Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that “security effectiveness” holds the highest importance, followed by “cost-effectiveness”, “scalability”, and “Integration and compatibility”, while other criteria (i.e., “performance impact”, “manageability and usability”, “compliance and regulatory requirements”, “resilience and redundancy”, “vendor support and collaboration”, and “future readiness”) contribute to the evaluation but have relatively lower weights. Alternatives such as “access control and authentication” and “security information and event management” with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as “compliance and regulatory requirements” and “encryption” have lower weighted sums but still provide value in their respective criteria. We also find that “deep learning” emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by “hybrid approaches”, “Bayesian networks”, “swarm intelligence”, and “machine learning”, while “fuzzy logic”, “natural language processing”, “expert systems”, and “genetic algorithms” exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.

Global urbanization and increasing water demand make efficient water resource management crucial. This study employs Multi-Criteria Decision Making (MCDM) to evaluate smart city water management strategies. We use representative criteria, employ objective judgment, assign weights through the Analytic Hierarchy Process (AHP), and score strategies based on meeting these criteria. We find that the “Effectiveness and Risk Management” criterion carries the highest weight (15.28%), underscoring its pivotal role in strategy evaluation and robustness. Medium-weight criteria include “Resource Efficiency, Equity, and Social Considerations” (10.44%), “Integration with Existing Systems, Technological Feasibility, and Ease of Implementation” (10.10%), and “Environmental Impact” (9.84%) for ecological mitigation. “Community Engagement and Public Acceptance” (9.79%) recognizes involvement, while “Scalability and Adaptability” (9.35%) addresses changing conditions. “Return on Investment” (9.07%) and “Regulatory and Policy Alignment” (8.8%) balance financial and governance concerns. Two low-weight criteria, “Data Reliability” (8.78%) and “Long-Term Sustainability” (8.55%), stress data accuracy and sustainability. Highly weighted strategies like “Smart Metering and Monitoring, Demand Management, Behavior Change” and “Smart Irrigation Systems” are particularly effective in improving water management in smart cities. However, medium-weighted (e.g., “Educational Campaigns and Public Awareness”, “Policy and Regulation”, “Rainwater Harvesting”, “Offshore Floating Photovoltaic Systems”, “Collaboration and Partnerships”, “Graywater Recycling and Reuse”, and “Distributed Water Infrastructure”) and low-weighted (e.g., “Water Desalination”) strategies also contribute and can be combined with higher-ranked ones to create customized water management approaches for each smart city’s unique context. This research is significant because it addresses urban water resource management complexity, offers a multi-criteria approach to enhance traditional single-focused methods, evaluates water strategies in smart cities comprehensively, and provides a criteria-weight-based resource allocation framework for sustainable decisions, boosting smart city resilience. Note that results may vary based on specific smart city needs and constraints. Future studies could explore factors like climate change on water management in smart cities and consider alternative MCDM methods like TOPSIS or ELECTRE for strategy evaluation.

Although climate change is an inherently global issue, its impacts will not be felt equally across Earth’s pressure belts and continental-scale regions. This study seeks to examine which areas are becoming warmer and experiencing drought, with a particular focus on Africa, in light of its low historical emissions but poor economic capacity for mitigation and adaptation to climate change, and Morocco, whose conditional goal, which will be achieved with foreign assistance, is rated as “almost sufficient” but is not yet in compliance with the Paris Agreement’s goal. We also explore the consistency and sources of uncertainty in Coupled Model Intercomparison Project Phase 6 (CMIP6) models and analyze what changes from CMIP5—whose projections are based on the Representative Concentration Pathways (RCPs)—to Shared Socio-Economic Pathways (SSPs)-based scenarios for CMIP6. We find that strong forcing, with no additional climate policies, is projected to raise the mean annual temperature over Morocco for the long-term period by 6.25 ${}^{°}$C. All CMIP6 models agree that warming (resp. drought) will be greater over land masses and poles (resp. tropical and coastal regions) than over oceans and equatorial regions (resp. high latitudes, equatorial, and monsoon zones), but less so on the intensity of changes.

In the era of rapid urbanization and technological progress, smart cities offer a promising solution to multifaceted global challenges, leveraging advanced technologies to optimize resources and enhance the quality of life; however, this interconnectedness also exposes them to novel vulnerabilities, particularly in the face of natural and man-made disasters, necessitating inventive strategies to ensure resilience against cyber threats and extreme weather events. This article delves into the exploration of smart cities’ diverse aspects and the categories of disasters they face, followed by an analysis of strategic mitigation approaches and their underlying criteria; it subsequently introduces the Multi-Criteria Decision-Making methodology, particularly Analytical Hierarchy Process (AHP), as a robust tool for systematic evaluation and prioritization of disaster management strategies in the increasingly complex landscape. The study’s analysis of relative weights underscores the pivotal role of resilience enhancement and communication redundancy as primary considerations in evaluating disaster management strategies for smart cities, while other criteria such as accuracy and timeliness, scaleability and adaptability, cost-effectiveness, ethical and privacy considerations, and training and skill requirements assume varying degrees of importance in supporting roles, providing valuable insights into the decision-making process. The assessment of alternative strategies highlights their prioritization in effective disaster management for smart cities, with notable emphasis on citizen engagement and education, early warning systems, and data analytics; further strategies such as integrated communication systems, resilient infrastructure design, drones and robotics, artificial intelligence algorithms, and IoT-enabled sensors and monitoring exhibit varying degrees of significance, offering insights into their roles and potential contributions to disaster management strategies based on their weighted sums. This research has practical significance, guiding stakeholders like urban planners, policymakers, and disaster management professionals to enhance smart city resilience and prioritize strategies based on critical factors, ultimately enabling effective disaster management in smart cities amid 21st-century challenges.

In this paper, we analyze the sensitivity of the optimal mixes to cost and variability associated with solar technologies and examine the role of Thermal Energy Storage (TES) combined to Concentrated Solar Power (CSP) together with time-space complementarity in reducing the adequacy risk—imposed by variable Renewable Energies (RE)—on the Moroccan electricity system. To do that, we model the optimal recommissioning of RE mixes including Photovoltaic (PV), wind energy and CSP without or with increasing levels of TES. Our objective is to maximize the RE production at a given cost, but also to limit the variance of the RE production stemming from meteorological fluctuations. This mean-variance analysis is a bi-objective optimization problem that is implemented in the E4CLIM modeling platform—which allows us to use climate data to simulate hourly Capacity Factors (CFs) and demand profiles adjusted to observations. We adapt this software to Morocco and its four electrical zones for the year 2018, add new CSP and TES simulation modules, perform some load reduction diagnostics, and account for the different rental costs of the three RE technologies by adding a maximum-cost constraint. We find that the risk decreases with the addition of TES to CSP, the more so as storage is increased keeping the mean capacity factor fixed. On the other hand, due to the higher cost of CSP compared to PV and wind, the maximum-cost constraint prevents the increase of the RE penetration without reducing the share of CSP compared to PV and wind and letting the risk increase in return. Thus, if small level of risk and higher penetrations are targeted, investment must be increased to install more CSP with TES. We also show that regional diversification is key to reduce the risk and that technological diversification is relevant when installing both PV and CSP without storage, but less so as the surplus of energy available for TES is increased and the CSP profiles flatten. Finally, we find that, thanks to TES, CSP is more suited than PV and wind to meet peak loads. This can be measured by the capacity credit, but not by the variance-based risk, suggesting that the latter is only a crude representation of the adequacy risk.