Search Results (205)

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

Coastal regions of Ecuador, particularly Esmeraldas and Manabí, face significant challenges related to energy access, waste management, and sustainable agricultural development. This study evaluates the renewable energy potential of cocoa waste biomass generated by smallholder farms in these provinces. A total of 20 cocoa farms, either certified or in the process of certification under the Rainforest Alliance standard, were surveyed to quantify the volume of agricultural and agro-industrial residues. Residual biomass generation ranged from 50 to 6500 tons per year, depending on farm size, planting density, and management practices. Spatial analysis revealed that Esmeraldas holds the highest concentration of cocoa waste biomass, with some farms reaching a gross energy potential of up to 89.07 TJ/year. Using thermochemical conversion scenarios, effective energy potential was estimated, and 75% of the farms exceeded the viability threshold of 100 MWh/year. The results confirm the feasibility of cocoa biomass as a renewable energy source, mainly when managed collectively at the community level. Incorporating this waste into decentralized energy systems supports circular economy models, enhances energy self-sufficiency, and aligns with sustainable supply chain goals promoted by certification schemes. This study contributes to national efforts in energy diversification and provides a replicable model for integrating renewable energy into rural agricultural systems. Full article

As a decentralized energy management paradigm, micro-energy grid (MEG) clusters enable synergistic operation of heterogeneous distributed energy assets, particularly through multi-energy vector coupling mechanisms that enhance distributed energy resource (DER) utilization efficiency in next-generation power networks. While individual MEGs demonstrate limited capability in responding to upper-grid demands using surplus energy after fulfilling local supply/demand balance, coordinated cluster operation significantly enhances system-wide flexibility. This paper proposes a two-stage robust optimization model that systematically addresses both the synergistic complementarity of multi-MEG systems and renewable energy uncertainty. First, the basic operation structure of MEG, including distributed generation, cogeneration units, and other devices, is established, and the operation mode of the MEG cluster responding to host utility grid flexibly is proposed. Then, aiming to reduce operation expenses, an optimal self-scheduling plan is generated by establishing a MEG scheduling optimization model; on this basis, the flexibility response capability of the MEG is measured. Finally, to tackle the uncertainty issue of wind and photovoltaic power generation, the two-stage robust theory is employed, and the scheduling optimization model of MEG cluster flexibility response to the host utility grid is constructed. A southern MEG cluster is chosen for simulation to test the model and method’s effectiveness. Results indicate that the MEG cluster’s flexible response mechanism can utilize individual MEGs’ excess power generation to meet the host utility grid’s dispatching needs, thereby significantly lowering the host utility grid’s dispatching costs. Full article

Global agriculture remains dependent on nitrogen fertilizers produced through fossil fuel-based processes, contributing to greenhouse gas emissions, energy use, and supply chain vulnerabilities. This review introduces plasma-activated water (PAW) as a novel, electricity-driven alternative for sustainable nitrogen delivery. Generated by non-thermal plasma, PAW infuses water with reactive oxygen and nitrogen species, offering a clean, decentralized substitute for conventional synthetic fertilizers derived from the Haber–Bosch and Ostwald processes. It can be produced on-site using renewable energy, reducing transportation costs and depending on fertilizers. Beyond its fertilizer properties, PAW enhances seed germination, plant growth, stress tolerance, and pest resistance, making it a multifunctional input for controlled environment agriculture. We also assess PAW’s techno-economic viability, including energy requirements, production costs, and potential scalability through renewable energy. These factors are crucial for determining its feasibility in both industrial systems and localized agricultural applications. Finally, the review examines PAW’s contribution to the ten United Nations Sustainable Development Goals, particularly in climate action, clean energy, and sustainable food production. By combining agronomic performance with circular production and emissions reduction, PAW presents a promising path toward more resilient, low-impact, and self-sufficient agricultural systems. Full article

Ghana’s electricity grid remains heavily fossil-fuel dependent (69%), resulting in high costs and unstable low-voltage (LV) networks, exacerbating supply shortages. This study evaluates the technical and economic feasibility of converting the Obaa-Yaa LV substation in Drobo, Ghana, into a solar-powered microgrid. Using the forward–backward method for technical analysis and financial metrics (NPV, IRR, DPP, and PI), the results show that rooftop solar on seven households generates 676,742 kWh annually—exceeding local demand by 115.8 kW—with no voltage violations (240 V ± 6%) and minimal losses (9.24 kW). Economic viability is demonstrated via an NPV of GHS 2.1M, IRR of 17%, and a 10-year payback. The findings underscore solar microgrids as a pragmatic solution for Ghana’s energy challenges, urging policymakers to incentivize decentralized renewable systems. Full article

The energy sector underwent a significant transformation with increasing demand for efficiency, transparency, and sustainability. The traditional or conventional system often faces several challenges, such as inefficient energy trading, a lack of transparency in renewable energy generation verification, and complex regulatory guidelines that affect its widespread adoption. Thus, blockchain technology has emerged as a potential solution to overcome these challenges, as it is known for its transparent, secure, and decentralized nature. However, despite the promising application of blockchain, its integration into the energy supply chain (ESC) is underexplored. The purpose of this research is to analyze the potential applications of blockchain technology in ESC in order to enhance efficiency, transparency, and sustainability in energy systems. The aim is to investigate the integration of blockchain with emerging technologies (such as IoTs, smart contracts, and P2P energy trading) in order to optimize energy production, distribution, and consumption. Furthermore, by comparing different blockchain platforms (like Ethereum, Solana, Hedera, and Hyperledger Fabric), this study discusses the security and scalability challenges of using blockchain in energy systems. It also examines the practical use cases of blockchain for the tokenization of RECs, dynamic energy pricing, and P2P energy trading by providing the Energy Web Foundation and Power Ledger as real-world examples. The article concludes that blockchain technology has the potential to transform ESC by enabling decentralized energy trading, which subsequently enhances transparency in energy transactions and the verification of renewable energy generation. It also identifies smart contracts and tokenization of energy assets as key parameters for dynamic pricing models and efficient trading mechanisms. However, regulatory and scalability challenges remain significant obstacles to its widespread adoption. Finally, this study provides the basis for future advancement in the adoption of blockchain technology in ESC, which offers a valuable resource for industry professionals, regulating authorities, and researchers. Full article

With the continuous expansion of data centers, their carbon emission has become a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading, carbon capture, and power-to-gas technologies are promising emission reduction techniques which are, however, seldom applied to data centers. To bridge this gap, we propose a carbon-neutral architecture for distributed data centers, where each data center consists of three subsystems, i.e., an energy subsystem for energy supply, thermal subsystem for data center cooling, and carbon subsystem for carbon trading. Then, we formulate the energy management problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a distributed solution framework using Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Finally, simulations using real-world data show that a cost saving of 20.3% is provided. Full article

Various sectors focus on transitioning to clean and renewable energy sources, particularly airport microgrids (AMGs), which offer the potential for highly reliable and resilient operations. As airports increasingly integrate renewable energy sources, ensuring stable and efficient power becomes a critical challenge. In this context, maintaining proper frequency is essential for the reliable operation of AMGs, which helps maintain grid stability and reliable operation. This paper proposes a new hybrid disturbance observer-based controller with a fractional-order controller (DOBC/FOC) for operating AMGs with high levels of renewable energy integration and advanced frequency regulation (FR) capabilities. The proposed controller utilizes DOBC coupled with a non-integer FOC for load frequency control (LFC), optimized for peak performance under varying operational conditions. In addition, a decentralized control strategy is introduced to manage the participation of electric vehicles and lithium-ion battery systems within the airport’s energy ecosystem, enabling effective demand response and energy storage utilization. Furthermore, the parameters of these controllers are optimized simultaneously to ensure optimal performance in both transient and steady-state conditions. The proposed DOBC/FOC controller demonstrates strong performance and reliability according to simulation outcomes, showcasing its superior performance in maintaining frequency stability, reducing fluctuations, and ensuring continuous power supply in diverse operating scenarios, such as 55.5% and 76.5% in step load perturbations when compared to the utilization of electric vehicles (EVs) and electric aircraft (EAC), respectively. These results underline the potential of this approach in enhancing the resilience and sustainability of AMG and contributing to more intelligent and eco-friendly airport infrastructure. Full article

The decarbonization of the heat supply is crucial for the German energy transition. Integrating Power-to-Heat technologies like heat pumps (HPs) into district heating grids (DHGs) can support this process. The efficiency of HPs can be increased through temperature reduction in the DHG, though decentralized reheating may be required to supply sufficient heat for the end consumers. In order to investigate the associated trade-off, this study evaluates the economic, ecological, and technical effects of temperature reduction in DHGs using the software tool nPro. In a three-step process heat demand, the DHG design and operation are modeled. Three operating temperature scenarios are considered: 60 °C, 50 °C, and an ambient dependent flow temperature varying between 40 and 50 °C. As the temperatures decrease, the balance shifts between centrally produced HP heat and decentralized heat from instantaneous electric water heaters (IEWHs). The initial temperature reduction leads to reduced CO₂ emissions, primary energy demand, heat losses, and total annual cost (TAC). However, with a further reduction in the operating temperature, an increase in these parameters occurs. While the necessary cost and primary energy for central components decrease, an increase in the decentralized heat generation is necessary to properly supply the heat demand. This leads to higher TAC and CO₂ emissions overall. Full article

This paper introduces a groundbreaking framework for optimizing microgrid operations using the Quantum Approximate Optimization Algorithm (QAOA). The increasing integration of decentralized energy systems, characterized by their reliance on renewable energy sources, presents unique challenges, including the stochastic nature of energy supply-and-demand management. Our study leverages quantum computing to enhance the operational efficiency and resilience of microgrids, transcending the limitations of traditional computational methods. The proposed QAOA-based model formulates the microgrid scheduling problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, suitable for quantum computation. This approach not only accommodates complex operational constraints—such as energy conservation, peak load management, and cost efficiency—but also dynamically adapts to the variability inherent in renewable energy sources. By encoding these constraints into a quantum-friendly Hamiltonian, QAOA facilitates a parallel exploration of multiple potential solutions, enhancing the probability of reaching an optimal solution within a feasible time frame. We validate our model through a comprehensive simulation using real-world data from a microgrid equipped with photovoltaic systems, wind turbines, and energy storage units. The results demonstrate that QAOA outperforms conventional optimization techniques in terms of cost reduction, energy efficiency, and system reliability. Furthermore, our study explores the scalability of quantum algorithms in energy systems, providing insights into their potential to handle larger, more complex grid architectures as quantum technology advances. This research not only underscores the viability of quantum algorithms in real-world applications but also sets a precedent for future studies on the integration of quantum computing into energy management systems, paving the way for more sustainable, efficient, and resilient energy infrastructures. Full article

Traditional centralized water systems are facing sustainability challenges due to climate and socioeconomic changes, extreme weather events, and aging infrastructure and their uncertainties. The energy sector has addressed similar challenges using the microgrid approach, which involves decentralized energy sources and their supply, improving system resilience and sustainable energy supply. This study investigated the resilience effects of water microgrids, which feature operational interactions between centralized and local systems for sustainable water supply. A lab-scale water distribution model was tested to demonstrate centralized, decentralized, and microgrid water systems under the disruption scenarios of pump shutdown, pump rate manipulation, and pipe leaks/bursts. The water microgrids integrate centralized and local systems’ operations, while the decentralized system operates independently. Then, functionality-based resilience and its attributes were evaluated for each disruption scenario. The results reveal that, overall, the microgrid configuration, with increased water supply redundancy and flexible operational adjustment based on system conditions, showed higher resilience, robustness, and recovery rate and a lower loss rate across disruption scenarios. The resilience effect of water microgrids was more evident with longer and more severe disruptions. Considering global challenges in water security under climate and socioeconomic changes, the findings suggest insights into a hybrid water system as a strategy to enhance resilience and water use efficiency and provide adaptive operations for sustainable water supply. Full article

Smart microgrids (SMGs) have emerged as a key solution to enhance energy management and sustainability within decentralized energy systems. This paper presents SmartGrid AI, a platform integrating deep reinforcement learning (DRL) and neural networks to optimize energy consumption, predict demand, and facilitate peer-to-peer (P2P) energy trading. The platform dynamically adapts to real-time energy demand and supply fluctuations, achieving a 23% reduction in energy costs, a 40% decrease in grid dependency, and an 85% renewable energy utilization rate. Furthermore, AI-driven P2P trading mechanisms demonstrate that 18% of electricity consumption is handled through efficient decentralized exchanges. The integration of vehicle-to-home (V2H) technology allows electric vehicle (EV) batteries to store surplus renewable energy and supply 15% of household energy demand during peak hours. Real-time data from Saudi Arabia validated the system’s performance, highlighting its scalability and adaptability to diverse energy market conditions. The quantitative results suggest that SmartGrid AI is a revolutionary method of sustainable and cost-effective energy management in SMGs. Full article

The development of renewable energy is increasingly blurring the line between the energy and agricultural sectors. Decarbonizing agriculture is essential for the development of sustainable development principles. This can be achieved in essentially the two following ways: by reducing fuel consumption and by making the livestock sector more efficient. This review sets out options for contributing to these two elements. The review sets the stage for a smoother synergy process, whereby waste generated in agriculture is fully utilized to strengthen farms. In conducting the review, the methods of scientific induction and deduction were used. One of the key elements is the recycling of the waste generated into biomethane. This biomethane in turn is used as a fuel for tractors and as a means of providing energy for farms. The production of biomethane or biogas can lead to decentralization of the energy system, with farms becoming less or completely independent from external energy supplies. At the same time, synergies with other forms of energy are being created. These make it possible to increase the income of farms by adding a new activity of supplying energy to other consumers. Full article

The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven by advancements in artificial intelligence (AI), big data, and Internet of Things (IoT) technology, this marketplace enables efficient energy trading through real-time supply–demand matching with dynamic pricing. Decentralized energy systems, such as solar and wind power, offer benefits like enhanced energy security but also present challenges in balancing supply and demand due to volatility. This research develops and validates an AI-based pricing model to optimize regional energy consumption and incentivize efficient usage to support grid stability. Through a SWOT analysis, this study highlights the strengths, weaknesses, opportunities, and threats of such a platform. Findings indicate that, with scalability, the AI-driven marketplace could significantly support the energy transition by increasing renewable energy use and therefore reducing carbon emissions. This paper presents a viable, scalable solution for SMEs aiming to participate in a resilient, sustainable, and localized energy market. Full article