Method for assessing the potential of miscanthus on marginal lands for high temperature heat demand : The case studies of France and Belgium

ACKNOWLEDGMENTS This study was funded by the Energy Transition Fund of Belgium. This support is gratefully acknowledged. AH was funded by UKRI BB/V0115533/1 and ER/S029575/1 grants.Peer reviewedPublisher PD

Towards CO2 valorization in a multi remote renewable energy hub framework with uncertainty quantification

peer reviewedIn this paper, we propose a multi-RREH (Remote Renewable Energy Hub) based optimization framework. This framework allows a valorization of CO2 using carbon capture technologies. This valorization is grounded on the idea that CO2 gathered from the atmosphere or post combustion can be combined with hydrogen to produce synthetic methane. The hydrogen is obtained from water electrolysis using renewable energy (RE). Such renewable energy is generated in RREHs, which are locations where RE is cheap and abundant (e.g., solar PV in the Sahara Desert, or wind in Greenland). We instantiate our framework on a case study focusing on Belgium and 2 RREHs, and we conduct a techno-economic analysis under uncertainty. This analysis highlights, among others, the interest in capturing CO2 via Post Combustion Carbon Capture (PCCC) rather than only through Direct Air Capture (DAC) for methane synthesis in RREH. By doing so, a notable reduction of 10% is observed in the total cost of the system under our reference scenario. In addition, we use our framework to derive a carbon price threshold above which carbon capture technologies may start playing a pivotal role in the decarbonation process of our industries. For example, this price threshold may give relevant information for calibrating the EU Emission Trading System so as to trigger the emergence of the multi-RREH

Optimizing upside variability and antifragility in renewable energy system design

Despite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces an antifragility indicator. The variability is optimized by favoring upside potential and providing downside protection towards a minimum acceptable performance, while the skewness indicates (anti)fragility. An antifragile design primarily enhances positive outcomes when the uncertainty of the random environment exceeds initial estimations. Hence, it circumvents the issue of underestimating the uncertainty in the operating environment. We applied the methodology to the design of a wind turbine for a community, considering the Levelized Cost Of Electricity (LCOE) as the quantity of interest. The design with optimized variability proves beneficial in 81% of the possible scenarios when compared to the conventional robust design. The antifragile design flourishes (LCOE drops by up to 120%) when the real-world uncertainty is higher than initially estimated in this paper. In conclusion, the framework provides a valid metric for optimizing the variability and detects promising antifragile design alternatives

Optimizing upside variability and antifragility in renewable energy system design

Abstract Despite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces an antifragility indicator. The variability is optimized by favoring upside potential and providing downside protection towards a minimum acceptable performance, while the skewness indicates (anti)fragility. An antifragile design primarily enhances positive outcomes when the uncertainty of the random environment exceeds initial estimations. Hence, it circumvents the issue of underestimating the uncertainty in the operating environment. We applied the methodology to the design of a wind turbine for a community, considering the Levelized Cost Of Electricity (LCOE) as the quantity of interest. The design with optimized variability proves beneficial in 81% of the possible scenarios when compared to the conventional robust design. The antifragile design flourishes (LCOE drops by up to 120%) when the real-world uncertainty is higher than initially estimated in this paper. In conclusion, the framework provides a valid metric for optimizing the variability and detects promising antifragile design alternatives

Robust design optimization and stochastic performance analysis of a grid-connected photovoltaic system with battery storage and hydrogen storage

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 (%). 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

Robust design optimization of a renewable-powered demand with energy storage using imprecise probabilities

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

Techno-economic uncertainty quantification and robust design optimization of a directly coupled photovoltaic-electrolyzer system

To solve the problem of large time shifts between renewable energy supply and user demand, power-to-H2 is a well-known option. In this framework, previous studies have shown that the direct coupling of a photovoltaic array with an electrolyzer stack is a viable solution. However, these studies assumed perfectly known operating parameters to optimize the setup. Moreover, they focused on maximizing hydrogen and minimizing the energy loss, while the cost was not addressed. We have performed an optimization including uncertainty quantification (i.e. surrogate-assisted robust design optimization) for several locations with the Levelized Cost Of Hydrogen (LCOH) as objective. This paper provides the least sensitive design to uncertainties and shows which parameters are most affecting the variability of the LCOH for that design. The robust design optimization illustrates that the mean and standard deviation of the LCOH are non-conflicting objectives for the robust designs of all considered locations. The optimal robust design is established at the considered location with the highest average yearly solar irradiance, achieving a mean LCOH of 6.6 €/kg and a standard deviation of 0.72 €/kg. The discount rate uncertainty is the main contributor to the LCOH variation. Therefore, installing a PV-electrolyzer system in locations with a high average yearly solar irradiation is favorable for both the LCOH mean and standard deviation, while de-risking the technology has the highest impact on further reducing the LCOH variation. Future works will focus on including accurate probability distributions and adding batteries to the system.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Where to build the ideal solar-powered ammonia plant? Design optimization of a Belgian and Moroccan power-to-ammonia plant for covering the Belgian demand under uncertainties

Regions with abundantly available renewable energy are not necessarily the same as those with a high population density and 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 to these regions, ammonia provides a flexible, easy-to-handle energy carrier. However, current literature rarely considers the impact of techno-economic uncertainty 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 to 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 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 (601 euro/tonneNH3 in mean and 98 euro/tonneNH3 in standard deviation) over local production (852 euro/tonneNH3 in mean and 139 euro/tonneNH3 in standard deviation). However, local ammonia production provides for more efficient (54.8% in mean) and less sensitive power-to-ammonia plant designs (0.16% in standard deviation), while the production in Morocco is less efficient (52.2% in mean) and more sensitive to uncertainties (0.39% in standard deviation). The capacity of the photovoltaic arrays and the electrolyzers highly influences both objectives. The sensitivity analysis shows that capital and operational expenses of the photovoltaics and electrolyzer stack dominate the designs with the lowest levelized cost in mean and standard deviation. However, the energy consumption uncertainty of the Haber–Bosch also impacts the cost of the lowest mean levelized cost. This uncertainty also dominates the designs with the highest energy efficiency in mean and lowest standard deviation

Where to build the ideal solar-powered ammonia plant? Design optimization of a Belgian and Moroccan power-to-ammonia plant for covering the Belgian demand under uncertainties

Regions with abundantly available renewable energy are not necessarily the same as those with a high population density and 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 to these regions, ammonia provides a flexible, easy-to-handle energy carrier. However, current literature rarely considers the impact of techno-economic uncertainty 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 to 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 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 (601 euro/tonneNH3 in mean and 98 euro/tonneNH3 in standard deviation) over local production (852 euro/tonneNH3 in mean and 139 euro/tonneNH3 in standard deviation). However, local ammonia production provides for more efficient (54.8% in mean) and less sensitive power-to-ammonia plant designs (0.16% in standard deviation), while the production in Morocco is less efficient (52.2% in mean) and more sensitive to uncertainties (0.39% in standard deviation). The capacity of the photovoltaic arrays and the electrolyzers highly influences both objectives. The sensitivity analysis shows that capital and operational expenses of the photovoltaics and electrolyzer stack dominate the designs with the lowest levelized cost in mean and standard deviation. However, the energy consumption uncertainty of the Haber–Bosch also impacts the cost of the lowest mean levelized cost. This uncertainty also dominates the designs with the highest energy efficiency in mean and lowest standard deviation.SCOPUS: ar.jinfo:eu-repo/semantics/publishe