Search Results (235)

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

The increasing penetration of renewable energy sources (RES) introduces significant variability and instability in modern power systems, creating a growing need for advanced and coordinated energy storage solutions. However, a key unresolved challenge remains the integrated modeling and optimal sizing of hybrid energy storage systems (ESS) that combine technologies with different temporal characteristics under high RES penetration. This study addresses this challenge by developing a unified techno-economic and physical–mathematical framework for hybrid ESS integrating lithium-ion (Li-ion), vanadium redox flow batteries (VRFB), and hydrogen (H₂) technologies. Unlike conventional approaches that treat storage technologies independently or use simplified hybrid representations, the proposed framework jointly considers dynamic energy balance, degradation-aware lifecycle behavior, and multi-criteria cost optimization. The model was implemented using Python 3.10-based simulation tools and evaluated under renewable penetration scenarios of 30%, 50%, and 70%. The results indicate that increasing RES penetration leads to higher power fluctuations, reaching ±15–20% at 50% RES and ±20–25% at 70% RES. The optimized hybrid system achieves an overall efficiency of up to 92%, reduces total system cost to approximately 450 USD/kWh, and extends operational lifetime to 25 years, demonstrating a balanced techno-economic performance compared to standalone storage technologies. The proposed framework addresses this gap by coupling dynamic energy balance analysis with degradation-aware techno-economic optimization, enabling coordinated allocation of storage functions across short-, medium-, and long-duration timescales. In this way, the study not only evaluates hybrid storage performance, but also provides a practical decision-support framework for renewable-dominated power systems, particularly in the context of Kazakhstan’s energy transition. Full article

Voltage stability assessment of a transmission interface is carried out by continuation power flow (CPF) using a fixed post-contingency operating condition. However, if legacy renewable energy sources (RESs) in the sink area are tripped during or following a fault, the actual post-fault operating point can differ from that assumed in the CPF study. This paper examines the effect of sink-area RES tripping on transmission interface voltage stability. The shift in the post-fault operating point caused by the loss of sink-area active power injection is explained using a two-bus equivalent, and the effect of reactive power support from connected RES on the transfer limit is also discussed. The proposed analysis is verified using a modified SAVNW test system in PSS/E. Two contingency scenarios were studied by applying a three-phase fault at the receiving-end bus and tripping one transmission interface line at fault clearing. The results show that sink-area RES tripping moves the post-fault operating point toward the nose point and reduces the voltage stability margin. The results also show that reactive power support from connected RES increases the transfer limit and leads to a larger margin. These effects should be considered in voltage stability assessment of transmission interfaces with legacy RES. Full article

Energy transition includes the substitution of centralized energy systems with decentralized variable renewable energy sources (vRES), the growth of which brings drawbacks such as grid congestion and intermittency. These issues are increasingly troublesome in many local energy systems, including in The Netherlands. Biogas may provide options to provide backup renewable energy in times of energy supply uncertainty. In The Netherlands, the consideration of biogas in such functions is limited. Meanwhile, local energy initiatives (LEIs) are spearheading the adoption of vRES. Because of concern over local grid balancing, LEIs may want or need to innovate and diversify their activities. Such innovation could include bioenergy in general, and biogas specifically. However, only a small number of LEIs consider bioenergy, and Dutch LEIs seem hesitant to venture into biogas specifically. In this paper we explore the question of what hinders adoption of biogas in The Netherlands in general, and by LEIs specifically, deploying an approach based on the technological innovation systems (TIS) concept. In that approach, we take insights from current and expected policy in The Netherlands juxtaposed with insights from similar countries surrounding The Netherlands. We conclude that historic developments in biogas already created a moderately supportive platform for large-scale biogas development, but some essential factors remain inadequately developed. Key barriers to biogas innovation, especially for LEIs, are insufficient mobilization of financial and knowledge resources, and insufficient attention to alleviating preconceptions. Dependable support and attention for socio-economic factors in policymaking would improve conditions associated with resources, preconceptions and resistance, and the situation for LEIs to explore the potential of biogas. However, it remains uncertain whether such measures would be sufficient to improve the potential of local biogas utilization in The Netherlands in a way that opens a role for biogas in solving energy transition challenges such as energy system balancing. Full article

The relationship between economic development and environmental degradation in Brazil was studied over the period 1970–2022, using ecological footprint (EF) as an environmental indicator. A contribution to the scientific literature exists here because biomass energy (BIO) has been separated from other types of renewable energy sources, and environmental policy stringency (EPS) and energy depletion (END) have been simultaneously analyzed for their joint impacts on EF in Brazil. In this research, four hypotheses were formulated for the relationships of: GDP, BIO, EPS, RE, and END with EF. The ARDL method was used in this analysis due to the different orders of integration for some of the variables and sample size limitations, both of which make alternative cointegration techniques inappropriate. All four hypotheses were supported in the empirical estimates of this study. In the long run, increases in GDP will result in increased EF, decreases in BIO and EPS will decrease EF, and no long-run relationship exists between RE and EF. However, RE has a short-term rebound effect. Increases in END will increase EF, indicating the environmental costs associated with the extraction and consumption of non-renewable resources. The statistically significant error correction term also supports the idea that there will be a quick adjustment towards the long-run equilibrium. The implications of these results suggest that Brazil continues to operate within a stage of growth driven primarily by scale rather than intensity, yet well-regulated biomass energy and strict environmental regulations provide a pathway for achieving decoupling in alignment with SDG 13 and SDG 15. 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

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factors like wind direction and speed. This variability poses significant challenges to the real-time power balance and control of the PS. To address the uncertainties in system operation and the challenges of RES consumption, this paper proposes an artificial intelligence (AI) algorithm-driven collaborative optimization strategy for virtual power plants (VPPs) considering RESs transmitted by flexible HVDC. Firstly, a self-attention mechanism and multiple gated structures are integrated into a long short-term memory (LSTM) deep learning model. This enhancement improves the model’s ability to capture multi-timescale characteristics of RESs, increasing forecasting accuracy and robustness. Based on these forecasts, a total cost optimization model for VPP operation is developed, which includes high penalty costs for wind and solar curtailment. By embedding economic constraints that prioritize RESs usage, the model can reduce waste caused by traditional cost-driven scheduling. Additionally, to solve the high-dimensional nonlinear optimization problem in VPP scheduling, an improved population-based incremental learning (PBIL) algorithm is introduced. It incorporates an elite retention strategy and an adaptive mutation operator to boost global search efficiency and convergence speed. Simulations based on an VPP incorporating typical offshore wind and solar RESs transmitted via flexible HVDC demonstrate that the improved LSTM reduces MAPE by 7.14% for wind and 4.27% for PV compared to classical LSTM, and the proposed method achieves the lowest curtailment rates (wind 10.74%, PV 10.23%) and total cost (43,752 RMB), outperforming GA, PSO, and GW by 10–18% in cost reduction. Simulation results show that the proposed strategy enhances RESs consumption while maintaining system economy under flexible HVDC transmission. This work offers theoretical and practical insights for optimizing PS with high RES penetration and supports the low-carbon transition of new-type PS. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

This study benchmarks renewable energy source (RES) utilization in the Visegrad Four (V4) against the EU average using Eurostat data for 2014–2022. A multi-layer framework was used to combine technology-specific per-capita indicators, sectoral RES shares, cluster analysis, and panel regression with fixed effects. The EU substantially exceeds V4 in hydropower (774.06 vs. 270.19 kWh/person), wind (972.06 vs. 161.30 kWh/person), and solar technologies. The electricity-sector gap is most pronounced (EU 41.17% vs. V4 18.69%). Paired t-tests confirmed a statistically significant persistent gap (t(8) = −20.78; p < 0.001), consistent with delayed convergence. Cluster analysis assigned all V4 countries to a single moderate-RES tier, structurally separated from Western and Nordic clusters; panel regression confirmed that the V4 coefficient was robustly negative (β = −5.783 to −9.088 pp) even after policy controls, with fossil lock-in (β = −2.404 pp) emerging as the most consistent structural determinant, whereas V4 × fossil lock-in interaction was positive (β = +2.558 pp), suggesting partial mitigation through differentiated pathways. Intra-V4 heterogeneity—Slovakia’s hydropower lock-in, Hungary’s wind prohibition, Poland’s coal dependency, and Czech Republic’s curtailed feed-in tariff—argues against homogeneous policy responses; results support technology-specific strategies (wind/solar PV in Poland/Czech Republic; solar thermal/heat pumps in Hungary/Slovakia) and grid modernisation as cross-cutting priority. Full article

The energy transition of small and medium-sized enterprises (SMEs) is an important element in achieving climate and energy goals, but its pace and scope remain varied. Previous studies have focused mainly on regulatory pressure, energy costs and financial barriers, while the importance of market factors has been analysed relatively rarely. The aim of this article is to assess consumer expectations perceived by enterprises as a factor that influences SMEs’ energy transition strategies. While the approach demonstrated by previous authors concentrated mostly on energy transition as a policy issue or an adjustment to legal changes, the originality and contribution of this paper lies in approaching this problem as one of a strategic adjustment to customers’ changing expectations. The study is based on a CATI survey of 417 Polish SMEs, predominantly micro- and small enterprises. The study covers the perception of customer expectations regarding energy efficiency, the use of renewable energy sources (RES) and environmental communication tools. Relationships were identified between perceived market signals and the energy-related actions of enterprises. The results indicate that SMEs perceive consumer expectations primarily as specific and quantifiable energy measures, such as reducing energy consumption and implementing renewable energy sources, while attaching less importance to formal reporting and certification tools. The energy transition is selective and incremental, focusing on solutions with low barriers to entry and short payback periods. From an energy policy perspective, the results suggest a need to design support instruments that are better aligned with how SMEs interpret market expectations. Full article

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article

The integration of renewable energy sources (RESs) into electric power systems introduces new challenges for system operation, reliability, and emergency management. Causal analysis, as a powerful data analysis tool, can reveal the interactions and influences between components in the power system, thus supporting the design, operation and optimization of the system. This review examines causal analysis methods applied to electric power systems with high-RES penetration, highlighting their effectiveness in identifying interactions among system components, detecting potential risks, and supporting operational decision-making. Key system properties, including safety, efficiency, flexibility, survivability, and reliability, are discussed in the context of high renewable integration. The review also analyzes lessons from systemic accidents and explores strategies to mitigate risks associated with excessive RES penetration. Finally, directions for future research are outlined, emphasizing real-time monitoring, advanced causal modeling, and methods to enhance the resilience of modern power systems. Full article

The rapid integration of inverter-based renewable energy sources (RES), particularly solar photovoltaic (PV) and wind power plants (WPPs), together with the large-scale deployment of battery energy storage systems (BESSs) is fundamentally reshaping modern power systems. While these technologies are essential for decarbonization, their converter-dominated and variable characteristics introduce new challenges for grid stability, operational security, and regulatory compliance. As a result, grid codes are being continuously revised to define advanced technical requirements, including fault ride-through (FRT) capability, reactive power support, frequency response, voltage control, and active power management for RESs and energy storage systems (ESS). This study presents a systematic comparative assessment of international grid codes, examining the technical and operational requirements imposed on inverter-based resources (IBR) and ESSs across multiple jurisdictions. In parallel, the current Turkish Grid Code is evaluated from a future-oriented perspective, and recommendations that can improve the existing regulatory framework are proposed, particularly regarding high-voltage ride-through capability, synthetic inertia provision, fast frequency response (FFR), hybrid power plant (HPP) coordination, and ESS-specific performance criteria. Based on the comparative analysis, the study proposes targeted amendments to the Turkish Grid Code aimed at enhancing system resilience under high renewable penetration levels. Furthermore, field-testing methodologies, model-based validation practices, and emerging digitalized compliance monitoring architectures are investigated to assess their applicability to next-generation power systems. By integrating international best practices with country-specific recommendations, this work contributes to the development of transparent, adaptive, and technically robust grid code compliance frameworks, supporting both academic research and practical grid modernization efforts. Full article