Search Results (1,115)

With the increasing penetration of distributed renewable energy, coordinated operation between distribution networks and multiple integrated energy microgrids (IEMs) has become increasingly important for improving operational economy and maintaining system security. To address the insufficient integration of network constraints, P2P energy sharing, and contribution-based benefit allocation, this paper proposes a hybrid game-based optimal scheduling model for multi-IEM systems under distribution network constraints. In the upper level, a Stackelberg game is established between the distribution system operator (DSO) and the multi-IEM alliance to coordinate electricity trading and distribution network operation. In the lower level, an asymmetric Nash bargaining-based cooperative game is developed to enable peer-to-peer (P2P) energy sharing and allocate cooperative benefits according to the actual energy-sharing contributions of individual IEMs. Furthermore, a distributed solution framework combining the Success-History Adaptive Differential Evolution (SHADE) algorithm and an improved alternating direction method of multipliers (ADMM) is adopted to preserve data privacy and improve computational efficiency. Case studies based on the modified IEEE 33-bus distribution system demonstrate that the proposed method eliminates voltage violations and reduces network losses by 9.0%. Meanwhile, the proposed cooperative mechanism decreases the total operating cost of the IEM alliance by 7815.8 CNY and yields a more contribution-consistent profit allocation among participating microgrids. In addition, the improved ADMM reduces computation time by 42.1% compared with the conventional serial ADMM. The results demonstrate the effectiveness of the proposed method in enhancing distribution network security, promoting renewable energy sharing, and improving the economic performance of multi-IEM systems. Full article

The rapid expansion of wind and solar generation has significantly increased the share of variable renewable energy in power systems worldwide, introducing new operational challenges. Among these, the simultaneous growth of renewable energy curtailment and persistent blackout risk reveals structural limitations in energy planning and system flexibility. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol to examine how the scientific literature has addressed the relationship between curtailment, energy security, and optimization strategies in high-renewable power systems. A total of 53 Q1-indexed articles published between 2021 and 2025 were analyzed using bibliometric and qualitative content analysis techniques. The results indicate that curtailment should not be interpreted solely as an operational inefficiency but rather as a potential flexibility asset when integrated with energy storage systems, power-to-X technologies, demand-side management, and stochastic optimization frameworks. The findings also highlight a shift from deterministic planning approaches toward robust and distributionally aware models capable of managing renewable uncertainty. Despite significant advances, geographic imbalances in case studies and limited integration between regulatory mechanisms and technical optimization remain key research gaps. This review contributes by synthesizing mitigation strategies into a structured flexibility framework and by outlining research directions for enhancing reliability in renewable-dominated systems. Full article

In the context of SDG 7 and SDG 13 of the 2030 Sustainable Development Agenda, a new performance indicator has started to gain momentum in scientific research: renewable energy productivity. Understanding the drivers and the challenges of green energy productivity could help add on to the classical focus of renewable energy research on infrastructure, technical and economic feasibility, and environmental and social impacts, by considering the performance indicators in this field more. Only very few studies have explored the influencing factors of renewable energy productivity. Thus, this research aims to reveal the impact of social, economic, energy, and environmental variables on green energy productivity. The methodological approach involves bibliometric analyses of the literature on green energy productivity (GEP) and panel data regression models involving 16 independent variables. The main findings indicate positive effects of green taxes, female participation in the workforce, and highly educated people on GEP, pointing out the importance of green taxation, education, and gender equality in sustainable development. On the other hand, negative relationships of green energy productivity with economic growth, traditional energy variables, and air pollution were found for the European Union’s member states over 2007 and 2023. The results suggest that the analyzed European countries based their economic growth on traditional resources, with less importance given to renewable resources and green technologies, as the share of renewable resources of GDP was also negatively correlated. While private financial resources increase green energy productivity, questions about research and development investments, urbanization, and diversity index are still debatable. Full article

The growing share of intermittent renewable electricity has increased the need for long-duration storage in industrial energy systems. Meanwhile, many industrial processes still release recoverable low-grade waste heat. Introducing this heat into pumped thermal energy storage (PTES) can improve thermal integration, but industrial waste heat is often unsteady, and its temperature and mass flow fluctuations may disturb the charging process. This study investigates an air-based reverse Brayton PTES system assisted by an industrial hot-water waste heat stream of approximately 100 °C. A dynamic model was developed in Simulink/Simscape. The shaft speed is fixed at 3000 rpm, and a PID controller regulates the molten-salt flow rate to maintain the thermal storage temperature. The results show that increasing the waste heat temperature from 95 °C to 105 °C mainly changes the charging-side heat distribution. The waste heat utilization power increases from 36.0 MW to 37.9 MW, while the regenerator power decreases from 126.8 MW to 122.0 MW. The thermal storage power increases slightly from 117.0 MW to 119.0 MW, with the mechanical input fixed at 81.0 MW. The influence of waste heat temperature is concentrated near the low-temperature heat exchanger, regenerator, and turbine outlet. Under dynamic disturbances, faster temperature ramps increase short-term deviations, but the PID-based molten-salt flow regulation keeps the storage temperature close to 550 °C, indicating that the proposed control strategy can suppress moderate thermal disturbances during charging. When waste heat temperature and mass flow rate vary together, same-direction changes strengthen the disturbance, whereas opposite-direction changes partly offset it. These results clarify the disturbance propagation mechanism of fluctuating industrial waste heat in the PTES charging loop and provide a basis for the dynamic design and temperature-control strategy of waste-heat-assisted PTES systems. Full article

As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 ± 0.0086 to 0.0048 ± 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder. Full article

Hybrid renewable energy systems are regarded as one of the most promising solutions for the autonomous power supply of remote and weakly electrified sites, where diesel generation remains a costly and carbon-intensive energy source. This study presents the optimization of an off-grid PV/BESS/DGU microgrid for three representative regions of Kazakhstan—North, Central/East, and South/South-West—under different environmental scenarios. The aim of the study was to determine the optimal installed photovoltaic capacity, battery storage capacity, diesel generator rated power, and annual load coverage balance using the Generalized Reduced Gradient (GRG) method. The optimization was carried out using two objective functions: the conventional levelized cost of electricity, $LCOE$, and the environmentally adjusted cost of electricity, ${LCOE}_{\mathrm{e}\mathrm{n}\mathrm{v}}$, which includes the monetized cost of emissions associated with diesel generator operation. The model was formulated as a constrained nonlinear programming problem incorporating hourly energy balance, battery state-of-charge constraints, diesel generator operating constraints, and carbon price scenarios of 0, 25, 50, and 100 USD/tCO₂. The results show that an increase in the carbon price systematically shifts the optimum toward a higher share of photovoltaic generation and reduced diesel generator use in all regions. The strongest response is observed in the South/South-West region, followed by Central/East, whereas the North exhibits the lowest sensitivity due to the more pronounced seasonality of solar generation. Under the considered scenarios, the optimal PV capacity increases by approximately 24–28%, while the share of diesel generation in annual load coverage decreases by approximately 28% in the North, 44% in Central/East, and 61% in the South/South-West. At the same time, the rated diesel generator capacity remains unchanged in most scenarios, indicating the persistence of its backup function. The results confirm that the PV/BESS/DGU configuration constitutes a technically and economically justified baseline architecture for autonomous power supply under Kazakhstan’s conditions, while the inclusion of environmental costs supports the cost-effective displacement of diesel generation. The GRG method proved to be suitable for the transparent and efficient optimization of hybrid microgrid parameters. Full article

Climate change is a global pressing concern that has affected all sectors, including the operations of Small and Medium Entreprises (SMEs) in developing countries, including Malawi. This has negatively affected their economies of scale and exacerbated the SMEs’ growth constraints. Nonetheless, renewable efficient energy (REE) systems, including solar and biogas, could help in building resilience to sustain their performance. In line with this, the study examined the factors that enhance the adoption of renewable efficient energies and constructed their resilience indices. Our study was grounded in the Diffusion of Innovation Theory and the Sustainable Livelihoods Framework. These theories contextualised the study and guided the selection of variables to estimate an Endogenous Switching Regression (ESR) econometric model, alongside estimating the absorptive, adaptive and transformative individual indices for 699 SMEs, using the 2019 Malawi Household Integrated Survey data. The results initially suggests that factors such as access to credit, being male, access to education, access to capital sources, a large profit share, bridging social capital and location among others, have a positive effect in influencing the adoption of renewable efficient systems. We simulated the adoption results and found that SMEs that adopts REE increase their resilience with an Average Treatment Effect of 0.117 and through the subsidy policy effect vulnerable SMEs that later adopt REE would shift their resilience by 0.169. Furthermore, the study found that transformative capacity plays the most important role in building long-term resilience for the SMEs. The study calls for policies, including establishing urban centres where SMEs can access information regarding REE and improving access to formal safety nets and capital sources beyond loan provisions. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

Green ammonia systems provide an important pathway for converting fluctuating renewable electricity into transportable chemical products. To address the coupled challenges of renewable power variability, heterogeneous electrolyzer dynamics, hydrogen storage constraints, and continuous ammonia synthesis under weak-grid conditions, this paper develops a mixed-integer linear programming scheduling model considering the coordination and start–stop characteristics of ALK–PEM hybrid electrolyzers. The model uses a 15 min resolution over a two-day horizon and integrates renewable power supply, grid electricity purchase, electrolysis, hydrogen storage, and flexible ammonia synthesis in a unified framework. The off, hot-standby, and running states of ALK and PEM electrolyzers are explicitly represented. The case results show that, under the high-renewable-resource scenario, ammonia production reaches 494.93 t, with a curtailment ratio of 3.23% and a grid electricity share of 0.68%, indicating strong renewable-energy conversion capability. Under the low-renewable-resource scenario, ammonia production decreases to 180.09 t and the grid electricity share increases to 40%, showing that the operating priority shifts to maintaining continuous production and safe hydrogen inventory. The ALK hydrogen production share decreases from 93.96% in the high-resource scenario to 75.66% in the low-resource scenario, while the PEM share increases from 6.04% to 24.34%. This indicates that ALK mainly supports large-scale base-load hydrogen production under abundant renewable resources, whereas PEM provides fast compensation and marginal regulation when renewable resources are limited and more volatile. The results demonstrate that ALK base-load production, PEM fast regulation, hydrogen storage buffering, and platform-like flexible ammonia operation jointly provide the main flexibility sources in the studied weak-grid green ammonia system. Full article

Hybrid renewable energy systems (HRES) integrating photovoltaic arrays (PV), wind turbines (WT), and fuel cells (FC) require coordinated maximum power extraction to maintain stable operation under dynamic environmental and load conditions. Conventional MPPT approaches based on independent source-level control often suffer from adverse source interaction, increased steady-state oscillation, degraded DC-link stability, and reduced total extracted power when multiple renewable sources operate simultaneously. To address these limitations, this paper proposes an integrated perturb-and-observe control framework for coordinated power optimization in photovoltaic–wind–fuel-cell hybrid renewable energy systems connected through a shared DC-link structure. Unlike conventional independent MPPT controllers, the proposed strategy evaluates the aggregate power behavior of the integrated system and performs coordinated duty-cycle adaptation to improve renewable-energy utilization while suppressing source conflicts and dynamic coupling effects. The proposed controller is implemented and validated using a real-time digital simulator under a sequential disturbance profile consisting of an irradiance drop at 0.2 s, wind-speed increase at 0.4 s, hydrogen-pressure fluctuation at 0.6 s, and load variation at 0.8 s. Comparative evaluation against conventional perturb-and-observe, incremental conductance, and fuzzy-logic-based MPPT methods demonstrates that the proposed framework achieves a tracking efficiency of $97.8\%$, reduces steady-state tracking error to $2.2\%$, and improves settling time by $42.8\%$ under these dynamic operating conditions. In addition, the proposed controller exhibits lower oscillatory behavior, improved extracted renewable power, and enhanced DC-link stability during simultaneous multi-source disturbances. The results demonstrate that the proposed framework provides an effective real-time coordination strategy for hydrogen-enabled hybrid renewable energy systems operating under dynamically coupled renewable-source conditions. Full article

Following the 2022 disruption of Russian pipeline gas, European countries shifted toward liquefied natural gas (LNG) at markedly different speeds; yet, the drivers of this variation remain poorly understood. This study asks what explains these differences. Using a balanced panel of eight major European gas importers over 2015–2024 (80 observations), the study models the share of LNG in total gas imports as the dependent variable, reversing the conventional approach that treats LNG as an explanatory variable for gas prices. The interaction between the post-2022 structural break and storage fill levels is negative and statistically significant (β = −0.006, p = 0.019 clustered; p = 0.002 Driscoll-Kraay), suggesting that countries with lower storage reserves tended to increase their LNG dependence more strongly. This result is robust across seven of eight specifications and survives time-trend controls and leave-one-country-out analysis. Marginal effects reveal that the storage–LNG relationship was absent before the shock and emerged only after the disruption. Renewable energy penetration emerges as a significant positive predictor. Full article

This paper examines whether total factor productivity (TFP) in Israeli agriculture has genuinely slowed or declined in recent years, or whether the reported trend is primarily driven by methodological choices, data limitations, and measurement error. We compare two widely used approaches to TFP measurement—those of the Bank of Israel and the U.S. Department of Agriculture (USDA)—which differ in their definitions of output, treatment of inputs, and assumptions regarding factor shares. We reconstruct and refine the underlying datasets, addressing important limitations in the existing measures, including the omission of foreign labor, inconsistencies in agricultural land measurement, and the application of non-representative input shares. Despite data improvements and methodological adjustments, both approaches yield similar qualitative conclusions. Following rapid increase in earlier decades, TFP growth in Israeli agriculture appears to have stagnated or declined since the early 2010s. A decomposition of output growth further indicates that recent production patterns have been driven primarily by greater input intensity per unit of land rather than by technological progress or efficiency gains. As a result, agricultural output has shown little or no net growth over the past decade. We discuss potential explanations for this slowdown, including climate change, the growing reliance on reclaimed and other marginal water sources, and the long-term decline in agricultural research and development (R&D) investment relative to sectoral output. Overall, the findings suggest that the productivity slowdown is real rather than an artifact of measurement and underscore the need for renewed investment in agricultural innovation and climate adaptation to sustain domestic production and strengthen food security. Full article

This study investigates whether the optimal utilization of the biomass potential contained in municipal solid waste (MSW) can support the implementation of circular economy (CE) principles and contribute to climate policy objectives, particularly the reduction in greenhouse gas (GHG) emissions in the waste management sector. The analysis evaluates whether waste-to-energy recovery can support the objectives of the European Green Deal, including a 55% reduction in GHG emissions by 2035 and the achievement of climate neutrality by 2050. The assessment was conducted for two MSW streams generated in a Polish municipality: separately collected biowaste and residual MSW remaining after meeting European reuse and recycling targets. The study summarizes the results of detailed experimental investigations of the physicochemical and fuel properties of these waste streams. Proven and commercially available energy recovery technologies, including anaerobic digestion (AD) of biowaste and incineration of residual waste, were analyzed. GHG emissions were assessed using a life cycle assessment (LCA) approach, taking into account both direct emissions and avoided emissions resulting from the substitution of conventional energy and fertilizer production. The experimental results revealed significant variability in the biodegradability and energy potential of individual biowaste fractions, with the highest biogas yields observed for kitchen waste. Residual waste exhibited a considerable calorific value and a significant share of renewable energy due to its biomass content. The results indicate that the share of renewable energy in electricity generated from waste is expected to increase from 46.1% in 2025 to 49.9% in 2040. In relation to the total electricity demand of the analyzed city, energy recovered from waste accounts for 1.8 ± 0.3% in 2025 and 1.3 ± 0.2% in 2040. Scenario-based modeling demonstrated that the target system, maximizing energy recovery from both biowaste and residual waste, achieves a consistently negative GHG emission balance throughout the analyzed period (2025–2040), ranging from −72 ± 15 kg CO₂-eq/ton in 2025, through the most favorable value of −81 ± 17 kg CO₂-eq/ton in 2035, to −57 ± 12 kg CO₂-eq/ton in 2040, expressed per ton of total managed biowaste and residual waste. Full article

The rapid growth of solar photovoltaic (PV) capacity is increasingly reshaping the operation of electricity systems, particularly in countries where renewable energy already represents a large share of generation. In Albania, where electricity production is strongly dominated by hydropower, increasing solar penetration is expected to affect short-term system behaviour, especially in terms of variability, surplus generation, and ramping dynamics. This study investigates PV integration at the system level using hourly electricity demand data for 2024 together with PV generation profiles scaled to different capacity scenarios. PV scenarios representing installed capacities of 150, 300, and 450 MWp, based on real PV deployment data, are analysed under varying levels of hydropower dominance. The analysis combines net-load modeling, ramping assessment, and a simplified flexibility-oriented mitigation approach to evaluate operational impacts under different hydropower conditions. The results indicate that increasing PV capacity significantly modifies the net-load profile. During summer periods, high solar generation substantially reduces midday net load, creating pronounced net-load valleys, whereas winter conditions remain more strongly influenced by electricity demand. As PV penetration increases, ramping intensity also increases. For example, extreme ramp values (Q99) rise from 80.87 MW/h at 300 MWp to 111.45 MW/h at 450 MWp, while the share of hours with ramp events exceeding 100 MW/h increases from 0.05% to 2.55%. The results of a conceptual flexibility approach that limits ramps to 60 MW/h show that extreme ramp events can be effectively mitigated, while moderate variability is largely unaffected. In summary, the results show that increasing solar PV penetration shifts the main operational challenge in Albania from energy balancing toward flexibility and variability management. The findings are particularly relevant for long-term system planning in hydropower-dominated systems and highlight the growing importance of flexibility measures and surplus management under high PV penetration. Full article

This study investigates how the hydrogen supply chain should be designed under alternative carbon tax scenarios to decarbonize heavy-duty freight transportation. A bi-objective, multi-period optimization model is developed to minimize the total daily system cost while constraining CO₂ emissions using the Augmented ε-constraint approach, thereby revealing the trade-off between economic and environmental objectives. The model was applied to Türkiye’s heavy-duty transportation sector and solved under zero, moderate, and aggressive carbon tax scenarios. The results show that the levelized cost of hydrogen (LCOH) ranges from 2.06 to 14.06 $/kg H₂. High carbon pricing increases the LCOH by 29.06% in hybrid designs, while raising the renewable energy share from 2.04% to 46.97% in centralized supply chains. Sensitivity analysis reveals that a ±20% variation in electrolyzer-based production costs does not alter the network topology but shifts the LCOH between 13.10 and 15.02 $/kg H₂ in emission-focused solutions. The findings indicate that in renewable-energy-based decentralized structures, higher carbon tax policies primarily increase the LCOH. Still, the overall technology mix and network topology remain largely unchanged compared to the no-tax case. However, in centralized supply chains, carbon pricing affects both the energy sources and selected technologies. By integrating Türkiye’s 2030–2053 policy milestones into a multi-period framework, this study distinguishes itself by providing a comprehensive, multi-period planning framework tailored to the economic and logistical realities of developing countries. Unlike existing models, our approach quantifies how evolving carbon tax trajectories decisively drive infrastructure investment by analyzing the direct impact of different tax levels on the operational and strategic decisions of heavy-duty transport. This research represents the first joint assessment of carbon tax policy instruments and the evolution of long-term hydrogen supply chains, offering a decision-making framework for policy-driven energy transitions in similar emerging economies. Full article