Advances in Energy Hybridization for Resilient Supply: A Sustainable Approach to the Growing World Demand

Abstract

Energy poverty, defined as a lack of access to reliable electricity and reliance on traditional biomass resources for cooking, affects over a billion people daily. The World Health Organization estimates that household air pollution from inefficient stoves causes more premature deaths than malaria, tuberculosis, and HIV/AIDS. Increasing demand for energy has led to dramatic increases in emissions. The need for reliable electricity and limiting emissions drives research on Resilient Hybrid Energy Systems (RHESs), which provide cleaner energy through combining wind, solar, and biomass energy with traditional fossil energy, increasing production efficiency and reliability and reducing generating costs and emissions. Microgrids have been shown as an efficient means of implementing RHESs, with some focused mainly on reducing the environmental impact of electric power generation. The technical challenges of designing, implementing, and applying microgrids involve conducting a cradle-to-grave Life Cycle Analysis (LCA) to evaluate these systems’ environmental and economic performance under diverse operating conditions to evaluate resiliency. A sample RHES was developed and used to demonstrate the implementation in rural applications, where the system can provide reliable electricity for heating, cooling, lighting, and pumping clean water. The model and findings can be utilized by other regions around the globe facing similar challenges.

Resilience-Driven Optimal Sizing of Energy Storage Systems in Remote Microgrids

“…The maintenance costs for the diesel generation, microturbine, wind turbine, and photovoltaics are calculated in (4) for each time frame t using the cost coefficients γ diesel , γ MT , γ WT , and γ PV . Here, P PV and P WT are solar and wind generation during time frame t. The pollutants' emissions costs are computed for the two types of conventional generators in (5) based on the price coefficient ψ and emission factor µ. In order to prioritize sensitive users, a shedding cost τ i will be assigned to each load i in (6), while P sh,i defines the shedding power applied to each load i.…”

“…On the other hand, there are still numerous remote areas that face the so-called energy poverty, defined by the lack of access to reliable electricity service [5]. In most cases, the supply of areas isolated from the main grid is achieved with difficulty through the traditional power lines.…”

Optimal Design of Hybrid Microgrid in Isolated Communities of Ecuador

“…These algorithms use different objective functions to utilize renewable resources as much as possible. For this purpose, it is possible to minimize functions like the power produced by diesel generator (genset) in a defined time interval [3], [26], the total investment and operating cost [8] - [10], [17] - [20], [23], [31], [32], the levelized cost of electricity, greenhouse gas emissions [5], [30], and the probability of power outage [7]. As shown in Table I, the current trend is using financial indicators as a decision-making criterion for the design of microgrid, while the technical requirements are incorporated through optimization constraints.…”

Water, Resources, and Resilience: Insights from Diverse Environmental Studies

“…The growing water crisis, exacerbated by factors such as climate change, population growth, and pollution, has compelled us to gather insights from various environmental studies [23][24][25][26][27][28][29][30][31][32][33]. We aim to disseminate knowledge that can drive positive change and foster innovative solutions for the responsible and sustainable management of our water resources [34][35][36][37][38][39][40][41][42].…”