs) have the potential to significantly enhance the flexibility and stability of power systems, while supporting carbon reduction targets by integrating distributed energy resources (DERs), energy management systems (EMSs), and energy storage systems (ESSs), which have attracted much attention in the power industry in recent years. Consequently, it can effectively address the variability and management challenges introduced by renewable energy. Furthermore, optimizing power market dispatch and user-side power management plays a pivotal role in promoting the transition of the energy industry towards sustainable development. The current study highlights the unresolved issue of strategic decision-making among market participants, such as energy companies, generation companies, and power distribution companies, despite the potentially significant benefits of VPPs. These entities must carefully evaluate the costs and benefits associated with adopting a VPP. Additionally, governments face the complex task of assessing the feasibility and effectiveness of providing subsidies to incentivize VPP adoption. Previous research has not adequately explored the long-term evolution of these decisions in a dynamic market environment, leading to a lack of adequate understanding of optimal strategies for market participants and regulators. This paper addresses this critical research gap by introducing an innovative bilateral evolutionary game model that integrates VPP and carbon trading markets. By utilizing the model, simulation experiments are carried out to compare different strategic decisions and analyze the stability and long-term evolution of these strategies. Research findings indicate that the adoption of VPP technology by market participants, in conjunction with government policies, results in an average 90% increase in market participants’ earnings, while government revenues see a 35% rise. This approach provides an alternative method for understanding the dynamic interactions between market participants and government policy, offering both theoretical and practical insights. The findings significantly contribute to the literature by proposing a robust framework for integrating VPPs into electricity markets, while offering valuable guidance to policymakers and market participants in developing effective strategies to support the sustainable energy transition. The application of this model has not only enhanced the understanding of market dynamics in theory, but also provided quantitative support for strategic decisions under different market conditions in practice.

An Analysis of the most Promising Strategies for Controlling the MPPT of Photovoltaic Systems

There has been a tremendous increase in the utilization of renewable energy sources, particularly, solar energy. In terms of potential energy sources, photovoltaics are among the most promising. This article provides a concise assessment of the prospective approaches of maximum power point tracking (MPPT) control for the photovoltaic system. To begin, a discussion is held regarding the fundamentals of the photovoltaic system, including the concept and fundamental theory behind the P&O MPPT control method, which is frequently utilized. After that, three of the most promising techniques for enhanced MPPT control are selected. There is also coverage of research that is meaningful on the fundamental theory and the primary concept of the three techniques. Several of the research findings from different approaches are compiled in this document for the purpose of comparison. The tracking time, the complexity of implementation, and the accuracy are the three main indexes that are examined in this final comparison of these three systems. The efficiency, accuracy, and applicability of these methodologies are indicated by these indexes, which can serve as a meaningful reference for future applications and additional research.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Green innovation imperative for natural resource-driven sustainable economic recovery: Linking rights Structure, corporate social responsibility, and renewable energy contracts

This study examines the complex relationships necessary for a sustainable economic recovery, focusing on the interplay between contracts for renewable energy, natural resource use, corporate social responsibility (CSR), and rights frameworks. Motivated by the increasing scrutiny of environmental practices, this research aims to highlight the need for sustainable business models during the transition to a more environmentally sensitive economy. The study area encompasses diverse sectors where CSR goals can be aligned with renewable energy project frameworks through natural resource utilization. Methodologies include a novel composite CSR evaluation indicator designed to complement industry rankings and a thorough analysis of CSR within the mining industry. Results demonstrate how aligning CSR with renewable energy initiatives can reshape profit models for stakeholders and emphasize the changing green product market as a catalyst for economic resurgence. Recommendations in the area of policies focus on the reasoned utilization of natural resources and the application of innovations following the principles of CSR. This research provides critical guidance to relevant authorities and institutions charged with ethical responsibility, ensuring the proper utilization and implementation of renewable energy sources to create a more ecological future based on green technology and sustainable resource management.

Research on Energy Management in Hydrogen–Electric Coupled Microgrids Based on Deep Reinforcement Learning

Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion, utilization, and storage technologies with distributed wind and photovoltaic power generation techniques, it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form of microgrids. However, the diverse operational mechanisms, varying capacities, and distinct forms of distributed energy sources within hydrogen-coupled microgrids complicate their operational conditions, making fine-tuned scheduling management and economic operation challenging. In response, this paper proposes an energy management method for hydrogen-coupled microgrids based on the deep deterministic policy gradient (DDPG). This method leverages predictive information on photovoltaic power generation, load power, and other factors to simulate energy management strategies for hydrogen-coupled microgrids using deep neural networks and obtains the optimal strategy through reinforcement learning, ultimately achieving optimized operation of hydrogen-coupled microgrids under complex conditions and uncertainties. The paper includes analysis using typical case studies and compares the optimization effects of the deep deterministic policy gradient and deep Q networks, validating the effectiveness and robustness of the proposed method.

The Effectiveness of the EU ETS Policy in Changing the Energy Mix in Selected European Countries

In the field of economic analysis, the study of the EU ETS policy has primarily focused on the impact of renewable energy consumption on economic growth, as well as the role of legal and fiscal instruments in the development of clean energy. This study aimed to evaluate the effectiveness of the EU ETS policy in altering the energy mix of selected European countries, providing both cognitive and applicational value. The evaluation of the effectiveness of this policy focused on the structure of the energy mix and the relationship between rising CO2 emission allowance prices and the decreasing share of coal in the energy mix. The goal was achieved through statistical analysis of secondary sources, primarily sourced from Bloomberg (2016–2024). The research findings indicated that changes in the structure of energy sources varied across the studied European countries, due to the adopted energy source utilization strategy, resource availability, and geopolitical situations. Additionally, different correlation values were noted between rising CO2 emission allowance prices and the expected reduction in fossil fuel use. Therefore, the EU ETS policy does not fulfill its assigned role—its implementation contributes to disparities in the economic situations of European economies and creates conditions for unequal competition.

Recent Progress on In Situ Catalytic Conversion Catalysts for Oil Shale.

With the gradual depletion of oil resources, it is urgent to vigorously develop unconventional and renewable energy sources. As an unconventional resource with abundant resources, the rational and efficient development of oil shale is of great significance to alleviating the national energy crisis. The main methods of oil shale development are surface dry distillation and in situ transformation. In situ catalytic conversion technology, as an emerging mining method, has the potential to improve efficiency, reduce costs, and environmental impact and is an important direction for future oil shale development. As a key component in the in situ conversion process, catalysts play a crucial role in the rate, selectivity, and product quality of the reaction. However, the research on oil shale pyrolysis catalysts is still in the laboratory stage and is difficult to translate into practical applications. Therefore, this paper reviews recent progress of catalyst research in the in situ catalytic conversion of oil shale, including the types, design principles, and reaction mechanisms of catalysts, and looks forward to the future development direction. It provides a certain reference for the discovery of new catalysts that can improve yield and reduce environmental pollution in the process of oil shale extraction and also provides a valuable technical reference for the green development and utilization of unconventional and strategic alternative energy sources in the world and the transformation and sustainable development of the global energy structure.

Optimization of multi-vehicle charging and discharging efficiency under time constraints based on reinforcement learning

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Simultaneous desulfurization and dearomatization of simulated straight-run diesel with novel green DBN-based ionic liquids

Given the surplus of diesel fuel in China and the extensive utilization of renewable energy sources, there is a growing inclination to eliminate organic sulphur and aromatic hydrocarbons from diesel fuel for its application as high-quality ethylene cracking feedstock. In this study, a series of 1,5-diazabicyclo[4.3.0]non-5-ene (DBN)-based ionic liquids with [DBNH]+ as the cation and imidazole as the anion were designed and synthesized for the simultaneous desulfurization and dearomatization of straight-run diesel fuel. Considering factors such as extractant stability, residue content in oil, and separation performance, [DBNH][Tr], exhibiting excellent performance characteristics, was screened as the optimal extractant. Following three-stage cross-flow extraction with an extractant/oil ratio of 3:1 at 30 °C, complete removals of sulfides (benzothiophene) and polycyclic aromatic hydrocarbons (1-methylnaphthalene) were achieved while saturated alkanes reached up to 95.8 wt%, making it suitable for ethylene cracking applications. The interaction mechanisms between ionic liquids and oil components were revealed by atoms in molecules (AIM) topology analysis, independent gradient model based on Hirshfeld partition (IGMH) analysis, and symmetric adaptive perturbation theory energy decomposition techniques, revealing that dispersion-dominated van der Waals (VDW) forces primarily govern interactions due to π-π stacking.

Techno-econo-environmental analysis of sustainable hybrid solar-wind-biogas using municipal solid waste-based grid independent power plant with dual mode energy storage strategy

In the contemporary landscape characterized by escalating energy issues and growing worldwide environmental apprehensions, the transition towards sustainable energy assumes a critical role in enhancing the standard of living. Hybrid renewable energy systems (HRES) are increasingly seen as crucial, particularly in off-grid communities, due to their provision of dependable and sustainable electrical alternatives to conventional fossil fuels. This study evaluates the viability of an autonomous system incorporating dual-mode energy storage, as well as explores the possibilities of converting municipal solid waste (MSW) into biogas to achieve both energy and environmental advantages. This research employs NSGA-II in MATLAB environment, optimizing novel HRES for size and performance where the fundamental goals include cost-effectiveness, health hazard mitigation, and ensuring a reliable electrical supply. The findings indicate that the system under investigation exhibits an ideal energy cost of 0.1393$/kWh, carbon emissions amounting to 184283.1 kg CO2-eq/yr, a health impact of 2.56 × 10−1 DALYs, and an ecological damage footprint of 1.45 × 10−7 species⋅year. The system exhibits a job creation rate of 4.22 jobs per megawatt and attains a human development score of 0.404. Through the utilization of renewable energy sources and advanced storage technologies, it is possible to address the challenges posed by energy and environmental problems.

Exploring the Development of Photoresists in Lacquer under the Background of Global Carbon Neutrality: A Mini Review

With the establishment of a global carbon-neutral target, green and sustainable design concepts, materials, and processes have become the key to scientific and technological innovation, which highlights the development and utilization of environmentally-friendly materials and renewable energy sources. At present, almost all the raw materials in commercial photoresist are from oil resources. In order to implement the policy of “green chemistry”, there is an urgent need to take the place of petroleum with renewable, biodegradable, and green biomass resources in photoresist production. As a kind of green and natural high polymer material from China, lacquer has excellent physical and chemical properties and has great application potential in the field of electronics. In this paper, the potential connections between photoresist and lacquer, especially in the green development of photoresist, are sorted out. The introduction of lacquer and its derivatives may help to improve the environmentally-friendly production of photoresist and its performance. Therefore, in the context of global carbon neutrality, the study of photoresists in the “lacquer” is of great significance in promoting the application of eco-friendly materials in the field of microelectronics.

Bioconvective three-dimensional flow of Sutterby nanoliquid due to moving plate with activation energy applications

Owing to enhanced features of nanomaterials, various applications of such materials have been suggested in the thermal systems, heat exchanges, electronic cooling systems, coolant processes, energy production etc. Following to such motivated applications, the objective of current analysis is to presents the bioconvective double stratification flow of Sutterby nanofluid due to bidirectional moving surface. The thermal interpretation of problem is subject to utilization of radiative effects, activation energy and heat source. The observations for heat, mass and microorganism's assessment are performed by using the convective boundary conditions. Shooting numerical simulations are performed for modeled problem. It is noticed that heat transfer enhances due to thermal stratification parameter. An increment in mass transfer is subject to larger values of mass stratification Biot number. The claimed results offer significance in controlling the heating and cooling processes, thermal devices, energy generation, manufacturing developments, solar systems etc.

Transient response of a megawatt-scale solar photovoltaic in an electric distribution utility

There is an increasing trend among customers of an electrical distribution utility to adopt grid-tied solar photovoltaic systems. This shift offers multiple benefits to consumers, including lower monthly electricity bills and a contribution to the development of green energy. For the electrical distribution utility, various impacts may arise due to varying levels of solar energy penetration. This study investigates the effects of integrating varying levels of solar photovoltaic penetration into the commercial consumer network of Cagayan de Oro Electric Power and Light Company (CEPALCO) in the Philippines. Utilizing PowerWorld simulator, the research evaluates 11 different scenarios with solar penetration levels adjusted according to the percentage of load demand. Key findings include alterations in solar megavolt ampere of reactive power output, bus voltage levels, transformer power loading, and transmission line ampacity, with frequency levels remaining stable across scenarios. The optimal solar penetration level was identified at 70%, balancing the benefits of solar energy integration with the need to maintain grid stability and operational limits. This optimal level ensures the effective utilization of renewable energy sources without compromising the performance of CEPALCO’s electrical infrastructure. The research concludes with recommendations for maintaining grid stability and operational limits at the optimal solar penetration limits.

Energy management algorithm development for smart car parks including charging stations, storage, and renewable energy sources

In this study, a photovoltaic system and stationary energy storage unit integrated vehicle charging station energy management algorithm were developed using a long-short term based prediction model (LSTM). The aim of the proposed system is to develop intelligent car parks with controlled charging stations aggregated by centralized charging stations instead of individual or dispersed uncontrolled charging stations to eliminate the imbalance in the demands of charging. The system has four energy sources: grid, vehicle batteries, PV system, and the stationary battery group. By calculating the power demand for vehicles in the car park, a dynamic energy management algorithm has been developed that provides efficient use of energy resources by considering the power demand density. Moreover, the forecasting model has been created by LSTM) not only to adjust charging timing and effectively use energy sources. The model was performed by forecasting not to take energy from the grid at critical times (overloaded times). The grid load is analyzed by the energy management algorithm and run for different times of the year, creating a load profile for 16 vehicles. The results of this study show that energy demand during the critical time interval can be reduced by 8–20 % solely through load shifting before and after the critical time interval without any additional resource support. Moreover, while the integration of only the PV system supports the grid by 15–20 %, the optimal utilization of other energy sources during the relevant time frame supports the grid by 65–75 %. In addition, by collecting charging stations at a central point, energy storage capacity is increased and effective energy management is achieved. In conclusion, it is proposed that the infrastructure of such a parking system should be set up before the use of electric vehicles increases significantly.

Operation strategy and capacity configuration of digital renewable energy power station with energy storage

As the utilization of renewable energy sources continues to expand, energy storage systems assume a crucial role in enabling the effective integration and utilization of renewable energy. This underscores their fundamental significance in mitigating the inherent intermittency and variability associated with renewable energy sources. This study focuses on the involvement of photovoltaic (PV) plants in medium and long-term transactions. It also explores the participation of battery energy storage system (BESS) in electricity trading and frequency regulation ancillary services. The objective is to establish a strategic research model for maximizing the benefits of PV plant and the BESS in the energy arbitrage and frequency regulation markets. Sensitivity analysis was conducted to assess the impact of variations in both the rated power and maximum continuous energy storage duration of the BESS. Base on the NSGA-II algorithm and TOPSIS algorithm, an optimization model for energy storage capacity configuration is developed. The optimal capacity configuration and maximum continuous energy storage duration are determined through computational analysis, yielding values of 30.8 MW and 4.521 h, respectively. At this configuration, the daily average revenue is 2.362 × 105 yuan, the initial investment cost is 1.45 × 109 yuan, and the payback period is 4.562 years.

Green Hydrogen from Hydroelectric Energy: Assessing the Potential at the Abanico Power Plant in Morona Santiago Province

A key strategy for optimizing the utilization of clean energy sources involves integrating energy vectors to effectively manage surplus power generated from renewable sources. This study specifically delves into the assessment of a hydrogen (H₂) generation project utilizing energy generated by the Hidroabanico power plant. Over a span of 13 years, the average monthly energy production was meticulously scrutinized, with a determination that 20% of this energy would be allocated for H₂ production. The operational framework of the prototype plant, which relies on Proton Exchange Membrane (PEM) electrolyser technology, is comprehensively elucidated. This system boasts an annual energy capacity capable of reaching 62,439 MWh (20% of total production), culminating in the production of 1,123 Tn of H₂. However, it is noteworthy that this quantity remains relatively limited compared to analyses conducted utilizing alternative technologies.