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Energy crops on marginal lands are seen as an interesting option to increase biomass contribution to the primary energy mix. However, in the literature there is currently a lack of integrated assessments of margin land availability, energy crop production potential and supply chain optimisation. Assessing the potential and the cost of these resources in a given region is therefore a difficult task. This work also emphasises the importance on a clear definition and discussion about marginal lands and the related ethical issues embedded in the concept to ensure positive societal impacts of the results. This study proposes a methodology to estimate and analyse, in terms of economic costs, the potential of miscanthus grown on marginal lands from the production to the final point of use. Different datasets are assembled and a supply chain optimisation model is developed to minimize the total cost of the system. Miscanthus is used as a representative energy crop for the Belgian and French case studies. High temperature heat demand is considered as final use. The miscanthus can be traded by truck either in the form of chips or pellets. The results show that the miscanthus on marginal lands could supply high temperature heat up to 38 TWh in France and 1,4 TWh in Belgium with an average cost of around 50 €/t. The different sensitivity analyses showed that the yield variation has the strongest influence on the final cost, together with the distances and the cost of production of miscanthus. The main pattern observed is the local consumption of miscanthus chips and export of the surplus (if any) to the neighbouring regions. Pellets are only of marginal interest for France and are never observed for Belgium. Distances and availability of sufficient feedstocks are the two main parameters impacting the production of pellets.

High-fidelity thermodynamic simulation software is available to perform detailed simulations of power plants. However, these models depend on many operating parameters the user must characterize to assess the power plant performances. Unfortunately, most parameters are underdetermined by experience: the validation of the model using field measurements does not allow for the complete determination of all parameters, notwithstanding the unavoidable uncertainties of the measurements themselves. These limitations can result in a drastic mismatch between simulated and actual performance and lead to biased, suboptimal decision-making. To address these limitations, we performed Uncertainty Quantification on key furnace heat transfer parameters to predict the thermodynamic performance of coal-fired power plants retrofitted to biomass (co-)firing under uncertain operating conditions. We used a high-fidelity Thermoflex® model to simulate the thermodynamic performance of the power plant, and we adopted Polynomial Chaos Expansion to perform Uncertainty Quantification in a computationally-efficient manner. Finally, we evaluated the effect of various fractions of biomass in the fuel (from 0 to 100%) on the performance, which provides additional information in the decision-making process during the retrofit of the power plant. The results illustrate that the uncertainty on the non-uniform radiant flux factor dominates the uncertainty on the power, efficiency and flue gas temperature, meaning that efforts should aim at reducing the epistemic uncertainty on the radiative heat flux in the boiler. Increasing the biomass fraction results in a decrease in the gross power and gross efficiency. The mean Furnace Exit Gas Temperature remains relatively stable, but reaches a minimum value at 60% biomass co-firing. In conclusion, Polynomial Chaos Expansion allows for a computationally-efficient probabilistic assessment of non-validated operational conditions, such as a fuel switch, in high-fi

Renewable energy is increasingly mobilizing more investment around the globe. However, there has been little attention to evaluating economic and regulatory (E&R) uncertainties, despite their enormous impact on the project cashflows. Consequently, this review analyzes, classifies, and discusses 130 articles dealing with the design of renewable energy projects under E&R uncertainties. After performing a survey and identifying the selected manuscripts, and the few previous reviews on the matter, the following innovative categorization is designed: sources of uncertainty, uncertainty characterization methods, problem formulations, solution methods, and regulatory frameworks. The classification reveals that electricity price is the most considered source of uncertainty, often alone, despite the existence of six other equally influential groups of E&R uncertainties. In addition, real options and optimization arise as the two main approaches researchers use to solve problems in energy system design. Subsequently, the following aspects of interest are discussed in depth: how modeling can be improved, which are the most influential variables, and potential lines of research. Conclusions show the necessity of modeling E&R uncertainties with currently underrepresented methods, suggest several policy recommendations, and encourage the integration of prevailing approaches.

The assessment of the future thermodynamics performance of a retrofitted heat and power production unit is prone to many uncertainties due to the large number of parameters involved in the modeling of all its components. To carry out uncertainty quantification analysis, alternatives to the traditional Monte Carlo method must be used due to the large stochastic dimension of the problem. In this paper, sparse polynomial chaos expansion (SPCE) is applied to the retrofit of a large coal-fired power plant into a biomass-fired combined heat and power unit to quantify the main drivers and the overall uncertainty on the plant’s performance. The thermodynamic model encompasses over 180 components and 1500 parameters. A methodology combining the use of SPCE and expert judgment is proposed to narrow down the sources of uncertainty and deliver reliable probability distributions for the main key performance indicators (KPIs). The impact of the uncertainties on each input parameter vary with the considered KPI and its assessment through the computation of Sobol’ indices. For both coal and biomass operations, the most impactful input parameters are the composition of the fuel and its heating value. The uncertainty on the performance and steam quality parameters is not much affected by the retrofit. Key furnace parameters exhibit a skewed probability distribution with large uncertainties, which is a strong attention point in terms of boiler operation and maintenance., SCOPUS: ar.j, info:eu-repo/semantics/published

Heavy-duty transport represents nearly 6% of the greenhouse gas emissions in Europe. Renewable hydrogen is a potential option to decarbonize heavy-duty transport, such as buses. Renewable hydrogen for buses promises excellent environmental performance, at the expense of a higher fuel cost, as opposed to a diesel-powered bus fleet. Despite the inherent uncertainty, feasibility studies in this framework generally assume deterministic techno-economic and environmental parameters, which can lead to a suboptimal performance that is sensitive to the random environment. To provide robust design alternatives, we applied robust design optimization on a wind- and solar-powered hydrogen refueling system and a hydrogen- and diesel-powered bus fleet, to optimize the Levelized Cost Of Driving (LCOD) and Carbon Intensity (CI), subject to technical, economic and environmental uncertainties. A fully diesel-powered bus fleet achieves the optimized LCOD mean of 1.24 €/km, but it results in the worst LCOD standard deviation (0.11 €/km), CI mean (1.33 kg˙CO2,eq /km) and CI standard deviation (0.075 kgCO2,eq /km) among the optimized designs. To reduce the LCOD standard deviation, CI mean and CI standard deviation, part of the diesel-powered bus fleet is converted into hydrogen-powered buses and the renewable-powered hydrogen refueling station is scaled accordingly. Converting 54 % of the diesel-powered bus fleet into hydrogen-powered buses results in a decrease in LCOD standard deviation by 36 %, a decrease in CI mean by 46% and a decrease in CI standard deviation by 51%, at the expense of an increase in LCOD mean by only 11 %. Future work will focus on the integration of full-electric buses.

Climate change is a constant call for the massive deployment of intermittent renewable energy sources, such as solar and wind. However, to cover the energy demand at all times, these sources require energy storage over more extended periods. In this framework, renewable energy storage in the form of hydrogen is gaining ground on leading the transition of today’s economy towards decarbonization. Among others, hydrogen can be integrated into multiple energy sectors: hydrogen can be converted back into electricity (power-to-power), it can be used to produce low-carbon fuels (power-to-fuel), and it can be used to fuel hydrogen vehicles (power-tomobility). The performance of these hydrogen-based energy systems is subject to uncertainties, such as the uncertainty on the solar irradiance, the energy consumption of hydrogen-powered buses, and the price of grid electricity. Disregarding these uncertainties in the design process can result in a drastic mismatch between simulated and real-world performance, and thus lead to a kill-by-randomness of the system. The Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems (RHEIA) framework provides a robust design optimization pipeline that considers real-world uncertainties and yields robust designs, i.e., designs with a performance less sensitive to these uncertainties. Moreover, RHEIA includes models to evaluate hydrogen’s techno-economic and environmental performance in a power-to-fuel, power-to-power, and power-to-mobility context. When combined, RHEIA unlocks the robust designs for hydrogen-based energy systems. As RHEIA considers the system models as a black box, the framework can be applied to existing open-source and closed-source models. To illustrate, an interface with the EnergyPLAN software is included in the framework.

European Green Deal sets the EU’s target towards becoming the world’s first climate-neutral continent by 2050. To achieve the 2050 Green Deal target, multi-combined actions are required, such as increasing renewable energy (RE) production in the EU, enhancing efficiency, and importing RE. The limited area, high population density, and geographical position constrain the EU’s RE self-sufficiency; in fact, the energy import dependency of the European Union (EU-27) reached 58.4% and 60.7% in 2018 and 2019, respectively. Interestingly, the final energy consumption by fuel comprises 23% of electricity and 77% of molecules. Consequently, a sustainable energy system requires not only green electricity but green molecules as well to move from fossil to electrified chemical industry (chemistree). In this context, the work analyses the LCOE of importing RE from Morocco, Algeria, Egypt, and Saudi Arabia to selected locations in the EU namely Rome, Madrid, and Cologne, since they have both a well-established energy importing/exporting network with the EU and a high potential of RE sources. A promising LCOE of H2 is found in all importing scenarios with an average of 5.20 €/kgH2. Hydrogen transport via pipelines (0.14 €/kg/1000 km) is found to be the optimal solution for the studied cases. Further investigation is required for importing RE via other types of molecules and e-fuels such as ammonia, methanol, and methane from the Middle East and North Africa (MENA) to the EU.

Direct Air Capture (DAC) technologies extract CO2 directly from the atmosphere and, therefore, compensate for the emissions from sectors that are difficult to decarbonize, e.g. aviation and heavy-duty mobility. However, due to the diluted CO2 in the atmosphere, DAC suffers from high costs and a significant energy footprint. When integrating DAC in power-to-gas systems, several underexplored synergies unfold that reduce the energy and water demand of the system, such as waste heat recycling from methanation and water recovery in the DAC unit. Interconnecting these energy and water streams results in a highly integrated system that is fragile towards changes in ambient and operating conditions. We developed a power-to-gas system with solid sorbent direct air capture and evaluated the energy efficiency and water self-sufficiency ratio under uncertain ambient and operating conditions. The results illustrate that operating at a desorption temperature of 61C, instead of 100oC, results in a water self-sufficient system under average ambient conditions for Belgium, at the expense of a reduction in the energy efficiency of 4% absolute (from 59% to 55%). Considering ambient and operating uncertainties results in a limited uncertainty on the energy efficiency (mean = 59.4%, standard deviation = 0.61%), but a significant uncertainty on the water self-sufficiency ratio (mean = 49.6%, standard deviation = 6.18%). Adopting time series for the ambient conditions is the main action to reduce uncertainty on the quantities of interest. Future work will focus on the dynamic operation of the system, including energy storage and renewable energy technologies.

Regions with abundantly available renewable energy are not necessarily the same as those with a high population density and high energy consumption. Therefore, renewable energy can be produced in optimal climate conditions with a remote renewable hub and transported to these population-dense regions. To establish this energy transport, ammonia provides a flexible, easy-to-handle energy carrier, which already showed a viable option for transporting energy from Australia to Japan. However, current literature rarely considers the impact of techno-economic uncertainty (variable energy consumption or uncertain capital and operational expenses) on the feasibility of this transport. Using those uncertainties, we performed a robust design optimization on the levelized cost of ammonia and the power-to-ammonia efficiency to compare the local (Belgium) and remote (Morocco) ammonia production and transport for Belgium. This paper provides the robust designs (i.e. least sensitive to uncertainty) for local and remote renewable ammonia production and the advantages of both approaches on the levelized cost and power-to-ammonia energy efficiency. The results confirm that ammonia production in regions with high solar irradiance followed by the transport of ammonia is cost-effective and robust (790 euro/tonneNH3 in mean and 128 euro/tonneNH3 in standard deviation) over local production (1334 euro/tonneNH3 in mean and 249 euro/tonneNH3 in standard deviation). However, local ammonia production provides for more efficient and less sensitive power-to-ammonia plant designs (53.6% in mean and 0.1% in standard deviation), while the remote production is less efficient and more sensitive to uncertainties (47.9% in mean and 1.53% in standard deviation). Both objectives are highly influenced by the capacity of the photovoltaic arrays and the electrolyzers, wherein in the case of Morroco, the backup capacity plays a significant role in the system’s efficiency. Future work aims to perform a techno

Waste heat recovery through cycle humidification is considered as an effective tool to increase the operational flexibility of micro gas turbines (mGTs) in cogeneration in a decentralized energy system (DES) context. Indeed, during periods with low heat demand, the excess thermal power can be reintroduced in the cycle under the form of heated water/steam, leading to improved electrical performance. The micro humid air turbine (mHAT) has been proven to be the most effective route for cycle humidification; however, so far, all research efforts focused on optimizing the mHAT performance at nominal electrical load, in the absence of any thermal load. Nevertheless, in a DES context, the thermal and electrical load of the mGT need to be changed depending on the demand, requiring both optimal nominal and part load performances. To address this need, in this paper, we present the first step toward the development of a control strategy for a Turbec T100 mGT-mHAT test rig. First, using experimental data, the global performance, depending on the operating point as well as the humidity level, has been assessed. Second, the performance of the saturation tower, i.e., the degree of saturation (relative humidity) of the working fluid leaving this saturator, is analyzed to assess the optimal water injection system control parameter settings. Results show that optimal mHAT performance can only be obtained when the working fluid leaving the saturation tower is fully saturated, but does not contain a remaining liquid fraction. Under these conditions, a maximal amount of waste heat is transferred from the water to the mGT working fluid in the saturation tower. From these data, some general observations can be made to optimize the performance, being maximizing injection pressure and aiming for a water flow rate of ≈5m3/h. Besides these general recommendations, having a specific control matrix, that allows setting the saturation tower control parameters for any set of operational setpoint

During renewable energy system design, parameters are generally fixed or characterized by a precise distribution. This leads to a representation that fails to distinguish between uncertainty related to natural variation (i.e. future, aleatory uncertainty) and uncertainty related to lack of data (i.e. present, epistemic uncertainty). Consequently, the main driver of uncertainty and effective guidelines to reduce the uncertainty remain undetermined. To assess these limitations on a grid-connected household supported by a photovoltaic-battery system, we distinguish between present and future uncertainty. Thereafter, we performed a robust design optimization and global sensitivity analysis. This paper provides the optimized designs, the main drivers of the variation in levelized cost of electricity and the effect of present uncertainty on these drivers. To reduce the levelized cost of electricity variance for an optimized photovoltaic array and optimized photovoltaic-battery design, improving the determination of the electricity price for every specific scenario is the most effective action. For the photovoltaic-battery robust design, the present uncertainty on the prediction accuracy of the electricity price should be addressed first, before the most effective action to reduce the levelized cost of electricity variance can be determined. Future work aims at the integration of a heat demand and hydrogen-based energy systems.

Wind and solar energies present a time and space disparity that generally leads to a mismatch between the demand and the supply. To harvest their maximum potentials, one of the main challenges is the storage and transport of these energies. This challenge can be tackled by electrofuels, such as hydrogen, methane, and methanol. They offer three main advantages: compatibility with existing distribution networks or technologies of conversion, economical storage solution for high capacity, and ability to couple sectors (i.e. electricity to transport, to heat, or to industry). However, the level of contribution of electric-energy carriers is unknown. To assess their role in the future, we used whole-energy system modelling (EnergyScope Typical Days) to study the case of Belgium in 2050. This model is multi-energy and multi-sector. It optimises the design of the overall system to minimise its costs and emissions. Such a model relies on many parameters (e.g. price of natural gas, efficiency of heat pump) to represent as closely as possible the future energy system. However, these parameters can be highly uncertain, especially for long-term planning. Consequently, this work uses the polynomial chaos expansion method to integrate a global sensitivity analysis in order to highlight the influence of the parameters on the total cost of the system. The outcome of this analysis points out that, compared to the deterministic cost-optimum situation, the system cost, accounting for uncertainties, becomes higher (+17%) and twice more uncertain at carbon neutrality and that electrofuels are a major contribution to the uncertainty (up to 53% in the variation of the costs) due to their importance in the energy system and their high uncertainties, their higher price, and uncertainty., SCOPUS: ar.j, info:eu-repo/semantics/published

Although the positive impact of cycle humidification on the performance of micro Gas Turbines (mGTs) has already been proven numerically and experimentally, very detailed modeling of the system performance remains challenging, especially the determination of the recuperator effectiveness, which has the highest impact on the final cycle performance. Indeed, the recuperator performance depends strongly on the mass flow rate of the air stream and its humidification level, two parameters that are difficult to measure accurately. Accurate modeling of the recuperator performance under both dry and humidified conditions is thus essential for correct assessment of the potential of humidified mGT cycles. In this paper, we present a detailed analysis of the recuperator performance under humidified conditions using averaged experimental data, extended with the application of a Support Vector Regression (SVR) on a time series to improve noise-modeling of the output signal, and thus enhance the accuracy of the monitoring process. In a first step, the missing experimental parameters were obtained indirectly, using experimental data in combination with the compressor map. Despite the low accuracy, some general trends could be observed, indicating that the recuperator, despite having an increased total exchanged heat flux, is too small to exploit the full potential of the humidification. In a second step, by means of the SVR model, a first attempt was made to improve the accuracy and reduce the scatter on the recuperator performance determination. The predicted results with the SVR indicated indeed a reduced scatter, opening a pathway towards online recuperator performance prediction.

The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Micro Gas Turbines (mGTs) constitutes a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of post-combustion Carbon Capture (CC) on these energy systems. To reduce the CC energy penalty, Exhaust Gas Recirculation (EGR) can be applied to the mGTs increasing the CO2 content in the exhaust gas and reducing the mass flow rate of flue gas to be treated. As a result, a lower investment and operational cost of the CC unit can be achieved. In spite of this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with EGR has been coupled with an amine-based CC plant and simulated using the software Aspen Plus®. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian Process Regression (GPR) model, trained using the Aspen Plus® data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a robust optimization using a Non-dominated Sorting Genetic Algorithm II (NSGA II) has been carried out, assessing the influence of each input uncertainty and varying several design variables. As a general result, the analysed power plant proves to be intrinsically very robust, even when the input variables are affected by strong

Balancing of intermittent energy such as solar energy can be achieved by batteries and hydrogen-based storage. However, combining these systems received limited attention in a grid-connected framework and its design optimization is often performed assuming fixed parameters. Hence, such optimization induces designs highly sensitive to real-world uncertainties, resulting in a drastic mismatch between simulated and actual performances. To fill the research gap on design optimization of grid-connected, hydrogen-based renewable energy systems, we performed a computationally efficient robust design optimization under different scenarios and compared the stochastic performance based on the corresponding cumulative density functions. This paper provides the optimized stochastic designs and the advantage of each design based on the financial flexibility of the system owner. The results illustrate that the economically preferred solution is a photovoltaic array when the self-sufficiency ratio is irrelevant (≤30%). When a higher self-sufficiency ratio threshold is of interest, i.e. up to 59%, photovoltaic-battery designs and photovoltaic-battery-hydrogen designs provide the cost-competitive alternatives which are least-sensitive to real-world uncertainty. Conclusively, including storage systems improves the probability of attaining an affordable levelized cost of electricity over the system lifetime. Future work will focus on the integration of the heat demand., SCOPUS: ar.j, DecretOANoAutActif, info:eu-repo/semantics/published

Improved management and impermeability of refrigerants is a leading solution to reverse global warming. Therefore, crank-driven reciprocating refrigerator compressors are gradually replaced by more efficient, oil-free and hermetic linear compressors. However, the design and operation of an electromagnetic actuator, fitted on the compression requirements of a reciprocating linear compressor, received limited attention. Current research mainly focuses on the optimisation of short stroke linear compressors, while long stroke compressors benefit from higher isentropic and volumetric efficiencies. Moreover, designing such a system focuses mainly on the trade-off between number of copper windings and the current required, due to the large computational cost of performing a full geometric design optimisation based on a Finite Element Method. Therefore, in this paper, a computationally-efficient, multi-objective design optimisation for six geometric design parameters has been applied on a solenoid driven linear compressor with a stroke of 44.2 mm. The proposed multi-fidelity optimisation approach takes advantage of established models for actuator optimisation in mechatronic applications, combined with analytical equations established for a solenoid actuator to increase the overall computational efficiency. This paper consists of the multi-fidelity optimisation algorithm, the analytic model and Finite Element Method of a solenoid and the optimised designs obtained for optimised power and copper volume, which dominates the actuator cost. The optimisation results illustrate a trade-off between minimising the peak power and minimising the volume of copper windings. Considering this trade-off, an intermediate design is highlighted, which requires 33.3% less power, at the expense of an increased copper volume by 5.3% as opposed to the design achieving the minimum copper volume. Despite that the effect of the number of windings on the input current remains a dominant design charac, SCOPUS: ar.j, info:eu-repo/semantics/published

The increasing share of wind energy induces a strain on the electricity network. To unburden the transmission system operators from this strain, the dispensable wind energy can locally be stored in an energy carrier, e.g. ammonia (NH3). Existing work considers fixed operational parameters during design optimization to represent real-life conditions of the Power-to-NH3 system. However, uncertainties significantly affect real-life performances, which can lead to suboptimal plants. To provide a robust design—least sensitive to uncertainties—we considered the main operational uncertainties during design optimization and illustrated the contribution of each uncertainty on the systems NH3 production. This work presents the optimization under uncertainty of the Power-to-NH3 process and a global sensitivity analysis on the optimized designs. The results revealed a design trade-off, where a productive design produces 3.2 times more NH3 on average, but is 2.6 times less robust (higher standard deviation) than the robust design. A global sensitivity analysis on the most robust design showed that the temperature fluctuation of the NH3 reactor dominates the average NH3 production by 99.7%. The same sensitivity analysis on the most productive design showed that the wind speed measurement error and the temperature variation are both influencing the ammonia production by respectively 75.4% and 22.5%. Accordingly, an accurate anemometer and improving the temperature control over the NH3 reactor are the most effective actions to make the most productive design more robust. However, a robust plant can be obtained by decreasing the load size of the plant. It suffices to improve the temperature control over the NH3 reactor to make this design (adopted from the trade-off) less sensitive to the noise. Future investigations involve analyzing the dynamic operations of the robust Power-to-NH3 pathway and analyze the impact of uncertainties on its levelized cost., SCOPUS: ar.j, info:eu-repo/semantics/published

Due to their high total efficiency and flexibility, micro Gas Turbines (mGTs) offer great potential for use in small-scale distributed cogeneration applications. The economic success of this application; however, fully depends on the optimal usage of the system, which requires careful selection of the number and size of the units in the system and their specific operating strategy. This is only possible if the performance of each individual unit is known precisely. However, in real world operating conditions, the parameters determining the operation and performance of an mGT are only known with a certain uncertainty. Depending on the sensitivity of the model to these parameters, the uncertainties may have a strong negative effect on the performance of the mGT. These uncertainties should thus be taken into consideration by the designers in an early stage of the design process to achieve a so-called robust design. In this paper, we present the robust optimization of a typical mGT, the Turbec T100, operation. This optimization under uncertainties is based on a classical multi-objective optimization scheme linked with an uncertainty propagation technique. In this approach, a robust optimum is found, less sensitive to variations in design and operation parameters. The deterministic optimization results in a Pareto front for maximal electrical efficiency and power output, highlighting that the two objectives are conflicting. The impact of the uncertainties on the parameters is translated into a slight negative shift in this Pareto front. Finally, the most robust operation can be found at a power output of 106.5 kWe, corresponding to a maximal efficiency of 30.6%., SCOPUS: cp.p, info:eu-repo/semantics/published