Energy Demand Research Articles

Ultrasonographic Evaluation of Effects of Pregnancy on Thyroid Volume in a Nigerian Cohort

Background: During pregnancy, the thyroid gland experiences physiological changes critical for metabolism regulation. Ultrasonography provides a non-invasive means to assess these changes effectively. Thyroid gland responds physiologically to increased demands for iodine and energy which is the case in pregnant women. Aim: This study aims to evaluate the physiological and pathological alterations in the thyroid gland of pregnant women across gestational ages (0-40 weeks) at a specialist hospital in Port Harcourt, Nigeria. The study is also aimed at giving normative values of thyroid volume in pregnancy which helps in ascertaining when a pregnant woman develops goiter as well as properly monitor thyroid hormones. Methods: A prospective study involving 559 healthy pregnant women was conducted from September 2023 to May 2024. Data collected included age, BMI, weight, and height. Thyroid volume was assessed via ultrasound. Statistical analysis was performed using SPSS version 21.0 with a significance level set at p < 0.05. Results: The mean maternal age was 29.95 ± 6.34 years. Significant positive correlations were observed between thyroid volume in the right lobe and variables such as age, BMI, weight, and height. The left lobe volume showed a positive correlation with parity. Conclusion: The study confirms thyroid volume increases during pregnancy, with significant correlations to specific demographic and anthropometric factors. These findings underscore the importance of routine ultrasonographic evaluation of the thyroid in pregnant women for early detection and management of potential disorders.

Solar Powered Electric Vehicle Charging Station With Integrated Battery Storage System

ABSTRACTThe shift towards electrical vehicles (EVs) can be an important alternative to internal combustion engines for sustainable energy solutions. However, increased EV adoption will increase the charging demand, and there will be a load on the grid electricity. Integrating solar photovoltaic systems with EV charging infrastructure will not only support environmental goals, but also ensure a more resilient and self‐sufficient energy system. A standalone PV system is a good option to reduce the stress on the grid for charging EVs. This present work pivots on the design and performance assessment of a solar photovoltaic system customized for an electric vehicle charging station in Bangalore, India. For this purpose, we have used the PVsyst software to design and optimize a standalone PV system with battery energy storage for EV charging stations. The result shows that 51.1 kWp PV system will be sufficient to meet the energy demand of the charging station by producing 98 313 kWh array energy. The proposed system showed a good average performance ratio of 68.90%. This study shows that the integration of standalone solar photovoltaic systems with EV charging stations is crucial in India and other countries to alleviate grid stress and promote sustainable energy use. This approach not only supports the transition to cleaner transportation but also enhances energy security and reduces dependency on fossil fuels.

Urine luck: Environmental assessment of yellow water management in buildings for urban agriculture

The increasing global demand for agricultural production poses challenges to maintain the needs for critical fertilizers such as nitrogen. This study explores the potential of human urine as a source of renewable nitrogen for fertilizer production. Through a life cycle assessment, three different urine management strategies were compared: (S1) an artificial wetland, (S2) an on-site lab-scale aerobic reactor for nitrogen recovery, and (S3) a centralized wastewater treatment plant. While scenario S2 had the highest impacts in 6 out of 8 categories, an advantage in marine eutrophication was identified. S2 showed high energy demand (750 kg MJ-eq) and ecotoxicity (602 kg 1.4-DCB-eq.) mainly due to energy requirements. Nitrogen production exceeded 2.3 times the yearly nitrogen demands of the building tomato production. Upscaling S2 reduces impacts up to 2 times, lowering the payback time from 29 to 13 years. Therefore, implementing large-scale nitrogen recovery systems in cities is encouraged.

Developing an energy storage systems selection methodology for peak shaving purposes in photovoltaic micro-installations

This study investigates the selection methodology for peak shaving purposes in photovoltaic micro-installations. Due to the increasing share of photovoltaic micro-installations in the national electricity production and the consequent changes in the country's energy policy, the need for energy storage is becoming increasingly apparent. Therefore, based on data collected throughout the study of a photovoltaic installation conducted in years 2020-2024 and data on market electricity prices, the correlation between production, market electricity prices, and energy value was analyzed. The analysis established interconnections between these values and identified factors influencing their changes. It was observed that the market price of electricity is relatively low during hours of increased photovoltaic energy production. By comparing the correlations between energy production and electricity prices over different years, an increasing impact of the increasing share of photovoltaic energy production in national electricity production on the market price of electricity was noted, highlighting the importance of energy storage. For the described household, an energy storage system was selected with a view to meeting the energy demand during hours of higher market electricity prices. The selected model was Lithium-Iron-Phosphate battery with a capacity of 5 kWh. Furthermore, the cost effectiveness of household-scale energy storage systems was examined. Implementing the proposed solutions could result in increased auto-consumption and presumably improve cost-effectiveness of photovoltaic installations in the future.

Parenteral lipid nutrition in congenital gastrointestinal malformation operated cases

Background: Congenital gastrointestinal malformation (CGIM) cases require immediate surgical correction after birth and proper nutrition to meet energy demands. Among the macronutrients, lipids are considered the major energy source, playing a vital role in parenteral nutrition. However, the time of their initiation remains unclear. We compared the data of CGIM cases where lipids were given from the first post-surgical day to those where parenteral lipids were initiated on the second or third day of surgery and then reevaluated them on the seventh day of parenteral nutrition.Methods: We collected the data of patients who had undergone surgical correction for CGIM and were kept on parenteral nutrition including lipids from day 1 of surgery labeling them as group 1. Group 2 comprised of neonates having lipid parenteral nutrition started from day 2 or 3 of surgery. Both groups were reevaluated on the 7th day of parenteral nutrition. Statistical analysis was performed.Results: Group 1 had higher total bilirubin (TB) statistically significant (p-value=0.0299), as well as lower lipase (p-value=0.0286), and lower total lipids (p-value=0.0365). The values were lower of triglycerides (p-value=0.0365), and total lipids (p-value=0.0365), on day 1. Comparing both groups, all biochemical parameters were found to be statistically significant.Conclusion: Lipid deficiency and overload should be carefully evaluated, especially in neonates having undergone recent surgical interventions, which increase their nutritional demands. No adverse effects were seen in our study related to parenteral lipid emulsion administration in post-surgical congenital gastrointestinal malformation cases. Early initiation of parenteral lipid regimens can provide better outcomes.Keywords: Congenital gastrointestinal malformations; Intravenous lipid emulsions; Parenteral nutrition; Gestational age; Milk formula

Process Simulation and Technical Evaluation Using Water-Energy-Product (WEP) Analysis of an Extractive-Based Biorefinery of Creole-Antillean Avocado Produced in the Montes De María

The annual increase in the world’s population significantly contributes to recent climate change and variability. Therefore, researchers, engineers, and professionals in all fields must integrate sustainability criteria into their decision-making. These criteria aim to minimize the environmental, social, economic, and energy impacts of human activities and industrial processes, helping mitigate climate change. This research focuses on developing scalable technology for the comprehensive use of avocados, adhering to sustainability principles. This work presents the modeling, simulation, and the WEP (Water-Energy-Product) technical evaluation of the process for obtaining bio-oil, chlorophyll, and biopesticide from the Creole-Antillean avocado. For this, the extractive-based biorefinery data related to water, energy, and products are taken from the material balance based on experimental results and process simulation. Then, eight process parameters are calculated, and eleven technical indicators are determined. Later, the extreme technical limitations for every indicator are demarcated, and an evaluation of the performance of the indicators is carried out. Results showed that the process has a high execution in aspects such as fractional water cost (TCF) and energy cost (TCE), as well as solvent reuse during extraction processes (SRI) and production yield, noting that the mentioned indicators are above 80%. In contrast, the metrics related to water management (FWC) and specific energy (ESI) showed the lowest performance. These discoveries support the use of optimization techniques like mass process integration. The energy-related indicators reveal that the process presents both benefits and drawbacks. One of the drawbacks is the energy source due to the high demand for electrical energy in the process, compared to natural gas. The specific energy intensity indicator (ESI) showed an intermediate performance (74%), indicating that the process consumes high energy. This indicator enables us to highlight that we can find energy aspects that require further study; for this reason, it is suitable to say that there is potential to enhance the energy efficiency of the process by applying energy integration methods.

Optimization of Renewable Energy Company Stock Portfolio for Investment Decision Making using the Markowitz Model

This study focuses on optimizing the renewable energy company's stock portfolio using the Markowitz model, which aims to balance risk and return for proper investment decision making. With the increasing demand for clean energy, portfolio optimization in the renewable energy sector is important for investors. This research takes into account historical stock performance and applies the Mean-Variance Optimization framework to minimize risk while maximizing return. This portfolio consists of selected renewable energy companies, and the analysis runs from September 2021 to August 2024. This study aims to analyze the allocation of investment portfolios in renewable energy company stocks in Indonesia. Based on the analysis results, the investment portfolio is allocated to five main stocks, namely BUMI.JK with an investment value of IDR 17,075,844 (17.08%), INDY.JK of IDR 5,825,852 (5.83%), KEEN.JK of IDR 33,766,798 (33.77%), RAJA.JK of IDR 43,084,876 (43.08%), and WIKA.JK of IDR 246,630 (0.25%). These results indicate that most of the funds are invested in RAJA.JK and KEEN.JK stocks, which contribute more than 75% of the total investment portfolio

High‐Entropy Perovskite Oxides for Thermochemical Solar Fuel Production

The increasing global demand for energy, coupled with the need to mitigate climate change, has spurred significant interest in renewable energy sources. Among these, solar energy holds particular promise due to its abundance and potential to be converted into clean fuels through thermochemical cycles. High‐entropy perovskite oxides (HEPOs) have emerged as promising materials for solar thermochemical hydrogen (STCH) production, offering advantages over traditional materials like ceria due to their enhanced thermal stability, flexibility in composition, and lower operating temperatures. Herein, the advantages of HEPOs, including their stability under extreme thermal conditions which is critical for repeated redox cycling in H2 production, are highlighted. The inherent configurational entropy allows for a broader range of element incorporation, leading to improved tunability of physical properties. However, challenges remain, particularly in terms of cost and scalability. To address this, strategies such as the use of more abundant elements and optimized synthesis are discussed. Additionally, the future potential of HEPOs, including their integration into advanced solar reactors, is explored, and how computational methods can be employed to predict new high‐entropy compositions with improved performance is examined. The development of HEPOs for STCH offers a promising pathway toward sustainable hydrogen production, addressing both environmental and economic challenges.

Optimal power generation of proton exchange membrane fuel cell using ANFIS based MPPT algorithm

Fuel cells are the most promising energy source for the future energy demand. The automobile industry is looking at the integration of fuel cells with electric vehicles (EV). This integration comes with many challenges like dynamic operational behaviors. For operating the fuel cell with maximum efficiency, this work proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method. The hydrogen flow rate, pressure and stack temperature are the parameters considered to track the maximum power point of the fuel cell. The ANFIS-MPPT algorithm has been integrated with the 1.26 kW fuel cell in MATLAB/Simulink® and validated in different scenarios like dynamic variation in hydrogen pressure, stack temperature, load variation. The performance has been observed and compared with the conventional MPPT algorithms of Perturb and Observe (P&O) algorithm and Incremental Conductance (InC) algorithm. The proposed ANFIS-MPPT algorithm improves the power stability by 10–15% than the P&O and InC methods. Also, the proposed ANFIS-MPPT has 30% faster response as compared to the P&O algorithm, and 23% than the InC algorithm. From the analysis, it is observed that the ANFIS, P&O and InC methods are having the response time of 2.5 s, 3.6 s and 4.5 s respectively. Also the ANFIS method delivers the maximum power output of 1.26 kW, whereas the P&O and InC deliver 1.13 kW, 1.19 kW respectively. The detailed simulation analysis and results are presented in this paper.

Implications of accelerated and delayed climate action for Ireland’s energy transition under carbon budgets

Limiting global warming requires the effective implementation of energy mitigation measures by individual countries. However, the consequences of the timing of these efforts on the technical feasibility of adhering to cumulative carbon budgets—which determines future global warming—are underexplored. Moreover, existing national studies on carbon budgets either overlook integrated sectoral interactions, path dependencies, or comprehensive demand-side strategies. To address this, we analyse Ireland’s mitigation pathways under equal per-capita carbon budgets using an energy systems optimisation model. Our findings reveal that delayed mitigation brings forward the need for a net-zero target by five years, risks carbon lock-in and stranded assets, increase reliance on carbon dioxide removal technologies and leads to higher long-term mitigation costs. To keep the Paris Agreement targets, countries must set and meet accelerated mid-term mitigation goals and address energy demand.

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

Mathematical modeling of Ethiopia's energy demand by sectors and energy types, with forecasts for the next 30 years

Due to the scarcity of economic resources, it is vital to optimize everything so that the supply and demand lines intersect in an optimized quantity, with no shortage or surplus of a provided item or service. Energy supply contains both surplus and shortage, thus estimating the amount of projected energy demand is a key work that must be completed. The objective of this paper is generating a mathematical model based on the actual data for thirty years forecasting. To create a mathematical model using actual data from the last fifteen years, a model that can represent the past trend and be used for future forecasting. This study provides a general overview of Ethiopia's current energy requirement with different energy type as well as sector-specific energy demand and estimates for different economic growth scenarios up to 2052. This model was created using a linear regression polynomial fit through Origin graphic and analysis software, and an econometric model was also applied. GDP was utilized as an independent variable in the economic model to determine the trend of energy consumption. Another input-output model is also used for multilinear regression to evaluate the change of four variables, GDP, population growth, urbanization growth rate, and general inflation rate, which was quantitatively linked with total energy requirement using Weka software. The mathematical model developed through linear and multilinear regression has been validated by using a different assumption on GDP growth based on past growth rate as low, medium and high growth rate and using the mathematical formula to generate an energy demand trend that can be compared with the actual trend; as a result, all the mathematical model that are generated has been found to be valid for the purpose of the intended work. Based on the generated mathematical model and different GDP growth rate scenario as low, medium, business as usual (BAU) and high a future energy demand was forecasted up to 2052 for thirty years. The model's results can help energy planners ensure that the country's supply capacity keeps up with predicted energy demand growth.

Decomposition analysis of renewable energy demand and coupling effect between renewable energy and energy demand: evidence from China

Driving up demand for renewable energy is key to China's response to the energy crisis and to controlling carbon emissions. Several studies have been conducted to explore the factors affecting renewable energy demand. However, the literature on the importance of technological innovation in fossil energy production in affecting renewable energy demand is scant. Therefore, it is essential to explore the impact of technological innovations in fossil energy production on renewable energy demand, as well as to reveal the dynamic penetration mechanism of renewable energy in the field of energy demand, to provide policy guidelines for China to promote its energy revolution. This study decomposes renewable energy demand into 10 drivers with the extended IDA-PDA (Index Decomposition Analysis and Production-theory Decomposition Analysis) model. Then the dynamic penetration mechanism of renewable energy on the energy demand side is revealed by combining the coupled model and the decomposition results. The results show that the investment intensity effects and energy restructuring contributed the most to renewable energy demand growth, contributing 60.73% and 79.97% respectively. Conversely, technological advances in fossil energy combustion made the largest negative cumulative contribution to renewable energy demand growth, with a cumulative negative contribution of -39.57%. The drivers of renewable energy demand vary considerably across types, regions and resource endowment levels, which means that regions should develop differentiated development strategies based on their own characteristics. In addition, the coupling effect between renewable energy demand and total energy demand is weakening, with market and policy effects becoming the drivers for augmenting the response of renewable energy to total energy demand. The findings of this study provide clear pathways for expanding renewable energy demand, offering important insights for the government to balance between promoting capital-driven interests and policy-driven development.

Optimal Configuration for Shared Electric-hydrogen Energy Storage for Multiple Integrated Energy Systems With Mobile Hydrogen Transportation

The flexible operation and storage of hydrogen and electric energy provide an effective path for the development of low-carbon energy and transportation systems. This paper introduces a configuration method for electric-hydrogen shared energy storage supporting the multiple energy and capacity demands of integrated energy systems (IESs). A Stackelberg game-based shared energy framework with gaseous hydrogen transportation by transportation network for shared energy storage operators (SESO) and IESs is established. In this framework, the electric power and electric storage sharing are accomplished by power lines, while the shared hydrogen is achieved by the transportation of mobile trucks. The target of the energy storage operator is to maximize its trading benefits with IESs and lower the life-cycle cost of the whole system, while the IESs aim to meet their energy demand and lower the operation cost via energy storage sharing. To solve this planning model, a two-level sine cosine based grey wolf optimizer (GWO-SCA)-bisectional method is provided to realize the system configuration, mobile hydrogen planning, and economic trading of the shared energy operator. A case study with 3 IESs, real-world geographic roads, and environmental conditions is carried out to verify the effectiveness of the method and the life-cycle configuration and operation economy of the shared energy storage. The comparisons show that the proposed energy storage sharing frame can achieve a higher energy utilization ratio of 92% and the proposed method can solve the two-level problem more efficiently, the calculation time is reduced by 80.2%.

The role of agriculture for achieving renewable energy-centered sustainable development objectives in rural Africa

Multi-dimensional and overlapping barriers to wellbeing severely affect many areas in rural sub-Saharan Africa. In the region, more than 90% of cropland is rainfed, less than one third of households have electricity, almost 60% of the population reports food insecurity, and more than 35% of the population lives below the international poverty line. Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of these challenges, slowing and potentially reversing development. Thus, there is a strong need for multi-sector interventions across multiple levels, from national policies, to regional and river catchment-scale planning, to local planning and investment. To implement such actions, it is key not only to assess technological solutions and their investment needs, but also to appraise their feasibility and implementation potential (from both a policy and a financial point of view). Here, we implement a modelling platform (RE4AFAGRI platform), which soft-links bottom-up process-based water and energy demand and techno-economic infrastructure assessment models (WaterCROP, M-LED, OnSSET) into a multi-node, national Nexus-extended Integrated Assessment Model (MESSAGEix-Nexus) for supply and investment assessment. The results of our analysis shed light on the role of water and energy demand in the agricultural sector for jointly affecting infrastructure and investment requirements for achieving rural sustainable development objectives. We find that scenarios with increased ambition in expanding irrigation and agricultural productivity result in improved diffusion and economic feasibility of infrastructure to provide universal energy access while supporting productive uses of energy. Moreover, we conduct business model analysis to appraise the framework conditions and micro and macro determinants that can ensure feasibility of investment and uptake of small-scale infrastructure, crucial for rural development. Altogether, our research demonstrates how integrated modelling with an explicit focus on Nexus interlinkages can represent the enabling role and the business conditions for renewable energy input in agriculture to become a leverage of rural sustainable development. In turn, important policy and investment-relevant insights can be derived.

How does structured adsorbent channel heterogeneity influence the efficiency of adsorptive CO2 capture?

Adsorbent monoliths are increasingly studied for carbon capture applications. These monoliths often contain a distribution of channels, due to defects in the die or inhomogeneous drying. The effect of such heterogeneities on CO2 capture process performance is not well investigated. In this work, the performance of a fixed bed, an ideal monolith and monoliths containing a wall size distribution are compared in a vacuum swing adsorption (VSA) cycle using a modelling approach. Using a monolith allows for a higher productivity (0.78 mmol/kgs) compared to a fixed bed (0.68 mmol/kgs) and a lower energy demand (190 kWh /tonne CO2 versus 320 kWh/tonne CO2). When the monolith contains a distribution of wall sizes, the process performance decreases. The recovery drops from 81 % to 68 %, the throughput drops from 0.78 mmol/kgs to 0.65 mmol/kgs and the energy demand increases from 190 kWh/tonne CO2 to 251 kWh/tonne CO2 for the widest distribution. Defects can severely impact performance.

Modeling the co-adoption dynamics of PV and heat pumps in Swiss residential buildings: Implications for policy and sustainability goals

The Swiss energy system is facing a paradigm shift as it strives to achieve net-zero greenhouse gas emissions, as well as phase out nuclear electricity production. This entails significant changes in the country’s energy landscape, including increased adoption of renewable energy technologies in the residential sector. To facilitate informed decision-making and planning by policymakers, this study introduces a system-dynamics model for the long-term adoption of solar photovoltaic (PV) and heat pump (HP) technologies in Swiss residential buildings. Unlike conventional approaches, this framework considers the feedback loops and correlations between PV and HP adoption, while also accounting for building heterogeneity. Through scenario analyses, the model evaluates the impact of regulatory and financial policy interventions on the transition of the residential energy system. The results highlight the significant influence of policy measures on technology deployment rates, energy demand, and greenhouse gas emissions. They demonstrate that slight adjustments in current policy and regulatory framework could allow to safely reach PV deployment targets, but strong modifications are necessary to completely decarbonize the residential sector. This study contributes to advancing our understanding of the complex dynamics shaping residential energy transitions and offers valuable insights to support the formulation and implementation of effective energy strategies.

Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state.

Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.

Comprehensive energy footprint of electrified fleets: School bus fleet case study

This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel under the average price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.

Technical evaluation and life-cycle assessment of solid oxide co-electrolysis integration in biomass-to-liquid processes for sustainable aviation fuel production

Building upon prior research, this paper integrates solid oxide electrolysis (SOEL) into a Biomass-to-Liquid (BtL) process, facilitating the production of sustainable aviation fuel (SAF) through gasification, co-electrolysis, and Fischer-Tropsch synthesis. Two different integration concepts are developed: An inline integration resulting in a directly electrified-Biomass-to-Liquid (eBtL) process and a parallel integration for a Power-and-Biomass-to-Liquid (PBtL) process. To maximize process efficiency, the SOEL is operated under endothermic conditions with heat supply from syngas cooling after gasification. The developed processes allow for an increase in carbon efficiency up to about 61% to 94%. The electrolysis power required corresponds to electrification ratios of 0.32 to 0.83MWel/MWth. Compared to the conventional H2 addition in PBtL processes, electricity demand can be reduced by 13% to 29% when using co-electrolysis instead of water electrolysis. The resulting increase in energy yield and energy efficiency is because up to 17% of the SOEL’s energy demand can be substituted with heat in the electrified BtL processes. A life cycle analysis shows the absolute requirement to operate the process with renewable electricity to reduce indirect greenhouse gas emissions. A critical evaluation of the technological feasibility indicates further development requirements for the SOEL and the gasification heat recovery technology to enable the integration approach.