Search Results (796)

This study investigates the relationship between renewable energy (RE) expansion and economic diversification in Egypt over 1990–2023 using a nonlinear autoregressive distributed lag (NARDL) framework. Egypt’s fossil fuel share stands at approximately 93% of primary energy supply, yet the country has committed to a 42% renewable electricity target by 2035. Despite quadrupling utility-scale RE capacity from 2.8 GW to 11.2 GW between 2015 and 2023, the Economic Diversification Index (EDI) has remained broadly stagnant. The bounds test confirms long-run cointegration (F = 6.760), exceeding small-sample critical values at the 1% level. Long-run estimates reveal that positive RE shocks are associated with lower diversification (θ⁺ = −0.571, p = 0.035) and negative shocks exhibit a statistically similar adverse effect (θ⁻ = −0.271, p = 0.024). Oil rents exhibit a positive long-run association (β = 0.145, p = 0.003). The error-correction term (−0.569) indicates approximately 57% annual adjustment. The Wald test provides marginal evidence against long-run symmetry (F = 2.999, p = 0.097). To complement the Granger causality analysis and address small-sample concerns, we additionally implement the Toda and Yamamoto augmented VAR procedure, which confirms robust unidirectional temporal precedence from LRE to LEDI (χ² = 23.48, p < 0.001) without reverse feedback (χ² = 2.25, p = 0.133). These patterns are interpreted through the lens of the Green Dependency Paradox—a conceptually distinct framework characterized by three mechanisms absent from classical resource curse theory: technology-mediated capital flight, procurement-induced deindustrialization, and policy-reversible lock-in operating under conditions of high import content, absent local content mandates, and fragmented industrial policy coordination. A tri-phase, evidence-grounded policy framework is proposed. All findings are explicitly conditional on Egypt’s current institutional context. Full article

Energy-intensive industries deploying hybrid renewable energy systems require performance monitoring frameworks that evolve with phased system implementation. This paper introduces the performance and sustainability framework, a simulation-grounded evolution of the sustainability balanced scorecard for longitudinal assessment of renewable energy infrastructure. The framework requires that key performance indicators derive from validated techno-economic simulations, that assessment is repeated at temporal checkpoints corresponding to physical system changes, and that each balanced scorecard perspective includes at least one environmental or circular-economy indicator. The framework is demonstrated in a cheese manufacturing facility in Crete, Greece, where a 38 kW cheese whey biomass generator, 72.2 kW photovoltaic system, and 10 kW wind turbine are deployed over five years. Annual HOMER Pro re-simulations are combined with weighted SWOT scoring to track technical, economic, environmental, and organisational performance. By Year 5, the system achieves an 88.7% electrical renewable fraction, 60.0% gross-operational CO₂-eq reduction, 0.1148 EUR/kWh levelised cost of energy, and 22.3% internal rate of return. The longitudinal trajectory also reveals declining delivered thermal renewable contribution and cheese whey utilisation, exposing operational trade-offs that single-point scorecard assessments would miss. Applicability of the PSF to community-scale governance under ISO 37101:2016 and to renewable energy communities under Directive (EU) 2018/2001 is examined exclusively as a conceptual scaling framework for future research. The present empirical demonstration is restricted to a single-facility case study, and no community-level stakeholder data are collected or analysed. Full article

Renewable energy sources (RESs) in microgrids are vital for sustainable and resilient power networks, especially in rural South Africa with diverse climatic conditions. Photovoltaic (PV) energy generation is intermittent, making it difficult to offer reliable electricity in varying conditions. The Proton Exchange Membrane Fuel Cell (PEMFC) and solar system are integrated in this study to provide a sustainable energy source that can address these issues. Under varying temperature and irradiance conditions, the PV system was evaluated with and without an LCL filter and PEMFC unit using PVGIS Northern Cape daily solar irradiation data. Results show that solar input variability causes large voltage fluctuations in the standalone PV system. This study adds to the expanding knowledge on RES resilience by utilising real-world climatic data and shows that PV-FC systems can be a sustainable and reliable option for microgrid and standalone applications in rural locations with ample resources or without electrical infrastructure. Full article

The increased penetration of renewable energy sources (RES) in the electrical grid has necessitated the concept of a Virtual Synchronous Generator (VSG) control which is used to make grid-connected power electronic converters behave as synchronous generators. While VSG controls are suitable for supporting the inertia of a microgrid, their use leads to grid instability in the event of a disturbance. This research addresses this limitation by integrating a fully connected Feedforward Neural Network (FCNN) into a VSG control to dynamically adjust the damping coefficient and inertia constant in real time. This approach could enhance system stability by reducing frequency and active power oscillations during grid disturbances, particularly during partial load rejection. To evaluate the effectiveness of the proposed method, a supervised learning-based FCNN was trained on VSG damping behavior under various grid disturbances. The trained model was then implemented in a simulation environment to regulate the VSG parameters dynamically. Simulation results show the neural network-based approach reduces high overshoots at the point of disturbance in active power and frequency oscillations; however, the VSG signal settles faster after the grid disturbance. These findings highlight the potential of machine learning in enhancing the stability of VSG-based microgrids, offering a computationally efficient solution for improving transient response and power-sharing performance. Full article

The continuous increase in Renewable Energy Sources (RES) in Greece’s electricity system has led to growing energy curtailment due to limited grid capacity, especially in high-production regions. According to recent data, more than 200 GWh of clean energy was curtailed in a single quarter in 2024, highlighting the urgent need for effective storage solutions. Curtailment represents a growing system level challenge, but it also creates an opportunity to convert surplus renewable electricity into green hydrogen through electrolysis. This study quantifies the hydrogen production potential of curtailed RES electricity in four Greek regions, Peloponnese, Crete, Thrace, and Western Macedonia, and evaluates alternative storage pathways under harmonized techno-economic assumptions. A scenario-based framework is developed using regional RES capacity, curtailment estimates, electrolyzer efficiency, hydrogen conversion factors, and indicative storage cost ranges. The analysis compares pressurized tank storage, underground storage, and hybrid configurations, while also estimating avoided CO₂ emissions from the substitution of grey hydrogen. The results indicate substantial regional variation. The Peloponnese exhibits the highest annual hydrogen potential, followed by Crete, Thrace, and Western Macedonia, while each region presents different infrastructure constraints and deployment roles. Mainland regions with access to geological storage show lower indicative hydrogen costs than island systems, where storage and export constraints increase costs. The findings show that curtailed renewable electricity can function as a low-carbon feedstock for hydrogen production in Greece, supporting grid flexibility, regional decarbonization, and the gradual development of hydrogen hubs under differentiated regional strategies. Full article

This paper examines the relationship between macroeconomic scale, the structure of energy consumption, and carbon dioxide emissions in Poland over the period 2000–2023, against the background of the country’s energy transition under European Union (EU) climate policy. The study aims to identify the extent to which gross domestic product (GDP), hard coal consumption, natural gas consumption, and electricity generation from renewable energy sources (RES) explain the level of CO₂ emissions in a coal-dependent economy undergoing gradual structural change. The empirical analysis is based on annual data from Statistics Poland and applies two complementary econometric approaches: an Ordinary Least Squares (OLS) model to capture the baseline relationships and an Autoregressive Distributed Lag (ARDL) model to examine short-run dynamics and lagged effects. The OLS results show that the model explains a substantial share of emission variability and that coal consumption is the only statistically significant determinant of CO₂ emissions, with a strong positive coefficient. GDP, natural gas consumption, and RES production do not exhibit statistically significant effects in the baseline specification. The ARDL results indicate that coal has the strongest contemporaneous statistical association with emissions, while also suggesting weak autoregressive properties of the emission system and the absence of statistically significant short-run associations for GDP, gas, and renewables. Sensitivity analysis further shows that coal remains the variable most strongly associated with emission levels, whereas the estimated associations for GDP, gas, and RES are comparatively weak. The findings suggest that, in Poland, emission dynamics are more closely linked to the carbon intensity of the energy mix than to the scale of economic activity itself. The study suggests that effective decarbonization is likely to be associated with a structural reduction in coal dependence, while the emission-reduction potential of renewable energy expansion may become more visible over a longer time horizon. These results have important implications for the design of Poland’s energy and climate policy, suggesting that the success of the transition is closely linked to changes in the structure of energy carriers in a way consistent with economic and infrastructural constraints. Full article

The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert a form of primary energy into electrical energy. Primary energy comes from a number of sources, such as fossil fuels, nuclear energy, hydropower, wind, and solar. The carbon neutrality targets set by the European Union and several countries around the world have driven a transformation characterized by the gradual replacement of synchronous thermal generation based on fossil fuels with Renewable Energy Sources (RES), such as wind and solar. The energy transition, while necessary to achieve the established targets, introduces significant challenges to the stability of Electrical Power Systems (EPS) and electrical grids, since RES do not yet contribute to stability at levels comparable to the generating units of large thermal power plants, whether in terms of inertia, which has seen a notable reduction in recent years, or in voltage control or short-circuit power. This article presents and discusses solutions to mitigate the effect of this reduction in inertia in power plants using synchronous compensators and synthetic inertia emulation using battery storage. Full article

The accelerating adoption of electric vehicles (EVs) alongside renewable distributed generators (RE-DGs), particularly solar photovoltaic (PV) and wind-based systems, is reshaping the operational and planning paradigms of modern power distribution networks. In this study, an optimal allocation framework is developed for the simultaneous integration of EV charging stations (EVCSs) and RE-DGs within a looped configuration of the IEEE 33-bus distribution system. Two advanced metaheuristic techniques—Improved Grey Wolf Optimizer (IGWO) and Metaheuristic COOT-Based Optimization (MCBO)—are employed to determine the optimal siting and sizing of these resources. The optimization objectives focus on minimizing active power losses while enhancing voltage stability and reducing overall voltage deviation across the network. Simulation results reveal that the MCBO algorithm demonstrates superior performance, yielding a maximum reduction of 82.49% in active power losses with the integration of standalone PV, and 78.14% when PV is deployed in conjunction with EVCSs. Similarly, wind turbine generator (WTG) integration resulted in a loss reduction of 85.74% without EVCSs and 81.57% with EVCS integration using the same approach. The findings further indicate that looped network configurations consistently outperform traditional radial systems in both loss reduction and voltage profile enhancement, underscoring their suitability for accommodating future EV and renewable energy penetrations in smart distribution grids. Full article

Self-contained local forecasting of wind and solar series can improve operational planning of wind farms and photovoltaic (PV) plant day-cycles in addition to numerical models, which are mostly behind time due to high simulation costs. Unstable electricity production requires balancing the availability of renewable energy (RE) with unpredictable user consumption to achieve effective usage. Artificial intelligence (AI) predictive modelling can minimise the intermittent uncertainty in wind and solar resources by trying to eliminate specific problems in RE-detached system reliability and optimal utilisation. The proposed 24 h day-training and prediction scheme comprises the starting detection and the following similarity re-assessment of sampling day-series intervals. Two-point professional weather stations record standard meteorological variables, of which the most relevant are selected as optimal model inputs. Automatic two-layer altitude observation captures key relationships between hill- and lowland-level data, which comply with pattern progress. New biologically inspired differential learning (DfL) is designed and developed to integrate adaptive neurocomputing (evolving node tree components) with customised numerical procedures of operator calculus (OC) based on derivative transforms. DfL enables the representation of uncertain dynamics related to local weather patterns. Angular and frequency data (wind azimuth, temperature, irradiation) are processed together with the amplitudes to solve simple 2-variable partial differential equations (PDEs) in binomial nodes. Differentiated data provide the fruitful information necessary to model upcoming changes in mid-term day horizons. Additional PDE components in periodic form improve the modelling of hidden complex patterns in cycle data. The DfL efficiency was proved in statistical experiments, compared to a variety of elaborated AI techniques, enhanced by selective difference input preprocessing. Successful LSTM-deep and stochastic tree learning shows little inferior model performances, notably in day-ahead estimation of chaotic 24 h wind series, and slightly better approximation of alterative 8 h solar cycles. Free parametric C++ software with the applied archive data is available for additional comparative and reproducible experiments. Full article

Agricultural residues and agro-waste are increasingly recognized as valuable reinforcements for sustainable composite materials. Natural fibers derived from these biomasses offer biodegradability, low density, renewability, and potential environmental benefits. However, their performance and sustainability depend strongly on extraction, surface treatment, and processing conditions. Therefore, evaluating the environmental emissions associated with natural fiber biocomposites is essential before claiming sustainability advantages. In this research, flax, jute, kenaf, and bagasse fibers were extracted and treated using an eco-friendly sodium bicarbonate solution, then incorporated into polylactic acid (PLA) matrix to fabricate biocomposites via injection molding. A life cycle assessment (LCA) was conducted using the ReCiPe midpoint (H) method, with a functional unit defined as “per kg” of manufactured biocomposite. The results revealed that jute fiber composites generated the highest emissions across several impact categories, including climate change (1.290 × 10¹ kg CO₂-Eq), terrestrial ecotoxicity (6.327 × 10¹ kg 1,4-DCB-Eq), human toxicity: carcinogenic effects (1.923 kg 1,4-DCB-Eq), and fossil resource use (3.202 kg oil-Eq). Jute also showed a 3.6% increase in terrestrial ecotoxicity and a 19.5% increase in land compared to flax, although it exhibited a 6.5% lower impact related to bagasse. A ±20% electricity-consumption sensitivity analysis further highlighted the dependence of environmental impacts on processing energy demand. Full article

The increasing demand for energy, rising electricity costs, and the growing need to reduce carbon emissions have driven industries toward the adoption of Renewable Energy Sources (RES) and Energy Storage Systems (ESS). However, selecting the most suitable ESS for industrial peak-shaving applications remains a complex decision involving technical, economic, and operational considerations. This paper proposes a practical and structured methodology for ESS selection that integrates conventional performance criteria with Industry 5.0 (I5.0) requirements, emphasizing sustainability, resilience, and human-centric industrial operation. Unlike existing multi-criteria decision-making approaches, the proposed framework reduces reliance on expert-based weighting, improving transparency and reproducibility. The methodology is implemented in two stages: initial KPI-based shortlisting of technologies, followed by detailed comparative performance analysis. A case study conducted in a European tire manufacturing plant compares lithium-ion batteries and flywheel energy storage systems under different peak-shaving strategies. Lithium-ion batteries demonstrated superior performance, covering approximately 80% of demand peaks compared with the 73% achieved by the flywheel system, confirming the effectiveness of the proposed methodology for practical industrial ESS selection. Full article

Electrified railways are major industrial electricity consumers. The Grid-Source-Storage-Vehicle (GSSV) system supports a more sustainable railway power supply by improving local renewable energy utilization, strengthening multi-source energy coordination, and promoting low-carbon development. However, existing rule-based energy management strategies (EMS) remain limited in their ability to support the efficient coordinated operation of the GSSV system. Moreover, under strong source-load fluctuations, conventional optimization-based EMS often fail to provide sufficiently reliable and responsive decision-making for real-time operation of GSSV systems. To address these issues, this paper proposes a real-time EMS based on a rule-guided enhanced non-dominated sorting genetic algorithm (RG-NSGA-II). First, based on the GSSV architecture, the operating modes of the system under different working conditions are systematically analyzed, and a corresponding rule-based EMS is designed. Then, a multi-objective optimization model considering system economic performance and grid power-intake fluctuation is formulated. Furthermore, a coordination mechanism between the rule-based EMS and the optimization EMS is developed. By embedding power commands generated by the rule-based EMS into the optimization EMS and regulating their activation through a time threshold, the proposed method improves the reliability, economic efficiency, and real-time performance of the EMS. Finally, the proposed method is validated, and the results show that the proposed real-time EMS ensures effective utilization of RE, improves power coordination efficiency and operational adaptability under fluctuating operating conditions, and delivers tangible environmental and economic sustainability benefits for electrified railway power supply systems. Full article

The growing challenge for electricity in South Africa is placing pressure on the country’s current electricity-generating capacity. Moreover, conventional power plants are the main source of high concentrations of greenhouse gases in the country. South Africa is the seventh-largest producer of coal globally, and coal takes the largest share in the generation of electricity, with significant negative environmental impacts. There is insufficient electricity grid infrastructure, which prevents remote areas from receiving electricity from the centralized power grid. South Africa has promise in adopting sustainable energy systems such as biomass, hydropower, wind, and solar energy. The country obtains 2500 h of sunshine per year, and the radiation content is 4–6 kWh/m². Solar and wind have significant potential, while biomass and hydropower have less potential. However, some challenges and limitations that affect the use of RE have been identified. Increasing offshore wind and solar energy will enable South Africa to attain its target of increasing the percentage of renewable energy in the energy mix from 11% to 41% by 2030. The diversification of production and reduction in greenhouse gas emissions require South Africa to actively modernize its transmission infrastructure and speed up the approval process of projects. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

Man-made climate change is empirically proven and places ethical and strategic responsibility on the current generation to mitigate risks for future generations. Within this context, the selection of future energy carriers is a central determinant of sustainable development. While electrification is widely promoted, particularly in the transport sector, it is associated with complex production chains, critical raw material dependencies, unresolved recycling challenges, and potential resource scarcity. Synthetic fuels therefore re-emerge as a potential complementary option, especially for applications that are difficult to electrify directly. However, their role remains controversial due to efficiency losses and cost challenges. This paper uses qualitative research based on expert interviews to investigate the role of synthetic fuels in the sustainable management of energy transition and responsible practices. A total of 11 experts, representing the energy sector, research institutions, engineering fields, environmental organizations, and political–regulatory contexts participated. The analysis focused on four dimensions—efficiency, awareness, knowledge, and acceptance. The findings have shown that synthetic fuels are not a universal substitute for fossil fuels but a highly conditional option for hard-to-electrify applications. Efficiency losses, limited renewable electricity availability, knowledge gaps, conceptual ambiguity, and acceptance challenges significantly constrain their systemic role. The paper concludes that synthetic fuels can only make a meaningful contribution under strict conditions, with clear prioritization, realistic expectations, and coherent long-term policy frameworks aligned with intergenerational responsibility and genuine sustainability. The findings should be interpreted primarily within the German and European policy and innovation context, as the expert sample is largely embedded in institutions operating in this environment. Nevertheless, the insights provide relevant indications for broader international debates on the role of synthetic fuels in energy transition. Full article