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Pharmaceutical residues in wastewater pose persistent ecological and public health risks, creating an urgent need for efficient and sustainable remediation technologies. MXene-based photocatalysts have attracted growing interest owing to their high electrical conductivity, tunable surface chemistry, abundant active sites, and excellent charge-transfer capability. This review summarizes recent advances in MXene-based photocatalytic systems for pharmaceutical wastewater treatment and renewable energy production. Key topics include pharmaceutical degradation pathways, reactive oxygen species generation, ecotoxicological implications, and the multifunctional roles of MXenes as conductive supports, electron mediators, and cocatalysts. Interfacial engineering strategies, including Z-scheme, S-scheme, and Schottky heterojunctions, are discussed with respect to light absorption, charge separation, and interfacial redox reactions. Practical considerations, such as reactor design, life cycle assessment, and techno-economic feasibility, are also addressed. Finally, current challenges and future directions are highlighted, particularly scalable fluorine-free synthesis, improved oxidative stability, and machine learning-assisted material design. This review provides a concise framework for developing stable, efficient, and scalable MXene-based photocatalytic platforms for pharmaceutical wastewater remediation and sustainable energy generation. Full article

This paper evaluates the potential for green hydrogen production in Tunisia using nearly 3 GW of renewable electricity distributed across four strategically selected sites: Haouaria, Zriba, Sbikha, and Feriana. These locations were chosen for their proximity to the Trans-Mediterranean (TransMed) natural gas pipeline linking Algeria to Italy, as well as their strong but underexploited solar and wind energy resources. Each site was optimized according to land availability and renewable energy potential: Haouaria is wind-dominant, Zriba employs a hybrid solar-wind configuration, Sbikha focuses on solar, and Feriana integrates both solar and wind over a large area. The analysis reveals a total green hydrogen production capacity supported by approximately 3.1 GW of installed renewable power, with a base-case LCOH ranging from $1.21 to $2.05 per kilogram. El Haouaria emerges as the most cost-effective site due to its highly favorable wind conditions, while the sensitivity analysis shows that LCOH can reach up to approximately $3.8 per kilogram under higher CAPEX assumptions. The findings underscore the viability of a multi-site development strategy and highlight northern Tunisia’s comparative advantage for low-cost green hydrogen production, thanks to its superior resource mix, existing infrastructure, and better water availability relative to Tunisia’s southern regions. Full article

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

The increasing penetration of variable renewable energy sources (RESs) poses significant challenges to power system flexibility and reliability, particularly in systems with high solar generation. At the same time, existing Concentrating Solar Power (CSP) plants in Europe face declining economic viability due to high capital costs and the expiration of incentivized tariff schemes. This study proposes and evaluates a novel approach to repurpose CSP plants as flexible energy assets through the integration of cold thermal energy storage (CTES) within a Thermally Integrated Power-to-Heat-to-Power Energy Storage (TI-PTES) framework. The proposed system combines an ice/water-based cold storage with a CO₂-based refrigeration cycle to enhance the efficiency of the CSP steam cycle by reducing condenser temperatures, while also enabling temporal shifting of electricity consumption. A techno-economic optimization model based on PyPSA is developed to determine the optimal sizing and operation of the storage and refrigeration system under realistic load and electricity price conditions representative of the Spanish market. Results show that the integration of cold storage significantly alters system operation, shifting the chiller from a continuous demand-following mode to an intermittent, high-intensity regime. This leads to a reduction in annual operating expenditures by approximately 32% and an increase in annual profit and net present value (NPV), despite higher capital investment. While hourly net revenue becomes more volatile, with negative values during charging periods, cumulative annual performance improves due to effective temporal optimization. However, the absence of strong electricity price arbitrage and negative price signals limits the revenue potential of the storage system, which primarily acts as a cost-reduction mechanism. The findings demonstrate that cold thermal storage can successfully reposition CSP plants as flexible, value-generating assets in modern electricity systems. The proposed concept offers a promising pathway for extending the operational lifetime of existing CSP infrastructure while supporting higher integration of renewable energy sources. Full article

Against the backdrop of China’s dual-carbon goals and the development of new power systems, the large-scale integration of renewable energy has intensified system regulation requirements and imposed higher demands on the low-carbon performance and flexibility of electricity market clearing mechanisms. To address the inability of conventional static carbon emission factors to accurately reflect the actual emission levels of coal-fired units, this paper proposes a joint energy and frequency regulation ancillary service clearing model incorporating dynamic carbon emission factors. First, a Transformer-based dynamic carbon emission factor model is developed using features such as unit output, load rate, start-up and shutdown status, and unit type to characterize the dynamic variation in the carbon emission intensity of coal-fired units. Second, a coordinated day-ahead and intraday market clearing model is established to jointly optimize unit commitment, generation scheduling, frequency regulation capacity allocation, energy storage operation, and renewable energy accommodation, thereby achieving coordinated improvements in economic efficiency, low-carbon performance, and operational flexibility. Case studies based on actual data from a provincial power grid in southern China demonstrate that the proposed model increases the renewable energy accommodation rate by 2.01%, reduces the total system cost by 1.51%, lowers total carbon emissions by 3.52%, and decreases carbon emission intensity by 5.15%. The results confirm that incorporating dynamic carbon emission factors into joint market clearing can effectively improve both the economic performance and emission reduction capability of the power system. Full article

The large-scale connection of distributed energy resources, electric vehicles, and flexible loads, together with expanding low-voltage monitoring and edge sensing, is turning distribution networks into active cyber-physical systems. Conventional offline simulation cannot fully support the online state tracking, short-term scenario analysis, operational risk assessment, and closed-loop decision support now expected in network operation. Digital twins offer a way to address this gap by linking network models to operational data and revising those models as system conditions change. After systematically searching Scopus and the Web of Science, six application areas for digital twin applications in distribution network simulations are summarized: model construction, simulation and validation platforms; asset, equipment and spatial digitalization; DER (distributed energy resource), PV, EV (electric vehicle) and prosumer integration; operation, monitoring and situational awareness; protection, fault diagnosis and resilience; and optimization, control and planning. The review examines the architectures, enabling technologies, and applications reported across this evidence base. The literature indicates a gradual shift from conceptual digital representations toward real-time simulation, hardware-in-the-loop validation, data-driven model updating, and distribution-side decision support. Persistent gaps concern low-voltage observability, data governance, model credibility assessment, standardized interfaces, cybersecurity, and closed-loop control. Full article

Pumped hydro storage remains one of the most established technologies for balancing supply and demand in electricity markets with high shares of renewable energy. This paper investigates the short-term economic optimization of a pumped hydro storage plant operating under real day-ahead market conditions. A Mixed-Integer Linear Programming model is used to optimize hourly dispatch decisions based on actual day-ahead electricity prices in Slovenia for the year 2024. The model accounts for technical constraints, including turbine and pump capacities, round-trip efficiency, energy storage limits, and restricted startup frequencies. The simulation results show that pumped hydro storage can achieve a positive market-based operating result by responding effectively to price volatility and frequent negative pricing events. Seasonal variations reveal higher revenues during summer months due to solar overproduction. The findings confirm the potential of pumped hydro storage to enhance grid flexibility and support the implementation of national energy transition objectives. By linking large-scale energy storage operations with renewable energy integration, grid flexibility, and market-based dispatch, the study also contributes to the technical and economic dimensions of sustainable energy system development. Full article

Adding an upper reservoir to conventional cascade hydropower stations to create pumped-storage systems is an effective strategy for enhancing hydropower regulation capacity and promoting high proportion of variable renewable energy consumption. To leverage the cross-seasonal energy and intra-day power regulation capabilities of such hybrid systems, this paper proposes a multi-scale nested dispatch and benefit assessment method. The coordination principles between pumped storage and cascade hydropower under high variable renewable energy penetration are first analyzed. Subsequently, a dynamic time-of-use electricity pricing mechanism is developed by capitalizing on the temporal characteristics of net load, and a multi-scale nested scheduling model that incorporates grid regulation demands is established. A techno-economic assessment framework is further developed to assess the comprehensive benefits of the pumped-storage retrofitting. The Wujiang Basin case study demonstrates significant benefits: a 4.5% improvement in peak–valley difference reduction, a decrease of 1039 GWh in annual variable renewable energy curtailment (8.8% of the system’s total), and a 30.8% rise in generation benefits. Under wet and dry hydrological years, generation benefits increase by 787 million and 645 million CNY, respectively. These results indicate that implementing pumped-storage retrofitting in cascade hydropower basins with abundant but seasonally uneven inflow can better align grid regulation requirements with project economic viability. Full article

Risk assessment frameworks for renewable energy projects are predominantly designed for liberalised electricity markets, leaving state-dominated and single-buyer systems analytically underserved. This systematic literature review (SLR) synthesises 116 peer-reviewed studies (2015–2026) following a PRISMA-compliant, Kitchenham-guided protocol to identify and critically evaluate project-level risks and assessment methodologies across diverse electricity market structures. Three contributions are made: (i) a market-structure-differentiated risk taxonomy showing how risk profiles differ structurally across liberalised, hybrid, and single-buyer markets; (ii) the Integrated Risk Assessment Framework for Renewable Energy Projects (IRAF-REPs), a five-layer architecture connecting market structure context, risk category taxonomy, assessment methods, project lifecycle phases, and risk-register standards (ISO 31000/COSO); and (iii) a structured three-horizon future research agenda. Market/price risk (~68%) and policy/regulatory risk (~58%) dominate the reviewed literature, while counterparty/PPA risk—dominant in single-buyer contexts—is largely absent from quantitative frameworks. Monte Carlo simulation and real options analysis lead quantitative practice in liberalised-market studies; the hybrid Monte Carlo-System Dynamics (MC-SD) combination appears in fewer than 4% of studies despite its conceptual suitability for single-buyer contexts. Five research gaps are identified. Findings advance SDG 7, SDG 13, and SDG 9, with direct governance relevance for Indonesia/PLN and comparable Global South economies. Full article

One of the latest and innovative areas of research in energy is the development of thermoelectric generators (TEGs). A novel family of soft, sustainable energy harvesters, hydrogel-based renewable flexible thermoelectric generators use linked ionic, electronic, and redox processes to transform heat gradients into electrical energy. According to recent research, a hydrogel-based TEG has ionic Seebeck coefficients (S) of the order 10–40 mV K⁻¹, which are tens to hundreds of times greater than those of electronic polymers. Thermal conductivities are modest (~0.3–0.6 W/m·K), ionic conductivities typically vary from 10⁻³ to 10⁻¹ S cm⁻¹, and water-rich gels are naturally soft with elastic moduli ~10³–10⁶ Pa and elongations > 100–800%. Recent developments in the concepts, properties, working mechanism, and potential applications of hydrogel-based thermoelectric generators are the focus of this review paper. We investigate the basic transport processes, such as ionic thermodiffusion, thermoelectric ion–electron coupling, and redox-mediated potential production, that allow thermoelectric conversion in hydrogels. This review identifies bottlenecks such as poor output power under minor gradients, summarize performance parameters, and assess methods to improve efficiency. Wearable and implanted power sources, low-grade waste heat collection, and environmental monitoring are examples of promising applications. Lastly, we describe the research avenues that must be pursued in order to expedite the transition of hydrogel-based thermoelectric generators from lab tests to useful, sustainable energy sources. Therefore, the review can provide fundamental knowledge on hydrogel-based TEGs along with their working principles. Full article

Currently, a major challenge for European economies is the volatility of electricity prices, which affects costs borne by households and firms, as well as inflation, economic competitiveness, and energy security. Although the literature has analysed various determinants of electricity prices, there is still limited evidence on the comparative short- and long-term effects of fiscal factors, the natural gas market, and the transition to renewable energy within the Member States of the European Union. This paper analyses the relationship between household electricity prices and a set of economic, climate, and fiscal determinants in EU countries over the period 2017–2025, using panel data econometric methods. The methodology includes pooled OLS models, fixed and random effects estimators, unit root tests, cross-sectional dependence (Pesaran CD) tests, cointegration analysis, and a Panel ARDL-PMG framework, complemented by robustness checks using FMOLS and DOLS-type estimators. The results indicate the existence of a stable long-run equilibrium relationship between the analysed variables, as well as significant cross-sectional dependence among countries, reflecting common shocks and interconnected dynamics in EU energy markets. Fixed effects models are used as the baseline specification, while PMG-ARDL and other dynamic estimators are employed for robustness analysis. The results are consistent across different econometric specifications. The conclusions highlight the dominant role of Household Gas Prices as the main determinant of electricity prices, while energy productivity shows a positive association with electricity price levels. Climate variables exhibit weak and unstable effects, and environmental taxes do not show statistically significant impacts within the sample period. Overall, the findings underline the importance of energy market dynamics, structural factors, and the ongoing energy transition in shaping electricity price developments in the European Union. Full article

This paper presents a comprehensive review of state-of-the-art developments in electric vehicle (EV) charging technologies, charging stations, and charging protocols, with particular emphasis on their integration with renewable energy sources (RESs). EV chargers are generally classified into on-board and off-board configurations. This study examines recent designs and advanced control strategies for both AC/DC and DC/DC power conversion stages, highlighting key technical aspects, recent innovations, and existing challenges. Furthermore, it provides an in-depth discussion of emerging multiport EV charger architectures that integrate photovoltaic (PV) systems, energy storage units, EVs, and the power grid within a unified framework. A comparative analysis is also presented to evaluate various converter topologies and energy management strategies used in the AC/DC and DC/DC stages of EV charging systems. Critical performance indicators such as power rating, output voltage level, efficiency, economic feasibility, and system complexity are also discussed. A comprehensive comparison is conducted among 13 review papers between 2015 and 2026, identifying key trends, methodological differences, and common findings. Full article

Hydrogen is increasingly recognized as a key energy carrier for decarbonizing hard-to-electrify sectors, yet more than 95% of current global production remains fossil-derived. Biological hydrogen (biohydrogen) produced by dark fermentation (DF), photo-fermentation (PF), or microbial electrolysis cells (MEC) offers the dual advantage of valorizing organic wastes while delivering low-carbon H₂; however, none of these standalone technologies mobilizes more than 25–33% (DF), 40–70% (PF), or 40–60% (MEC) of feedstock organic carbon through H₂-producing oxidation pathways. Most existing reviews compare these pathways on hydrogen yield alone, a metric that conceals where the majority of feedstock carbon and electrons are actually lost and obscures the quantitative rationale for system integration. This review reframes the comparison around carbon and electron flow, explicitly tracking how much input carbon is mobilized through H₂-producing oxidation pathways, how much is retained in volatile fatty acids (VFAs), biomass, or unlinked CO₂, and what happens to the associated electrons. Stoichiometric, mechanistic, and reactor-level evidence is synthesized to show that DF channels only 25–33% of input organic carbon through H₂-yielding decarboxylation on real heterogeneous substrates, with 40–60% retained as residual VFAs and unhydrolyzed solids; PF can recover 60–80% of VFA carbon but is constrained by photon economics and nitrogenase sensitivity; and MEC achieves >85% COD removal only when coupled to an upstream acidogenic stage. Two-stage (DF–PF, DF–MEC) and three-stage (DF–PF–MEC, DF–MEC–AD) configurations are critically evaluated, with theoretical yields separated from experimentally demonstrated performance on real wastes and hidden energy inputs (pretreatment, inter-stage transfer, gas separation, and compression) explicitly accounted for. DF–MEC coupling is identified as the most near-term tractable configuration, achieving 55–70% H₂-pathway carbon mobilization and 80–92% COD removal at an electrical input of 0.9–1.5 kWh/m³ H₂, with levelized hydrogen costs of US$3–5.5/kg under favorable waste-tipping-fee conditions. Multi-stage systems push carbon recovery above 70% but carry unresolved capital, methanogenesis control, and scale-up penalties. This review closes by proposing a standardized ten-descriptor reporting framework including H₂-pathway carbon mobilization (%), cathodic hydrogen recovery (rCAT), net energy recovery (NEB), and LCA carbon intensity under both attributional and consequential boundaries, and demonstrates its backward compatibility by retrospective application to seven studies already in the literature. Research priorities tractable on a 5–10 year horizon are identified, centered on methanogen suppression at pilot scale, real-waste MEC performance, and renewable-electricity coupling. Full article

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims. Full article