One of the key challenges of the twenty-first century is the decarbonisation of energy systems amid a rapidly growing demand for energy. One of the key processes to achieve this objective is the electrification of these energy systems, coupled with the decarbonisation of the power system. This ambition has led to increasing investments in variable renewable energy deployment, mostly in the form of solar and wind energy. While their capital costs have plummeted in recent years, these technologies are inherently subject to a high degree of intermittency due to their dependence on meteorological conditions. Therefore, energy storage is widely recognised as an indispensable enabler of the energy transition. The interdependencies between generators, storage facilities and other network components make long-term energy planning a highly complex task. Energy system optimisation models are commonly employed to tackle this complexity. However, these are typically used in a deterministic manner, hereby failing to represent the significant uncertainty associated with a range of important policy, technical and economic exogenous factors. Also, this approach fails to reveal any non-linear dynamics in these models, such as regime shifts and tipping points. Responding to the growing emphasis in the literature on the need for global sensitivity analysis in long-term energy planning, this paper proposes a Bayesian methodology for the exploration and probabilistic characterisation of scenarios based on the output of an investment and dispatch co-optimisation model.

To illustrate the applicability of the proposed methodology in long-term energy planning, the exploration of an optimal spatial deployment strategy of closed-loop pumped hydro energy storage in Kenya by 2050 is used as a case study. For this purpose, the paper introduces PyPSA-KE, an investment and dispatch co-optimisation model for Kenya based on the PyPSA-Earth framework, calibrated using context-specific data. While the absolute capacities to deploy are highly dependent on the input parameters, the model indicates that PHS offers great potential to contribute to Kenya's energy transition and growth, supported by batteries for short-term storage.

The Gaussian copula-based Bayesian Network provides a computationally efficient and accurate surrogate for scenario inference in a context of epistemic uncertainty. It also captures the intrinsic structural sensitivity of the IDOM it is based on. This enables the derivation of probabilistic risk metrics that are essential for robust, risk-aware investment optimisation. The proposed Bayesian methodology is readily transferable to other ESOM-based problems.

This research explores the feasibility of implementing ceramic microfiltration (CMF) treatment in Maputo, Mozambique, to reclaim wastewater for industrial reuse, addressing the city's pressing water scarcity challenges. As rapid urbanization increases Maputo's reliance on potable water for industrial and agricultural needs, this study evaluates reclaimed wastewater as a sustainable alternative to alleviate demand on the city's limited freshwater resources. Using a CMF pilot plant, the project tested wastewater from the recently upgraded Infulene Wastewater Treatment Plant (WWTP) to assess whether CMF treatment could achieve quality standards suitable for applications such as cooling, concrete production, car washes, agricultural irrigation, and municipal park irrigation. Furthermore, the opportunity of scalability was tested through a water balance, while relevant stakeholders were interviewed and costs estimated to complete the feasibility assessment.

Laboratory results indicated that CMF treatment effectively reduces turbidity, chemical oxygen demand (COD), and biological pollutants like E. coli and coliforms. However, dissolved particles and heavy metals were not removed, limiting its efficacy for high-specification uses. While the treated effluent met quality standards for lower-specification applications, such as local car washes and park irrigation, it did not reach the stricter requirements needed for cooling water or concrete production. This underscores a need for process optimization, particularly through coagulation, to expand CMF's application range.

To assess sustainable water availability, a water balance analysis of the Infulene WWTP considered seasonal flows and local agricultural demands. The findings suggest that although the current water supply is insufficient during dry months, full capacity utilization and improved sewer network connections in the future could support CMF-based water reuse consistently across seasons, with potential scalability for additional users.

Economic analysis compared CMF's capital and operational costs with revenue from reclaimed water sales, showing that while considerable initial investment is required, direct piping could potentially make CMF-treated water competitively priced against potable supplies under the condition of reaching maximum treatment capacity at a scaled up CMF plant. High costs associated with truck-based delivery, however, present a barrier to adoption for potential users. Stakeholder interest was strong across industrial users and developers, though contingent on achieving cost parity with the existing water network.

This study concludes that, while integrating CMF technology into Maputo's water management strategy offers promise, challenges remain in achieving quality standards for certain industrial applications and in lowering costs. Addressing these technical and economic barriers could open avenues for CMF's broader adoption, especially with future assessments that include alternative suppliers and configurations.