Greece confronts intensifying water scarcity driven by population growth, urbanization, tourism, and climate variability, despite its extensive coastline. Traditional sources are strained, with agriculture consuming ~80% of withdrawals (surface water ~38%, groundwater ~62%). Desalination, predominantly reverse osmosis (RO), offers a mature solution, already meeting 30–95% of domestic needs in Aegean islands, but its energy intensity challenge sustainability within the water–energy–food nexus. This study presents a geospatial framework to assess energy requirements for a hypothetical scenario in which seawater desalination fully supplies domestic water demand in Greece. High-resolution GIS data, WorldPop population grids, and hydrological networks enable estimation of daily demand (173 L/capita/day) and energy decomposition: desalination (SEC = 5 kWh/m3 SWRO), elevation pumping plus residual pressure (15 m head), and frictional losses. The hypothetical pipelines follow reverse natural drainage paths for realistic routing. Results highlight substantial spatial disparities: inland cities face significantly higher and more uniform energy costs (Ioannina: mean dynamic head 8.3 kWh/m3, ~43% higher than the coastal reference of Athens at 5.8 kWh/m3), driven by elevation and distance; coastal centres show lower means but greater variability (Athens: highest total ~3.35 GWh/day). In summary, fully supplying domestic water demand via desalination would necessitate an additional ~8% of the country’s total electricity consumption. Findings affirm desalination’s potential for coastal/island supply while revealing energy barriers inland.
Flexible interconnection among different building types holds significant importance for integrating distributed energy resources, mitigating regional load peak-valley differences, and enhancing the local consumption capacity of renewable energy. Addressing the issue of insufficient multi-energy synergy in multi-building clusters, this paper proposes a bi-level optimal configuration method for flexible interconnected energy systems that accounts for multi-energy complementarity. By constructing a comprehensive multi-energy flow model encompassing all elements of source, network, load, storage, and conversion, a bi-level optimization framework is established. The upper level aims to minimize total lifecycle cost and carbon emissions, while the lower level targets maximizing the renewable energy self-consumption rate and minimizing daily operational cost. An improved NSGA-II algorithm integrating Lévy flight and a good point set is employed for an efficient solution. Simulation results demonstrate that the proposed scheme can achieve cross-spatiotemporal energy transfer and multi-energy collaborative optimization. In a typical summer day scenario, the system’s renewable energy self-consumption rate increased to 96.20%, operational cost was reduced by 8.83%, and carbon emissions decreased by 10.18%, validating the effectiveness and superiority of the method in improving energy utilization efficiency and supporting the low-carbon and economic transition of regional building systems. The outcomes of this study can provide theoretical support and engineering reference for the low-carbon, economical, and efficient planning of multi-building energy systems.
Although fossil fuels are the primary source of energy in the world, their greenhouse gas emissions and other pollutants provide serious environmental problems. This study uses a gasoline blend with ethanol and methanol to examine the emissions and performance of a spark ignition (SI) engine. An experimental design focused on engine input factors such as load and fuel blends. Brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), and emissions of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx) were examined about these parameters using Taguchi’s L16 orthogonal array and ANOVA via Minitab 18. The results show that 80% engine load and a 15% blend for both ethanol and methanol provide the best engine performance, greatly lowering BSFC and raising BTE. Notably, 20% engine load and 15% blend result in the lowest CO emissions, whilst 20% load and 0% blend result in the lowest NOx emissions. Also, 20% load and 15% blend result in the lowest HC emissions. This study highlights the potential of alternative fuel blends to improve engine efficiency and reduce hazardous emissions.
