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The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article

Sustained demand growth and the increasing share of renewable energy sources pose challenges for the operation of modern electrical systems. The variability in wind and solar photovoltaic generation causes temporary imbalances between supply and demand, requiring the incorporation of energy management and storage strategies to guarantee supply. In this context, the need arises to develop optimization models that allow for efficient energy dispatch, minimizing costs and promoting the appropriate use of both conventional and renewable resources. This study formulated a time block dispatch optimization model implemented in the IEEE 24-node system, integrating thermal, hydroelectric, photovoltaic, wind, and energy storage systems. The methodology was based on DC power flows and was developed in MATLAB R2024b, incorporating nodal balance constraints, transmission and generation capacity limits, as well as the operating conditions of the storage systems. The model allowed for the evaluation of both energy and economic performance, validating its behavior under conditions of peak demand and renewable variability. The results demonstrate that the inclusion of energy storage systems allows for a reduction in high-cost thermal generation, optimizing demand coverage with a greater share of renewable energy. An average storage efficiency of 85.5% was achieved, and total system costs were reduced by USD 40,392.39 per day, equivalent to annual savings of USD 14.75 million. Furthermore, power flows remained below 85% of transmission capacity, confirming the proper operation of the grid. In this sense, the model fulfills the proposed objectives and proves to be a tool for energy planning and the technical-economic integration of storage in electrical networks. Full article

The rapid global expansion of offshore wind energy (OWE) has established it as a critical component of the renewable energy transition; however, this development concurrently introduces significant underwater noise pollution into marine ecosystems. This paper provides a comprehensive review of the acoustic footprint of OWE across its entire lifecycle, rigorously distinguishing between the high-intensity, acute impulsive noise generated during pile-driving construction and the chronic, low-frequency continuous noise associated with decades-long turbine operation. We critically evaluate the engineering capabilities and limitations of current underwater acoustic monitoring architectures, including buoy-based real-time monitoring nodes, cabled high-bandwidth systems (e.g., cabled hydrophone arrays with DAQ/DSP and fiber-optic distributed acoustic sensing, DAS), and autonomous seabed archival recorders (PAM deployment). Furthermore, documented biological impacts are synthesized across diverse taxa, ranging from auditory masking and threshold shifts in marine mammals to the often-overlooked sensitivity of invertebrates and fish to particle motion—a key metric frequently missing from standard pressure-based assessments. Our analysis identifies a fundamental gap in current governance paradigms, which disproportionately prioritize the mitigation of short-term acute impacts while neglecting the cumulative ecological risks of long-term operational noise. This review synthesizes recent evidence on chronic operational noise and outlines a conceptual pathway from event-based compliance monitoring toward long-term, adaptive soundscape management. We propose the implementation of integrated, adaptive acoustic monitoring networks capable of quantifying cumulative noise exposure and informing real-time mitigation strategies. Such a paradigm shift is essential for optimizing mitigation technologies and ensuring the sustainable coexistence of marine renewable energy development and marine biodiversity. Full article

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

The occurrence of salt wedge intrusion is a common phenomenon in microtidal Mediterranean river mouths, particularly during summer, when reduced river discharge occurs. In the Strymon River, upstream saltwater intrusion affects both the hydrodynamic functioning of the system and the estuarine ecosystem. This study investigates the integrated ecohydrological management of river mouths characterized by salt wedge intrusion, aiming to both limit upstream saltwater penetration and exploit the salinity gradient between seawater and river water for renewable energy production. The study examines the operation of a Salinity Gradient Energy power plant based on Pressure Retarded Osmosis (PRO) technology, with a nominal capacity of 1 MW, located at the Strymon River mouth. A dynamically coupled hydrodynamic and energy production model is developed to assess four operational scenarios with different seawater and freshwater intake locations along the river channel. The results show that, in all scenarios, salt wedge intrusion is restricted to a distance of less than 2000 m from the river mouth, while salt wedge salinity is reduced by up to 35% compared to reference conditions. At the same time, annual energy production exceeds 1.03 GWh in all scenarios, corresponding to the electricity demand of approximately 824 to 1045 households, depending on the operational configuration. Overall, the study demonstrates that salinity gradient energy exploitation can be effectively combined with ecological control of salt wedge intrusion, providing a novel and sustainable framework for the management of Mediterranean estuarine systems. Full article

In provincial electricity markets where long-term contracts and spot trading coexist, multiple categories of imbalance funds arise from congestion, energy deviations and dual-track price differences, posing challenges to energy management systems (EMS) in terms of fair and robust settlement. This paper proposes an EMS-oriented framework to assess and diagnose alternative imbalance settlement mechanisms in a provincial dual-track market. First, a unified settlement model is developed that reconstructs key imbalance fund categories and allocates them to heterogeneous agents—thermal, renewable and storage units and different user groups—under a library of settlement rules. Second, a multi-scenario simulation platform is built, covering normal operation, tight supply and high-renewable-volatility conditions. Third, a multi-criteria evaluation scheme is designed to quantify economic efficiency, fairness, risk and renewable support for each mechanism–scenario combination. Finally, a category–agent two-dimensional diagnostic module is introduced to reveal misallocation patterns and the main money-transfer paths among fund categories and agent groups. A case study on a realistic provincial system shows that the proposed framework can distinguish mechanisms with better overall robustness, identify severe cross-subsidies in extreme scenarios and provide practical guidance for refining imbalance settlement parameters within EMS-driven market operations. Full article

Microbial components are part of the composition of all waste, including lignocellulosic biomass (e.g., agricultural, domestic, industrial, and municipal wastes) generated via human activities. If little attention is given to these wastes or if they are not adequately managed, they tend to end up in the environment (soil, water, and farmland), decomposing naturally through microbial activities, producing greenhouse gases, causing eutrophication, preventing sunlight penetration, and depleting oxygen in the water. Several treatment methods are applicable to these wastes. However, anaerobic digestion is presented as the best option to properly treat the waste. It is regarded as the best technique to achieve sustainable energy development in both developing and developed countries. During anaerobic digestion, the organic matter in the waste is converted via the concerted activities of microbes belonging to different trophic levels, in the absence of oxygen, to yield biogas (renewable energy), bio-fertiliser, and sanitisation of the waste, rendering it better and safer for human handling. Varying levels of loss of bacterial viability and their antibiotic-resistance genes are observed with this process, as bacteria differ in susceptibility to temperature, pH, nutrient scarcity, and the presence of antimicrobials. Anaerobic digestion of agricultural residues and the immediate processing (post-treatment) of the digestate help to stabilise the digestate, making it safe for land applications, tackling waste management, and protecting food chains from contamination, in addition to the environment. This review focuses on the anaerobic digestion of lignocellulosic biomass, yielding biogas as energy, alongside sanitising the wastes by inactivating microbial components found therein, therefore reducing the contamination potential of the effluent or digestate discharged from the biodigester following the process. Several findings registered by different researchers through different studies performed in different countries under different scenarios while employing varying methods have been assembled in a chronological fashion to emphasise similarities and divergences or variations that deepen knowledge pertaining to the significance of the anaerobic digestion process in terms of the microbial interactions responsible for producing energy, addressing sanitisation and hygiene crisis, and the post-treatment of the digestate to ensure its use as biofertiliser. In other words, it is a comprehensive review that synthesises knowledge from multiple fields covering comparative aspects of anaerobic digestion in terms of sanitation, hygiene, and energy production and consolidates it in a single document to present and address the problem of waste management through anaerobic digestion technology. Full article

Energy recovery technology is becoming a crucial part of modern approaches that address decarbonization, efficiency, and transitioning into a circular economy. In addition, apart from its advancements in efficiency and environmental benefits, its progress appears to be progressively limited due to its maturity and increasing complexity. In this case, innovation that focuses solely in the firm appears ineffective because more and more important knowledge in terms of innovation in processes and environmental aspects is becoming and remaining outside of organizational boundaries. In this paper, open innovation will be explored in its function as a structural innovation method of advancing energy recovery technology. The paper employs the narrative literature review of peer-reviewed literature indexed in the Scopus database to explore the implications of the outside-in model of open innovation, the inside-out model of open innovation, and the coupled model of open innovation with respect to the primary recovery processes of energy such as combustion, gasification, pyrolysis, anaerobic digestion, and landfill gas recovery. The literature incorporates findings about the implications of knowledge inflows and outflows with respect to the mentioned energy recovery processes. The results show that open innovation efficacy strongly varies according to the degree of technological maturity and performance issues, in that outside-in open innovation tends to be very effective in mature and semi-mature technology sectors, where incremental improvements in efficiency require specialized knowledge outside the industry, while coupled open innovation is crucial for addressing system-wide issues in areas such as emissions, regulatory compatibility, and infrastructure integration, while inside-out innovation is largely a means of facilitating technology dissemination and standardization once a degree of technological maturity had been realized. This study, through the association of selective open innovation practices with corresponding energy recovery technology and challenges, aims to provide a more nuanced perspective on the assistive potential of collaborative innovation in effecting sustainable development in energy recovery technology. Full article

The growing demand for sustainable energy alternatives highlights the need for decision support tools in biodiesel supply chains. This study proposes a mixed-integer programming (MIP) model for tactical planning in the palm oil biodiesel supply chain, focusing on refining, blending, and distribution. The model incorporates economies of scale, inventory, and transport constraints and is enhanced with valid inequalities (VI) and a warm-start heuristic procedure (WS) to improve computational efficiency. Computational experiments on simulated instances with up to 6273 variables and 47 million iterations demonstrated robust performance, achieving solutions within 15 min. The model also reduced time-to-first-feasible (TTFF) solutions by 60–75% and CPU times by 17–21% compared to the baseline, confirming its applicability in realistic contexts. The proposed model provides actionable insights for managers by supporting decisions on facility scaling, product allocation, and profitability under supply–demand constraints. Beyond palm oil biodiesel, the formulation and its VI + WS enhancement provide a transferable blueprint for tactical planning in other process industry and renewable energy supply chains, where (i) multi-echelon flow conservation holds and (ii) discrete operating scales couple throughput with fixed/variable cost structures, enabling fast scenario analyses under changing prices, demand, and capacities. Full article

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental footprint of industrial packaging by integrating recent developments in life cycle assessment (LCA), ecological footprint (EF) methodologies, material innovations, and circular economy models. The assessment examines the sustainability performance of conventional and alternative packaging materials, plastics, aluminum, corrugated cardboard, and polylactic acid (PLA). Findings indicate that although corrugated cardboard is renewable, it still presents a measurable environmental burden, with evidence suggesting that incorporating solar energy into production can reduce its footprint by more than 12%. PLA-based trays demonstrate promising environmental performance when sourced from renewable feedstocks and directed to appropriate composting systems. Despite these advancements, several systemic challenges persist, including ecological overshoot in industrial regions where EF may exceed local biocapacity limitations in waste management infrastructure, and significant economic trade-offs. Transportation-related emissions and scalability constraints for bio-based materials further hinder large-scale adoption. Existing research suggests that integrating sustainable packaging across supply chains could meaningfully reduce environmental impacts. Achieving this transition requires coordinated cross-sector collaboration, standardized policy frameworks, and embedding advanced environmental criteria into packaging design and decision-making processes. Full article

Hydrogen storage is vital to the development of renewables, especially in low-infrastructure countries. Metal hydrides offer a small but safe solid-state candidate for hydrogen storage at medium pressures and near-ambient temperature, yet large-scale applications face heat-management challenges. In this article, we numerically analyze examples of two large-scale lanthanum pentanickel (LaNi₅)-based metal hydride reactor configurations with shell-and-tube heat exchangers. This research studies two large-scale shell-and-tube metal hydride reactor configurations: a tube-side cooling reactor with hydride powder packed in the shell and coolant flowing through internal tubes, and a shell-side cooling reactor using annular hydride pellets with coolant circulating through the shell. The thermal and kinetic performance of these large-scale reactors was simulated using COMSOL Multiphysics (version 6.1) and analyzed under different geometries and operating conditions typical of industrial scales. The tube-side solution provided 90% hydrogen absorption in 1500–2000 s at 30 bar, while the shell-side solution reached the same level of absorption in 430 s at 10 bar. Results show that tube-side cooling has higher storage, while shell-side cooling improves heat removal and kinetics. For energy and maritime transport applications, these findings reveal optimization insights for large-scale, efficient hydrogen storage systems. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions. Full article

Microbial fuel cells (MFCs) have attracted increasing attention due to their potential applications in renewable energy generation, waste utilization, and biomass upgrading, offering a promising alternative to traditional fossil fuels. By directly converting carbon-rich wastes into electricity, MFCs provide a unique approach to simultaneously address energy demand and waste management challenges. This review systematically examines the effects of various carbon-rich substrates on MFC performance, including lignocellulosic biomasses, molasses, lipid waste, crude glycerol, and C1 compounds. These substrates, characterized by wide availability, low cost, and high carbon content, have demonstrated considerable potential for efficient bioelectricity generation and resource recovery. Particular emphasis is placed on the roles of microbial community regulation and genetic engineering strategies in enhancing substrate utilization efficiency and power output. Additionally, the application of carbon-rich wastes in electrode fabrication is discussed, highlighting their contributions to improved electrical conductivity, sustainability, and overall system performance. The integration of carbon-rich substrates into MFCs offers promising prospects for alleviating energy shortages, improving wastewater treatment efficiency, and reducing environmental pollution, thereby supporting the development of a circular bioeconomy. Despite existing challenges related to scalability, operational stability, and system cost, MFCs exhibit strong potential for large-scale implementation across diverse industrial sectors. Full article

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities. Full article