Different scenarios at different scales must be studied to help define long term policies to decarbonate our societies. In this work, we analyse the Belgian energy system in 2035 for different carbon emission targets, and accounting for electricity, heat, and mobility. To achieve this objective, we applied the EnergyScope Typical Days open source model, which optimises both the investment and the operation strategy of a complete energy system for a target year. The model includes 96 technologies and 24 resources that have to supply, hourly, the heat, electricity, mobility, and non-energy demands. In line with other research, we identify and quantify, with a merit order, different technological steps of the energy transition. The lack of endogenous resources in Belgium is highlighted and estimated at 275.6 TWh/y. It becomes obvious that additional potentials shall be obtained by importing renewable fuels and/or electricity, deploying geothermal energy, etc. Aside from a reduction of the energy demand, a mix of solutions is shown to be, by far, the most cost effective to reach low carbon emissions.

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.

There is currently no consensus regarding whether or not renewable energies are capable of supplying all of our energy needs in the near future. To shed new light on this controversy, this paper develops a methodology articulating a macroeconomic model with two sectors (energy and non-energy) and an energy model that is able to calculate the maximum potentials of solar and wind energy. The results show that, in a business-as-usual context, a complete energy transition on a global scale is unachievable before the end of the century. The reason lies in the increasing capital needs of the energy sector, which slows, if not stops, economic growth and the energy transition. A complete transition can be achieved by 2070 provided that (i) energy demand is kept under control at its current level, (ii) a sufficient rate of capital growth is sustained (above its historical level), and (iii) substantial progress is made in terms of energy efficiency. However, this strategy requires a significant increase in the savings rate, with a negative impact on consumption, which ends up stagnating at the end of the transition.

Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., a priori evaluation). However, its impact on the results of the energy system model (i.e., a posteriori evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up a posteriori metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this a priori metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.

For decentralized power generation in West Africa, gas from a small biomass gasification unit can be used as the main fuel in a dual-fuel engine with diesel as the pilot fuel. To study the combustion in this type of engine (Lister Petter), experiments were conducted with a surrogate gas composed of liquefied petroleum gas and nitrogen (LPGN2), the energy context of which is similar to that of syngas. The tests were conducted at different loads and for different diesel substitution rates. The combustion analysis showed that the LPGN2 mixture had an overall behaviour similar to neat diesel, while the pressure peaks were lower in dual-fuel mode. The results also indicated a longer ignition delay and a pronounced diffusive combustion phase leading to a lower indicated mean effective pressure with gas. The fuel efficiencies remained low in both mono- and dual-fuel operation. The relative instability of the combustion in dual-fuel mode gave rise to an increase in the coefficient of variation (COV_IMEP). Compared to neat diesel, the engine running at low loads in dual-fuel mode showed higher emission levels of CO, a slight reduction of 2.5% of CO₂ and a substantial decrease of 73% for nitrogen oxides.