Search Results (739)

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

Large-scale renewable energy integration introduces random power fluctuations into microgrids, increasing the difficulty of frequency regulation. To improve regulation stability and training efficiency, this article proposes sample selection generative adversarial networks (SSGANs) based on sample selection networks (SSNs), conditional generative adversarial networks (CGANs), and the actor–critic framework. First, the SSNs are trained to evaluate sample information values and prioritize informative samples for model training. Second, the CGANs learn the conditional mapping between microgrid operating states and control actions, and the pretrained generator is transferred into the actor–critic framework as the actor. Third, the actor–critic framework further optimizes the control policy online to generate real-time frequency regulation commands. The proposed method is tested on a standard two-area system and further validated on a complex four-area system. Case studies show that SSGANs achieve faster convergence and better frequency regulation performance than typical control algorithms. Full article

The global renewable energy sector now represents the world’s fastest-growing sector, with growth projected to more than double by 2030 and expected to exceed 4600 GW between 2025 and 2030. This is driven by falling costs, increasing consumer awareness, sustainable energy production models, and national and international climate commitments. This review study aims to discuss the transformation initiatives in the renewable energy sector with net-zero targets. A total of 89 studies published between 2020 and 2026 were identified for this literature review. The results indicate that digital transformation has the potential to significantly optimize the performance of the renewable energy sector by resolving its sustainability issues. This study discusses the waste types and waste management strategies in the renewable energy sector. It also highlights the indicators, barriers, and drivers of sustainable performance in the renewable energy sector by integrating advanced technological solutions in manufacturing, supply chain management, maintenance, monitoring, and the management of renewable energy equipment. The study findings demand global commitment and policy coordination in achieving the goals of decarbonization. The literature insights highlight future core research fields and can guide international organizations, industrial policymakers, and academic scholars towards a better and more sustainable future. Full article

The Dynamic Economic Dispatch (DED) problem underpins the cost-efficient and reliable operation of modern power systems, yet valve-point loading, ramp-rate coupling, and the growing share of intermittent wind, photovoltaic, and pumped-storage hydro (PSH) resources render it highly non-convex. Metaheuristic methods typically require large computational budgets and hand-crafted constraint-handling rules, whereas deep reinforcement learning agents rarely guarantee the feasibility of the schedules they produce. To address both limitations, this paper proposes a Two-Stage PPO–RLMPA framework that couples data-driven policy learning with a biomimetic metaheuristic search inspired by marine predator–prey dynamics. In the first stage, a Proximal Policy Optimization (PPO) agent is trained on a Markov Decision Process reformulation of DED in which a deterministic Safety Layer projects every raw action onto the feasible set defined by capacity, ramp-rate, and power-balance constraints, so the policy only observes physically viable transitions. In the second stage, the PPO dispatch is refined by the RLMPA module, a Marine Predators Algorithm (MPA) whose exploration–exploitation balance, Lévy-flight foraging, and Fish Aggregating Devices (FADs) attraction mechanisms emulate strategies documented in marine ecosystems; its step-size factor and FADs probability are further adapted online by a Deep Q-Network. This biomimetics-informed refinement translates predator–prey foraging intelligence into economically efficient thermal dispatch under valve-point non-convexity. Across 30 independent runs on ten- and twenty-unit benchmark systems with wind, PV, and PSH integration, the framework attains best costs of USD 368,763 and USD 737,348 on Test Systems 1 and 2, corresponding to reductions of approximately $1.1\%$ and $4.4\%$ over the CFCEP baseline, with zero post-repair constraint violations in every run. Full article

The inherent variability of wind power generation poses major challenges for maintaining voltage stability and power quality in modern distribution networks. Conventional rule-based and optimisation-driven control strategies often fail to respond effectively to these rapid fluctuations. To address this limitation, this paper introduces an adaptive reinforcement learning (RL) framework that autonomously optimises reactive power compensation and on-load tap changer (OLTC) operations in real time. The proposed deep Q-network (DQN) agent learns optimal control policies through continuous interaction with the grid environment, minimising voltage deviations and network losses under dynamic wind conditions. Using the IEEE 33-bus distribution test system, the trained DQN achieved a substantial improvement in voltage regulation, reducing the average deviation from 0.041 p.u. (rule-based) to 0.014 p.u. and lowered power losses by 24.6/5 compared to traditional optimisation techniques such as Particle Swarm Optimisation (PSO) and static rule-based control. Furthermore, the DQN controller demonstrated the fastest learning convergence within 120 episodes, validating its potential for real-time adaptive voltage control. Overall, the study highlights RL as a promising, scalable solution for autonomous voltage regulation in smart grids integrated with renewables. 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

Under international climate governance frameworks, including the Paris Agreement, the global decarbonization process has accelerated, imposing more stringent requirements on power system flexibility and low-carbon operation. Against this backdrop, pumped storage power stations, characterized by high flexibility and rapid response capability, serve as large-scale energy storage solutions that can replace thermal power for peak shaving, thereby enhancing renewable energy integration and delivering significant carbon reduction benefits in multi-energy complementary systems. A carbon reduction calculation model is developed within the framework of the Chinese Certified Emission Reduction (CCER) trading mechanism to quantify the annual contributions of pumped storage to carbon reduction. Using a Fractional-Order Gray Model (FGM) optimized via Particle Swarm Optimization (PSO), future carbon market prices are forecasted, facilitating a robust economic evaluation. The findings reveal that, over its lifecycle, pumped storage could achieve a total carbon reduction of approximately 23.27 million tons of CO₂, yielding approximately 7.981 billion CNY in carbon reduction value, with an initial 7-year CCER inclusion period contributing 254.0787 million CNY in carbon credits. It provides critical economic and policy insights, supporting the design of advanced power systems that position pumped storage as a central regulatory asset in carbon reduction strategies. Full article

As renewable penetration in data-center power supply increases, stochastic renewable output can cause tie-line power fluctuations between data centers (DCs) and the utility grid. This paper proposes a privacy-preserving multi-time-scale tie-line power smoothing method for multiple DCs. A two-stage first-order low-pass filter decomposes tie-line fluctuations into high- and low-frequency regulation targets. Server task shifting tracks the high-frequency target, while uninterruptible power supply (UPS) regulation compensates the low-frequency residual under practical energy and power constraints. Second, a federated adaptive proximal policy optimization (Fed-AdaPPO) framework is developed. Proximal policy optimization (PPO) provides stable policy optimization in the continuous action space, and the upper confidence bound (UCB)-guided adaptive exploration improves task-shifting exploration. Critically, only Critic gradients are aggregated across DCs; Actor networks, raw workload data, and user-sensitive information remain local. This design reduces the risk of exposing local state-action mappings. Results show that coordinated server-cluster and UPS regulation reduces the standard deviation of tie-line power by at least 33.4% while maintaining service quality and data privacy. Full article

Background: Upgrading existing housing, particularly affordable housing in China’s high-altitude cold regions, to near-zero energy standards requires balancing three key considerations: carbon reduction, life-cycle cost, and residents’ affordability. Methods: We developed a simulation-based multi-objective optimization framework to evaluate repair-and-retrofit packages involving the building envelope, ventilation, heating electrification, on-site renewables, and control strategies, subject to social feasibility and affordability constraints. Results: The Pareto-optimal solutions revealed a clear knee region in which substantial operational carbon reductions and acceptable thermal safety could be achieved at moderate investment levels. Further decarbonization was enabled by strong system-level synergies among heat recovery ventilation, heat pumps, and photovoltaic systems. Affordability-constrained optimization shifted the feasible solution space toward options associated with lower household energy burdens and more favorable distributional outcomes. Conclusions: Policy scenario analysis indicates that grid decarbonization and targeted financial support can expand the feasible space for low-carbon pathways and improve equity, thereby enabling scalable near-zero energy upgrading strategies. Full article

The integration of distributed energy resources (DERs) has enhanced the operational flexibility and complexity of smart building energy management, which is crucial to urban sustainable development. However, the limitations of strategy applicability across different environments and lengthy development cycles pose significant challenges for energy management. To address this, this paper proposes a transferred multi-thread deep reinforcement learning (TMDRL) framework for the cross-domain energy management of smart buildings. Firstly, a source-domain heterogeneous exploration architecture based on multi-thread deep reinforcement learning (DRL) is proposed. A transferable source-domain knowledge base is constructed to enhance the generalization ability of pre-trained strategies. Secondly, a decoupled double-critic optimization mechanism is designed to mitigate policy evaluation bias during cross-domain transfer. Finally, simulations using real-world datasets from different times and areas are conducted. The results show that compared to A3C, DDPG, and SAC, the proposed TMDRL framework reduces total costs by 32.77%, 18.14%, and 37.24%, while improving convergence efficiency by 29.55%, 22.89%, and 32.84%, respectively. The reduction in total cost and improvement in convergence efficiency demonstrate that the proposed TMDRL framework effectively saves energy and enhances the utilization of renewable energy, proving the sustainable benefits of smart building energy management across domains. 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

Background: This study aims to evaluate the impact of energy and logistics factors on the milk producer price index to support evidence-based policies that maintain price stability at an optimal level. Methods: Annual data for 2000–2023 are used, including the milk producer price index, milk production volume, transport CPI, diesel price, CO₂ emissions from agriculture, and renewable energy consumption (percentage of total energy consumption). A log-linear ARDL model is applied to examine both short- and long-run asymmetric effects of diesel prices, transport costs, and agricultural CO₂ emissions on milk production dynamics. Results: The research results indicate that energy expenses, logistics considerations, and environmental metrics have statistically significant asymmetric influences on milk production. This underscores the varying short-term adjustments and enduring long-term economic effects throughout the supply chain. Conclusions: Energy and cost factors on the supply side significantly influence the stability of milk markets. Therefore, improving transportation efficiency, encouraging the use of renewable energy sources, and addressing environmental impacts can contribute to consistent and sustainable pricing. Specific policies—including investments in transport infrastructure, subsidies for green energy targeting dairy producers, carbon pricing with support tailored to the sector, and digitalization of supply chains—can enhance resilience and ensure price stability. Full article

Vehicle fleet renewal policies promoting NEVs aim to decarbonize transportation but inadvertently alter urban atmospheric corrosivity, threatening the durability of infrastructure coatings. This study investigated the cross-system impacts of vehicle trade-in subsidies on the degradation of protective coatings. We developed a coupled framework integrating a Mixed Logit model for fleet evolution, dynamic Life Cycle Assessment for tracking acidic precursors (SO₂, NO_x), and an Environmental Corrosion Risk Index. Using established Dose–Response Functions, we quantified the lifespan depletion of a standard epoxy zinc-rich primer and polyurethane topcoat system. Our results indicate that aggressive subsidies induce a transition to heavy NEVs, triggering an “emission inversion” that spikes upstream grid acidic emissions. This localized acidification significantly accelerates chemical degradation, reducing the effective service life of infrastructure coatings by 1.3–2.3 years and necessitating premature, costly recoating. We identify a Pareto-optimal subsidy window (8000–10,500 CNY) that effectively balances decarbonization targets with coating preservation. In conclusion, sustainable urban policies must incorporate surface engineering and material durability metrics to prevent emission shifts from compromising the physical integrity of transportation infrastructure. Full article

The growing penetration of variable renewable energy sources transforms power grid dispatch into a high-dimensional, stochastic, and multi-agent decision-making problem that challenges both classical optimization and standard Reinforcement Learning (RL) methods. Traditional RL policies, typically parameterized as unimodal Gaussians, lack the expressiveness to capture the multimodal action distributions that arise when multiple feasible dispatch strategies coexist, while diffusion-based generative policies achieve expressiveness at the cost of prohibitively many iterative denoising steps during inference. We propose Multi-Agent Flow-based Q-Learning (MAFQL), a framework that addresses this expressiveness–efficiency tradeoff by integrating conditional flow matching with conservative Q-learning under a Centralized Training with Decentralized Execution (CTDE) architecture. The framework consists of a unified training pipeline that combines four learning objectives: behavior cloning, flow matching, conservative Q-learning, and distillation. This allows for expressive policy generation through only 1–5 ODE integration steps. Measured per-agent inference latencies below $8\mathrm{ms}$ (P99) are achieved on both GPU and CPU hardware, which is compatible with the response requirements of automatic generation control. We formulate the dispatch task as a Dec-POMDP over three physically grounded control zones derived from the RTE network topology and evaluate MAFQL on the IEEE 118-bus and 14-bus systems in the Grid2Op simulator. Empirical results show that MAFQL CTDE substantially outperforms all tested baseline methods on the 118-bus system under a composite multi-objective reward function and that it demonstrates initial cross-scale generalizability on the 14-bus system. The decentralized execution variant consistently outperforms centralized execution, consistent with the hypothesis that distillation facilitates effective knowledge transfer. At the end of the paper we discuss current limitations such as the absence of ablation studies, end-to-end latency measurements, and formal safety guarantees, then outline directions for addressing them. Full article

Electrification of road transport is widely promoted as a pathway to reduce greenhouse gas (GHG) emissions; however, its effectiveness depends critically on electricity carbon intensity, renewable energy share, charging behavior, and grid capacity constraints. This study develops a multi-objective analytical and optimization framework to evaluate cost and carbon-optimal electric vehicles electrification by jointly minimizing system cost and carbon emissions under coupled transport–energy system conditions. A closed form cut-off condition is derived to determine the minimum renewable electricity share required for electric vehicles to achieve lower emissions than internal combustion engine vehicles, and the formulation is extended to mixed fleets including battery electric and plug-in hybrid electric vehicles. The framework integrates fleet-level emissions, electricity demand, renewable capacity limits, charging losses, carbon taxation, and peak charging constraints to define a feasible electrification region. Feasibility mapping, Monte Carlo exploration, and evolutionary multi-objective optimization are employed to characterize trade-offs between CO₂ emission and total system cost, and to identify Pareto-optimal and knee point solutions. The results show that electrification without sufficient renewable support or coordinated charging can increase emissions and violate grid limits, whereas integrated planning enables significant emission reduction within economically viable regions. These findings provide a quantitative and decision-oriented basis for cut-off-informed and grid-aware electrification planning in carbon-constrained power systems. Full article