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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]