Search Results (181)

Artificial intelligence is accelerating data center electricity demand, but annual energy totals alone do not reveal when, where, or at whose cost new loads enter power systems. This review asks how uncertain projects should be translated into demand, infrastructure, price, flexibility, and environmental consequences. We conducted a structured critical review of 44 peer-reviewed studies, working papers, and official, regulatory, statistical, system-operator, and institutional reports identified through 31 August 2026, with explicit source selection and claim-level reliability appraisal. The central finding is that the economic effect of AI data centers depends on project-realization probability, coincident demand, and location, not on announced capacity alone. Concentrated growth can require generation and network investment and raise local prices. Enforceable temporal or spatial flexibility can reduce these costs, but unverified flexibility warrants no planning credit. Annual renewable matching supports an accounting claim but not necessarily consequential decarbonization, which also requires additional supply, temporal matching, and geographic deliverability. Policy should therefore condition connection on project credibility, cost responsibility, measured flexibility, and credible procurement. This review’s incremental contribution is to move beyond consumption inventories by integrating uncertain project realization, short-run operation, medium-run connection and investment, long-run expansion, distributional incidence, and environmental counterfactuals in one energy-economics framework. Full article

The energy consumption and electricity cost of air-conditioning systems in metro stations keep rising with the expansion of rail transit. Ice thermal-energy storage (ITES) shifts cooling load to off-peak hours, yet existing studies mostly optimize ice-storage operation on a daily scale and rarely address multi-chiller load distribution, the coordination of the large and small systems of a station, or monthly peak shaving adapted to the two-part electricity tariff. For a standard metro station with platform screen doors in Wuxi, China, the annual hourly cooling loads of the large and small systems are simulated with DeST 2.0, and energy-consumption models of the chillers, multi-split units, and pumps are established. A two-layer monthly peak-shaving framework is then proposed: for the large system, a balanced load-distribution strategy and three stored-cooling allocation schemes (base-load chiller priority, cold-storage priority, and peak shaving) integrated via a monthly load-baseline switching model; for the small system, three annual regulation strategies. The balanced load-distribution strategy saves 0.49–14.90% of energy compared with full-load operation; the combined cold-storage-priority and peak-shaving strategy reduces the monthly demand charge by 15.69% and the annual electricity cost by 2.32% versus the single cold-storage-priority strategy; the ice-storage scheme cuts the annual energy cost by 23.02% versus the conventional system, with peak-shifting and valley-utilization ratios of 56.89% and 39.75%; and the energy-efficiency-priority strategy lowers the small-system energy consumption, operating cost and carbon emission by 8.19% and 16.90%. As these strategies are derived from the load profile and tariff structure of the case station, their applicability to stations with different conditions requires further validation. Full article

This paper presents a regional techno-economic and environmental screening of electricity production from biomass in Yalova, Türkiye, based on a secondary biomass inventory, logistics and conversion assumptions, an auditable MATLAB/Octave feedstock allocation model, a consistent tariff-feedstock-CAPEX-O&M test, and emissions screening. The source inventory indicates 610,498 tons/year of biomass that translates into 55,040 ton-of-equivalent-oil/year of biomass. The optimization is not presented as a new mathematical algorithm; its contribution is to convert the regional inventory into an operational procurement decision under explicit availability, delivered-cost, yield, and throughput constraints. Annual throughput at 220,000 t leads to the minimum cost scenario of 140,573 t/year of chicken litter with 79,427 t/year of forest residues to generate 40.37 GWh/year (net equivalent of 5.42 MW @ 85% capacity factor) at a delivered feedstock cost of 286.8 USD/MWh. Using the same tariff as 2788.4 TRY/MWh and an exchange rate of 35 TRY/USD consistently to the optimized scenario, the resulting tariff is 79.67 USD/MWh, while electricity income is estimated at USD 3.22 million/year, whereas total feedstock expenditure is approximately USD 11.58 million/year before fixed O&M costs. The result of consistency of 25 years for electricity-only economics therefore cannot show any economic viability in electricity production based on the above assumptions. Environmental impacts are presented as screening results but not the carbon benefit through the life cycle: The grid factor of the grid for ‘other renewable’ projects in Turkey for 2023 (0.5350 tCO₂/MWh) shows a grid displacement of about 21.6 ktCO₂/year, and direct biogenic CO₂ and non-CO₂/air pollutant emissions need to be reported separately based on feedstock sourcing and combustion controls and alternative waste disposal. The results support adaptive feedstock planning, but they do not constitute a final investment or life-cycle assessment. Full article

In global climate governance, the implementation of unilateral carbon border adjustment mechanisms is impacting the international trading system. Existing research has largely focused on the macro-level trade and emissions reduction effects of carbon tariffs, with few studies systematically examining their dynamic impacts on the division of labor, positioning, and resilience of global value chains along production networks. This study employs a recursive dynamic computable general equilibrium model, combined with a trade value-added decomposition method, to construct four scenarios and quantitatively examine the heterogeneous impacts of carbon tariffs on the division of labor within global value chains. The results indicate: First, carbon tariffs alter relative prices in international markets, causing welfare and economic gains to shift overall from developing economies to policy-implementing and other developed economies. Second, tariff barriers trigger trade diversion and domestic substitution effects, driving regionalization in the flow of intermediate goods trade and inhibiting value-added creation in developing economies. Third, the degree of value chain participation and division of labor positions across economies diverge: forward participation increases in policy-implementing economies, while basic manufacturing economies such as China face the dual challenges of pressure on forward participation and a decline in their relative position. Fourth, external constraints drive certain segments of the transnational division of labor to converge inward. Combined with the rising concentration of foreign value-added sources and increased deviation in production length, this weakens the overall risk resilience of global value chains. This study provides a quantitative basis for assessing the trade spillover effects of climate policies and the evolution of global production networks. Full article

The increasing penetration of variable renewable energy requires industrial facilities to manage energy supply, production flexibility, and cost exposure simultaneously. This study develops a risk-aware model for the co-optimization of human–cobot production scheduling, on-site photovoltaic generation, and battery energy storage within a digital-twin-ready decision environment. The artifact is an offline prescriptive model; it is not connected to a physical production system and does not include automatic or real-time data exchange. A two-stage computational experiment was conducted using mixed-integer linear programming with conditional value-at-risk and an out-of-sample Monte Carlo simulation comprising 1000 independent daily scenarios of electricity prices, photovoltaic generation, and grid carbon intensity. Five strategies were compared: grid-only supply, photovoltaic generation, photovoltaics with battery storage, risk-neutral co-optimization, and risk-aware co-optimization. Relative to the modeled baseline, the risk-neutral variant reduced mean daily operating cost by 59.2%, operational emissions by 46.1%, and grid-energy use by 48.3%, without delaying production. The risk-aware variant reduced the CVaR95 of daily cost by a further 2.6% relative to the risk-neutral solution, at a 4.2% increase in mean cost. These percentages are scenario-specific operational effects rather than plant-level, annual, or life-cycle estimates. A screening-level investment assessment indicates that economic viability remains contingent on site-specific capital costs, operating days, tariffs, and implementation expenditure. The findings show that scheduling flexibility is a resource of energy transition, while risk aversion should be calibrated explicitly as a managerial decision. Full article

In cold-region industrial parks, prolonged heating seasons and intensive hot water demands trigger severe temporal mismatches between stochastic renewable generation and rigid thermal requirements. To address this, a multidimensional synergistic optimization framework for industrial thermal energy systems is proposed. The physical architecture integrates wind, solar, and shallow geothermal energy with hybrid storage, establishing thermodynamic boundaries. Concurrently, a customized solver is developed for the coupled electro-thermal scheduling problem. At its core, the active load restructuring strategy (ALRS) exploits the thermal inertia of thermal storage tanks and leverages the high coefficient of performance of ground source heat pumps. ALRS restructures the all-day hot water supply load to nighttime windows characterized by abundant wind power and off-peak tariffs, achieving profound source–load temporal decoupling and transforming rigid thermal demands into cross-period virtual flexible assets. Assessments demonstrate that the proposed strategy reduces typical-day electricity costs by 82.51%. Compared to a grid-dependent rigid baseline, comprehensive daily operational and carbon emission costs decrease by 71.9% and 90.2%, respectively. This study demonstrates the potential of translating thermal flexibility into coordinated energy management and provides a system-level reference for the low-carbon operation of industrial thermal energy systems. Full article

Import-dependent megacities must decarbonize while accommodating distributed photovoltaics (PV) under constrained local-generation and interchange boundaries. This study develops a full-year, hourly, 48 h rolling mixed-integer linear programming model for coordinated city-level scheduling in Guangzhou, China. The VPP-controlled portfolio includes distributed PV, grid- and user-side battery energy storage systems, demand response, and aggregated electric vehicles, whereas local generators and inter-regional imports are represented as system-operator coordination variables. After correcting the energy balances of flexible resources and recalibrating the hourly interchange boundary, the baseline scheduled-import share is 68.68%, only 0.64 percentage points above the measured schedule. Across four policy-defined PV anchors, combined annual operating-and-carbon cost decreases from CNY 75.01 to 65.50 billion, carbon intensity declines from 467.9 to 412.2 g CO₂/kWh, and import share falls from 68.68% to 63.92%. A dense 0–12 GW sweep at 0.5 GW intervals reveals a continuous response: curtailment first exceeds 1 MWh at 4.0 GW but remains only 0.006%, providing no evidence of a unique breakpoint. A conventional time-of-use tariff reduces import share by 0.58 percentage points while increasing carbon intensity by 1.25 g CO₂/kWh. These results are conditional on the deterministic supply construction, calibrated interchange boundary, adopted emission factors, and operating-cost scope. Full article

Emerging organic pollutants, principally antibiotics, synthetic dyes and per- and polyfluoroalkyl substances (PFAS), persist in aquatic systems because of chemical stability, incomplete metabolic transformation and limited removal in conventional biological and physical treatment. Hybrid nanocatalysts coupled with advanced oxidation processes (AOPs) and, for PFAS, advanced reduction processes (ARPs) are the principal research responses. Four architectures dominate the recent literature: hollow nanostructures that improve photon utilization and shorten carrier diffusion paths; metal-free floatable composites that ease recovery and surface oxygenation; carbon-nanostructure heterojunctions; and nanomaterial-reinforced membranes that combine rejection with catalytic degradation. These materials are most often paired with visible-light or solar photocatalysis and with peroxymonosulfate (PMS) activation. Laboratory parent-compound removals commonly exceed 80–95% in synthetic single-solute solutions, yet total organic carbon (TOC) mineralization, reusability after documented regeneration, and energy or cost metrics remain sparsely reported. Electrical energy per order (EE/O) for laboratory photocatalysis is frequently greater than 100 kWh m⁻³ order⁻¹, whereas optimized ultraviolet/hydrogen peroxide (UV/H₂O₂) systems can approach 0.06 kWh m⁻³ order⁻¹ at full scale. Reported PMS treatment costs for real chemical-industry wastewater span 0.43–4.66 € kg⁻¹ chemical oxygen demand (COD) under the tariff assumed by the source study. Reactor evidence is dominated by short-duration batch slurry vessels and laboratory membrane modules; continuous-flow and multi-month operational data are scarce. This review maps design evidence, performance data and process configurations in the peer-reviewed literature from 2010 to 2026. It integrates a bibliometric reading of publication trends and keyword evolution, distinguishes parent-compound removal from TOC mineralization and from fluoride release, and identifies the experimental and sustainability gaps that currently limit confident extrapolation to engineered treatment systems. Full article

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

Residential electricity demand in Bahrain is strongly shaped by long cooling seasons, high air-conditioning dependence and the operation of standardised housing typologies. This paper evaluates the energy and comfort implications of applying a controlled night-ventilation mixed-mode strategy to a representative Bahraini Ministry of Housing residential unit. The study compares two DesignBuilder/EnergyPlus scenarios: S0, a replicated closed-window baseline with split air-conditioning at a 24 °C cooling setpoint, and S1, a controlled mixed-mode case in which windows are available only during night-time periods when outdoor conditions are suitable for passive cooling. The base model is grounded in a published and validated Bahraini residential dataset containing geometry, construction properties, schedules, HVAC assumptions and monthly electricity bills, and was cross-checked against this dataset (see Methods for the monthly error statistics used to qualify this comparison). The replicated baseline produced 34,729.46 kWh/year, which differs from the published reference value of 34,720.19 kWh/year by only 0.027%. The S1 case reduced annual simulated electricity consumption to 33,593.04 kWh/year, corresponding to a saving of 1136.42 kWh/year, or 3.27% relative to S0. The benefit was highly seasonal: January, February, March and December accounted for 96.2% of the annual saving, while May to September showed no meaningful reduction. Hourly January outputs showed 425 zero-cooling hours out of 744 h (57.1%), cooling electricity of 87.32 kWh and a mean operative temperature of 23.81 °C. January thermal-comfort indicators (mean PMV +0.94; only 0.5% of hours within the ±0.5 PMV band) show that the strategy delivers real electricity savings but does not, on the PMV metric, guarantee acceptable comfort in every hour; the strategy is therefore best understood as a seasonal, humidity-aware operational measure that reduces cooling electricity in cooler months while carrying a thermal-comfort trade-off that requires either a tighter control-temperature threshold or explicit acceptance of a wider comfort range. Because the strategy yields no savings during the peak summer months, it should be positioned as a complement to—not a substitute for—envelope and HVAC-efficiency upgrades, and its economic case under Bahrain’s subsidised electricity tariff is modest and is presented here as indicative potential rather than a demonstrated payback benefit. By reducing residential cooling electricity without requiring building-fabric retrofit, the findings support the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities) and SDG 13 (climate action), by contributing to sustainable, low-carbon operation of the residential building stock in hot–arid Gulf climates. Full article

South Korea faces an imminent spent nuclear fuel (SNF) storage bottleneck that threatens reactor operations and the country’s low-carbon transition. Using data from a nationwide, stratified face-to-face survey conducted in late 2024, this study examines public acceptance of an interim SNF storage facility (ISF) in South Korea. The survey included 1000 adults aged 20–65. Public acceptance was measured using a 1–9 support scale. We investigate its associations with respondents’ socioeconomic, regional, and attitudinal characteristics using ordered probit and ordinary least squares specifications. Opposition (47.4%) exceeds support (35.4%). In the preferred baseline ordered probit model, acceptance is positively associated with residence in the Seoul Metropolitan Area, self-reported familiarity with SNF management, stated tariff-cost-sharing orientation, age, education, income, and household headship, and negatively associated with province-level residence in an area containing one or more NPPs and with life satisfaction. Partial ordinal models indicate that the statistical significance of Education, Income, and Satisfaction is not robust across specifications. This study is grounded in the broader literature on procedural fairness, information asymmetry, and distributive equity. Within this framework, the observed associations inform a set of policy considerations. These considerations span transparent risk communication, tariff-linked cost sharing, independent monitoring, and community-benefit arrangements. This nationally representative evidence informs sustainable SNF governance and practical regulatory design for maintaining electricity reliability during the energy transition. Full article

The widespread adoption of EVs is expected to intensify peak demand, stress distribution networks, and increase carbon emissions if traditional grid-based charging models continue. Therefore, this study proposes a smart-grid-connected microgrid (MG) architecture for EV charging stations (EVCSs) that integrates renewables and energy storage, while incorporating incentive-based response (iDR) and net energy metering schemes into a single optimization framework. To maintain the quality of EV charging services, optimize grid interactions and MG reliability, and explore the socioeconomic impact of establishing such infrastructures, a comprehensive 4E optimization framework incorporating energy, environmental, employment, and economic metrics is developed. The suggested framework is applied to an urban case study in Riyadh, including realistic load profiles, tariff structures, EV charging behavior, network outage scenarios, NEM conditions, and an assumed iDR incentive scenario. A grid-dependent BES/Conv/Grid configuration optimized without iDR is adopted as the common reference for consistently evaluating all system configurations. Compared with this common reference, the results show that while renewable hybridization improves performance, the assumed iDR scenario provides further economic and operational benefits. The optimal iDR-enabled configuration achieves an 87.5% reduction in TNPC and a standard HOMER Grid LCOE of $0.0287/kWh, corresponding to a 67% reduction relative to the common grid-dependent reference. When electricity exports are excluded from the LCOE normalization, the corresponding load-serving LCOE is approximately $0.0372/kWh, which remains approximately 57.3% lower than the reference value of $0.0872/kWh. In addition, the optimal MG generates $63,128 in revenue/year through participation in iDR events without degrading EV charging service performance relative to the non-iDR scenario. Employment analysis indicates that the optimal system supports approximately 49 job-years of direct project-associated employment over the 25-year project lifetime through infrastructure deployment, operation, and maintenance, while ecologically, it limits annual grid-related CO₂ emissions to approximately 280.18 tons, representing about an 84.7% reduction relative to the reference scenario. These findings confirm that coordinated demand-side flexibility and intelligent storage dispatch can partially substitute for infrastructure oversizing, enabling cost-effective, low-carbon, and investor-attractive EVCS-based MGs aligned with sustainability targets. Full article

Photovoltaic (PV) deployment is expanding rapidly, but the climate and economic value of identical generation varies with electricity-system and market conditions. This study examines whether avoided-emission rankings align with project-level financial valuation when physical project characteristics are held constant. A documented 1.101 MWp PV project, with expected annual generation of 1592.6 MWh/year, was assessed counterfactually across seven regional electricity groupings, with Brazil as the reference case. The framework integrates generation-weighted emission factors, avoided-emission accounting, discounted cash flow, carbon-abatement costs, deterministic sensitivity analysis, Monte Carlo simulation, and exploratory Pearson and Spearman correlations with exact permutation inference. To address the physical variability of the reference asset, an auxiliary hourly PV simulation covering ten complete years (2016–2025) was additionally performed using solar irradiance, ambient temperature, and wind-speed data. The modeled annual generation averaged 1472.4 MWh/year, with a coefficient of variation of 1.44%, indicating limited interannual variability over the evaluated decade. Financial indicators were evaluated over a 20-year horizon, whereas lifetime avoided emissions and carbon-abatement costs were assessed over a 25-year technical lifetime. The Arab League presented the highest avoided emissions (1005 tCO₂/year; 6.1 times Brazil) but the weakest base-case financial result: a net abatement cost of approximately USD 7/tCO₂ and a 70.1% probability of positive NPV. The European Union achieved the highest NPV, a net abatement cost of approximately −USD 255/tCO₂, and a 100% probability of positive NPV despite lower mitigation. An inverse association between emission factor and electricity tariff further characterized this mismatch (Spearman’s $\rho =-0.786$; exact permutation $p=0.048$). The results show that physical PV production, mitigation potential, and project-level financial valuation represent distinct but complementary dimensions that should be assessed jointly when comparing PV deployment contexts. Full article

Five pathways—diesel, renewable hydrogen, green ammonia, Fischer–Tropsch e-diesel, and B100 waste cooking oil fatty acid methyl ester (WCO-FAME)—were compared on a common service basis using a reduced-order engine exergy model, a partial techno-economic cost model, and 10,000 Latin hypercube simulation samples; each alternative was paired with diesel by sample index. The results were normalised to 1 kWh of net brake work (kWh_b) and 1000 tonne-kilometres of freight work (1000 tkm). A carbon balance back-calculation from European Commission Vehicle Energy Consumption Calculation Tool (VECTO) monitoring data for subgroup 5-Long Haul (5-LH; a 4 × 2 tractor–semitrailer) yielded 89.31 kWh_b/1000 tkm; first-order payload penalties increased this requirement to 94.79 kWh_b/1000 tkm for hydrogen and 91.97 kWh_b/1000 tkm for ammonia. Deterministic central-case exergy efficiencies were 43.02% for hydrogen, 39.67% for e-diesel, 39.35% for diesel, 37.56% for B100, and 33.81% for ammonia. The study-specific partial cost was expressed in 2025 EUR per 1000 tkm and included delivered fuel, maintenance, and annualised incremental vehicle capital; common fleet costs, including driver, tyre, toll, insurance, and tax costs, and externalities were excluded. Therefore, this metric was not a full freight cost or tariff. Deterministic central-case costs were EUR 25.85/1000 tkm for diesel, EUR 26.72/1000 tkm for B100, EUR 58.64/1000 tkm for hydrogen, EUR 59.84/1000 tkm for ammonia, and EUR 67.44/1000 tkm for e-diesel. Hydrogen exceeded diesel in exergy efficiency in 96.50% of the paired simulation samples; all three electrofuel pathways were costlier than diesel in every paired simulation sample, whereas B100 was cheaper than diesel in 35.62% of the paired simulation samples. Thus, hydrogen led in exergy efficiency, diesel in cost under the defined partial economic boundary, and B100 was the closest cost competitor. These are conditional model-screening results rather than a completed comparative life-cycle assessment: no quantitative life-cycle impact assessment (LCIA), environmental ranking or marginal abatement cost result is reported. Full article

This paper examines how the body of research and innovation on electric cooking for low- and middle-income countries has evolved to the extent that electric cooking can now be argued to have the potential to influence energy system performance. Methods: The paper synthesises recent evidence on electric cooking from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly argued to be acting as a system-strengthening source of demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption can improve utility and mini-grid viability and support further investment, and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which has great potential to improve load factors and align demand with generation. However, a persistent planning blind spot remains, with growth in electric cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward tangible system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should increasingly be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential. Full article