Search Results (679)

With the increasing penetration of wind and photovoltaic generation, microgrids are playing an increasingly important role in promoting renewable energy accommodation, enhancing operational flexibility, and enabling low-carbon energy management. However, the strong uncertainty of renewable generation and load demand, together with the coupling effects of electricity, hydrogen, and carbon markets, poses significant challenges to the optimal planning and operation of microgrid energy storage systems. To address these issues, this paper proposes a bi-level optimal sizing framework for an electric–hydrogen hybrid energy storage system (EHH-ESS) in a microgrid under multi-market coupling. First, typical wind–solar–load scenarios are generated using a Wasserstein generative adversarial network with gradient penalty (WGAN-GP), so as to capture the stochastic characteristics and temporal correlations of renewable generation and load demand. Then, a multi-market coupling index (MCI), integrating electricity price, hydrogen price, and carbon price signals, is constructed to characterize time-varying economic and low-carbon operating incentives and to guide coordinated dispatch decisions. On this basis, a bi-level multi-objective optimization model is established. The upper level determines the optimal capacities of battery storage, electrolyzers, fuel cells, and hydrogen tanks, while the lower level performs hourly coordinated operation of the microgrid under multi-market conditions. The model considers annual equivalent total cost, renewable energy curtailment rate, and carbon emissions as objective functions, and is solved using the NSGA-III algorithm. Compared with the no-storage benchmark, the proposed scheme improves the annual operating economics and renewable-energy accommodation under the studied market conditions. The proposed method significantly reduces annual operating cost and improves renewable energy accommodation. However, under the current carbon price and grid emission factor settings, the optimal economic solution increases carbon emissions relative to the baseline, indicating a trade-off between economic arbitrage and low-carbon operation. Full article

Rapid growth in photovoltaic (PV) development has intensified competition between renewable energy expansion and land resources, highlighting the importance of integrating PV growth with spatial planning toward carbon neutrality. This study addresses the lack of township-level evidence by integrating spatio-temporal analysis, the Optimal Parameter Geodetector (OPGD), and the Patch-generating Land Use Simulation (PLUS) model to investigate the evolution, driving mechanisms, and future spatial patterns of PV development. Taking Xintai City as a case study, this research examines PV development from 2010 to 2022 and simulates future spatial patterns under natural development (ND) and carbon neutrality (CN) scenarios. Results show that PV development entered a rapid expansion stage after 2015, characterized by leapfrog growth and increasing spatial agglomeration. The centroid of PV patches shifted eastward, whereas the centroid of PV area migrated westward, indicating differentiated spatial evolution between the number and scale of PV facilities. PV development was jointly shaped by climatic conditions, socioeconomic factors, and land availability, with interactions among multiple factors substantially enhancing spatial heterogeneity. Scenario simulations revealed that PV development continued under both scenarios, while the CN scenario promoted more concentrated expansion in coal subsidence areas and inefficient industrial and mining lands, thereby helping alleviate potential conflicts with cultivated land protection. These findings highlight the importance of prioritizing low-conflict land resources and strengthening spatial coordination between renewable energy development and land management to support sustainable low-carbon development in resource-based cities. Full article

High-purity quartz is a critical raw material for high-tech industries such as semiconductors and photovoltaics, yet its purity is severely constrained by gas–liquid inclusions within quartz crystals that are difficult to eliminate. The calcination–water quenching process is a key pretreatment step for removing inclusions and achieving deep purification, but its underlying mechanisms and the influence of process parameters on removal efficiency remain insufficiently understood. In this study, systematic calcination–water quenching experiments at different temperature gradients (500 °C, 700 °C, 900 °C, and 1100 °C) were conducted on high-purity quartz samples from Inner Mongolia and Angola. Comprehensive analytical techniques, including X-ray diffraction (XRD), major and trace element analyses, and polarizing microscopy, were employed to investigate the microstructural evolution, inclusion morphology, impurity element concentration changes, and phase transformation behavior before and after treatment. With increasing temperature, the quartz samples exhibited pronounced whitening and pulverization, accompanied by a significant reduction in the number of internal linear inclusions. Elemental analysis revealed that calcination–water quenching effectively removed certain alkali metals, alkaline-earth metals, and iron impurities, with 900 °C identified as the optimal calcination temperature; moreover, the sand-sized samples consistently showed better impurity removal efficiency than the lump-sized counterparts. XRD analysis was used to verify the phase transformation of quartz during calcination. Excessive temperatures (e.g., 1100 °C) led to a rebound in the content of some impurity elements. The calcination–water quenching process promotes inclusion decrepitation, exposure, and subsequent removal through the combined effects of volumetric strain induced by quartz phase transitions, thermal pressurization of inclusions, and thermal-shock stress from water quenching. This study establishes the optimal process window (hold at 900 °C for 2 h, sand-sized morphology) for the specific ore samples, elucidates the multi-factor synergistic mechanism of inclusion rupture, and provides both experimental and theoretical bases for the industrial purification of high-purity quartz. Full article

As the most critical component of organic solar cells (OSCs), the morphology of the active layer directly dictates the photovoltaic performance of the devices. Recent studies have demonstrated that tailoring the active layer morphology using solid additives is a facile and effective strategy to boost the performance of OSCs. Herein, we design and synthesize a novel solid additive, 5-bromobenzo[b]thiophene (5-BrBT), by introducing a bromine substituent onto the common benzothiophene unit. It is found that 5-BrBT optimizes the active layer formation process by effectively prolonging the nucleation time, which facilitates more controllable molecular nucleation and subsequent crystal growth during the pre-aggregation stage, leading to a more ideal donor-acceptor phase distribution. Furthermore, the binary PM6:L8-BO organic solar cells, fabricated with the incorporation of 5-BrBT, exhibit superior charge transport properties and exciton-generation efficiency, along with significantly suppressed charge recombination behaviors. Consequently, the 5-BrBT-treated binary PM6:L8-BO-based OSCs achieve an outstanding power conversion efficiency (PCE) of up to 19.44%, accompanied by simultaneous enhancements in short-circuit current density (J_SC) and fill factor (FF). This work provides a promising optimization strategy for achieving ideal nucleation behaviors during the bulk-heterojunction (BHJ) film processing via solid additives, which is expected to promote the development of more efficient OSCs. Full article

Photovoltaic (PV) enhancement technologies, including cooling systems, reflectors, and tracking mechanisms, are widely employed to improve the electrical performance of PV systems. However, the effectiveness of these technologies should be evaluated not only in terms of performance improvement but also by considering the associated implementation costs. To address this need, previous studies introduced the Cost Effectiveness Factor (${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$), which integrates power output and manufacturing cost into a single performance indicator. While ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$ is useful for short-term and experimental assessments, it is based on instantaneous power output and does not account for the cumulative energy generated over extended operating periods. Furthermore, many experimental and field studies on PV enhancement technologies, particularly PV cooling systems, report their performance in terms of energy generation (kWh) rather than instantaneous power output (W), creating a need for an energy-based assessment methodology. Therefore, this study proposes a new Cost–Energy Effectiveness Factor (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$) that extends the concept of ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$ by incorporating energy output instead of power output, thereby enabling a more comprehensive evaluation of long-term techno-economic performance. The proposed indicator integrates the output energy of PV systems with and without enhancers, the manufacturing cost of PV enhancers, and the unit cost of PV electricity into a single dimensionless factor. In addition, a theoretical minimum value (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$) is introduced to establish a benchmark for performance evaluation. Comprehensive sensitivity analyses were performed to investigate the influence of key technical and economic parameters, including the output energy of the enhanced and unenhanced PV systems, manufacturing cost, the unit PV electricity cost, and maximum output power under standard test conditions. The results indicate that ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ decreases with increasing enhanced PV energy output and electricity value; however, it increases with higher manufacturing costs and greater energy production from the reference PV system. In contrast, variations in the maximum output power affect only the benchmark value (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$) without influencing the actual ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ values. The proposed indicator was further validated using data obtained from real photovoltaic cooling systems, demonstrating its applicability under practical operating conditions and confirming its suitability for real-world PV enhancement scenarios. Compared with ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$, the proposed ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ provides a more realistic representation of the long-term benefits of PV enhancement technologies because it evaluates accumulated energy generation rather than instantaneous power output. The indicator successfully differentiates between effective, neutral, and ineffective PV enhancers and offers a practical tool for researchers, designers, manufacturers, and investors seeking to compare PV enhancement technologies from both energy and economic perspectives. Consequently, ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ can serve as an effective preliminary screening and comparative assessment tool for PV enhancement technologies, thereby promoting the efficient utilization of sustainable energy resources, while detailed investment decisions should be supported by comprehensive techno-economic analyses that consider lifecycle costs, discount rates, financing conditions, and other project-specific economic factors. Full article

Clarifying how vegetation restoration regulates wind erosion, sediment redistribution, and soil improvement is essential for ecological management in desert photovoltaic power stations. This study was conducted in a high-clearance flexible-support photovoltaic power station at the edge of the Kubuqi Desert. Three restoration measures were compared: reed mulch combined with Atriplex canescens planting along the panel front edge (M1), A. canescens planting along the panel front edge alone (M2), and reed mulch combined with grass seeding (M3). The panel front-edge zone (QY), under-panel zone (BX), and pedestal zone (JZ) were used as functional units to analyze surface sediment grain-size characteristics, soil moisture, soil nutrients, windbreak efficiency, aerodynamic roughness length, and cumulative sand-fixing efficiency. All restoration measures altered the surface sediment structure, with Mz ranging from 2.005 to 2.364 and D₀ from 1.459 to 1.935. Soil moisture ranged from 0.58% to 4.34%, with the highest value occurring in the 20–30 cm layer of QY under M1. M1 also showed higher soil organic matter in QY and JZ, reaching 1.87 and 1.16 g·kg⁻¹, respectively. Windbreak efficiency decreased with height under all measures. M1 maintained the highest and most stable values, decreasing only from 61.16% at 10 cm to 55.52% at 100 cm. The total cumulative sand-fixing efficiency was also highest under M1 (233.66%), while M2 (215.05%) and M3 (214.58%) showed comparable total effects but different zonal responses. Wind-eroded materials shifted from fine-sand dominance toward a higher relative contribution of medium sand, reflecting the reduction in finer transported fractions rather than true grain coarsening. The novelty of this study lies in linking wind-erodible sediment redistribution, soil water and nutrient responses, and windbreak–sand-fixing performance across internal functional zones of a flexible-support photovoltaic array. These results indicate that vegetation restoration in desert photovoltaic power stations should be configured by functional zone, with composite interception at the panel front edge, structural maintenance in the under-panel zone, and cover-based sand trapping in deposition-prone areas. Full article

High electricity prices remain a substantial barrier to electric vehicle (EV) diffusion. To address this challenge, we propose a bundled sales model that integrates EVs with distributed, operationally off-grid photovoltaic (PV) systems for self-consumption. Using a sequential game-theoretic framework and scenario analysis calibrated to U.S. and German data, we show that, within the calibrated scenarios and declared system boundaries, bundling accelerates EV adoption and reduces modeled oil dependency, measured as the physical volume of fossil fuel displaced by the bundled fleet. In Germany, bundling increases oil-dependency reduction by 7.8 percentage points, to 34.5%, relative to the traditional unbundled model. The bundled model also delivers stronger decarbonization, yielding incremental lifecycle emission reductions of 11% in the U.S. and 29% in Germany under the declared system boundary. Three insights follow. First, bundling is especially advantageous in markets with high grid tariffs, strong solar irradiance, or falling PV costs. Second, decoupling EV charging from carbon-intensive grids promotes household energy self-sufficiency and helps households become more resilient energy prosumers. Third, the threshold analysis indicates that the model is already viable in high-tariff markets such as Germany, while declining battery costs are likely to trigger a tipping point in lower-tariff markets such as the U.S., supporting a gradual diffusion pattern from suburbs to cities. These findings identify a viable pathway for low-carbon transport transitions through synergistic EV–solar integration. Full article

Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing the sustainability and resilience of energy systems, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study analyzes the Italian electricity market and assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. The optimization problem is formulated in physical terms, aiming to maximize locally utilized energy, defined as the sum of self-consumed and shared photovoltaic generation. This choice reflects the structure of the Italian regulatory framework, where incentives are directly linked to the amount of energy shared within the community. In this context, energy-based optimization is preferred to avoid embedding assumptions on discount rates, investment horizons, and financing conditions, which may vary significantly across users and introduce additional uncertainty. From a sustainability perspective, maximizing local energy utilization contributes to improving energy efficiency, reducing reliance on external energy sources, and enhancing the capacity of decentralized systems to absorb market shocks. For this reason, economic indicators such as Net Present Value (NPV) or payback period are not explicitly included in the optimization objective. This is justified by the focus of the analysis on short-term operational performance and exposure to electricity price volatility, rather than long-term investment evaluation. Moreover, given that the economic value of the REC is largely determined by shared energy volumes under the current Italian incentive scheme, maximizing local energy utilization provides a consistent proxy for economic performance. Nevertheless, the integration of financial metrics such as NPV or payback period represents a relevant extension for future research, particularly in the context of investment decision-making. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations. Results show that RECs reduce price sensitivity by approximately 8–15% compared to individual users, as estimated by panel regression coefficients. Furthermore, the volatility of economic value decreases by around 1.95% under the community configuration, particularly during the 2022 price shock demonstrating that RECs exhibit significantly lower price dependence than standalone consumers. To assess the robustness of these findings, a machine learning framework is employed to relax linearity assumptions and capture potential non-linear effects. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the coefficient of variation decreases by approximately 4.4% under the post-reform incentive scheme, highlighting the role of policy design in supporting economically and operationally sustainable energy communities. Overall, this study develops a data-driven analysis based on a high-frequency synthetic dataset designed to reproduce realistic consumption and generation dynamics, providing robust evidence that RECs contribute not only to renewable energy deployment but also to the economic and systemic sustainability of electricity markets under conditions of high volatility. Full article

This study presents a mathematics-based simulation model for designing, analyzing, and optimizing offshore green hydrogen stations powered by solar photovoltaic systems, applicable to any location worldwide. Developed in Python, the model integrates environmental, physical, and technological parameters to simulate and forecast hydrogen production via water electrolysis using alkaline (ALK) or proton exchange membrane (PEM) electrolyzers, combined with an adiabatic compressor that enhances energy storage and facilitates integration into smart grids. The five-phase modular methodology includes timeframe definition; estimation of solar electricity generation based on solar trajectory and the geographic orientation of photovoltaic panels; performance modeling of electrolyzers and compressors; and the integration of all components into a cohesive system. A case study demonstrates the model’s real-world applicability. Results from the Gulf of Cadiz case study show a substantial increase in solar energy capture in offshore environments due to reduced atmospheric pollution and sea-surface reflection. The reflected component is modeled as a function of sea-surface flatness. This reflection increases the daily average solar irradiance received by the photovoltaic panels by 8.44%. Under the modeled 2026 conditions and equivalent irradiance levels, the ALK electrolyzer produces 3.347% more hydrogen than the PEM electrolyzer. In addition, a 20% increase in electrolyzer efficiency raises hydrogen production by 32.35%, whereas the same increase in compressor efficiency improves production by 0.758%. These impacts directly correlate with proportional reductions in the photovoltaic panel surface area, driven by increased electricity generation capacity, which translates into smaller infrastructure needs. The model enables quantitative evaluation of trade-offs among solar irradiance, component performance, and system design. It supports cost reduction through optimized sizing and improved integration. This approach contributes to lowering the Levelized Cost of Electricity (LCOE) and promoting the viability of marine-based green hydrogen deployment. Full article

Energy Strategy 2050 promotes photovoltaic (PV) deployment to reduce fossil fuel dependence in Switzerland. However, limited available land constrains conventional solar farms, motivating the deployment of linear PV (LPV) systems along transport corridors such as highways or railways. This paper contributes to a systematic design and modeling methodology for an LPV power plant interconnected through a medium-voltage direct current (MVDC) collection network. The main methodological contribution is the development of a modified iterative modified nodal analysis (MNA) framework tailored to LPV–MVDC systems. In long-distance feeders with high line impedance, nonlinear voltage–current coupling becomes significant. These nonlinearities cannot be accurately captured by conventional MNA assuming fixed current injections. The proposed iterative approach can more accurately capture these effects compared to conventional MNA. A case study of a 5 km railway-based LPV system is investigated to present the design and modeling methodology, including layout design, network modeling, and cable sizing. The LPV power plant reaches a peak power of 3.4 MW and requires 40 DC/DC converter stations rated at 100 kW each. Cable analysis shows that 6 mm² copper conductors satisfy voltage drop limits at a string level, while 95 mm² conductors maintain MVDC voltage variations within 3%. These results highlight technical feasibility of MVDC-based integration for efficient long-distance renewable energy distribution. Full article

The energy management (EM) of smart multi-carrier energy hub (SMCEH) systems for cost and emission reduction remains a challenging problem due to the diversity of renewable energy resources (RERs), varying load demands, and the stochastic nature of these resources. This paper addresses the EM problem of SMCEHs to minimize operational costs and greenhouse gas (GHG) emissions using the particle swarm optimization (PSO) algorithm. The studied SMCEHs are designed to simultaneously supply electrical, cooling, and thermal demands. The hub system comprises wind turbines (WTs), photovoltaic (PV) panels, gas turbines (GT), electric chillers (EC), gas boilers (GBs), absorption chillers (AC), battery storage systems, and thermal storage units. To assess system performance and the impact of key technologies, three case studies are investigated: (i) EM of SMCEHs without RERs, (ii) EM of SMCEHs with RERs, and (iii) EM of SMCEHs with RERs and an integrated carbon capture unit (CCU). These scenarios enable a systematic evaluation of the role of renewable integration and carbon capture in enhancing system performance. The results demonstrate that incorporating RERs into SMCEHs leads to a substantial reduction in both operational costs and GHG emissions. Furthermore, the integration of a CCU provides additional emission reductions, underscoring its effectiveness in supporting the low-carbon operation of SMCEHs. The obtained results show that integrating RERs into SMCEH decreases the total cost and emissions by 64.12% and 7.95%, respectively, compared to the scenario without RERs. Furthermore, the integration of the CCU into SMCEHs provides a 39.36% reduction in total costs and a 72.57% decrease in CO₂ emissions. The suggested energy management solution promotes a sustainable and low-carbon emission system by maximum utilization of the RERs and CCU. Full article

Tin–lead halide perovskite is a promising narrow-bandgap absorber for high-performance perovskite solar cells. However, the easy oxidation of Sn²⁺ and the resulting defect formation still limit these films’ quality and photovoltaic performance. Tin fluoride (SnF₂) is widely used as an antioxidant additive in Sn-containing perovskites, but its optimal concentration remains strongly dependent on the specific perovskite composition. Herein, we systematically investigate the influence of SnF₂ concentration on the film quality and device performance of methylammonium-free Sn–Pb perovskite solar cells. By varying the SnF₂ content from 0 to 15% relative to SnI₂, we find that an appropriate amount of SnF₂ can effectively improve the surface morphology, enhance crystallinity, promote preferred crystal orientation, and suppress defect-assisted non-radiative recombination. In particular, the film with 10% SnF₂ exhibits the smoothest surface, with a reduced root-mean-square roughness, enhanced photoluminescence intensity, and a lower trap density compared with the control films. As a result, the optimized device delivers a champion power conversion efficiency of 19.15%, significantly outperforming the control device. This work demonstrates the importance of SnF₂ concentration optimization and provides a useful guideline for improving MA-free Sn–Pb perovskite solar cells. Full article

Global requirement of clean, cost-effective and sustainable energy has stimulated massive research and development in photocatalytic materials that have the potential to harvest solar based energy while mitigating the environmental issues. Among various materials, perovskite oxides have emerged as a promising energy resource. Owing to the structural versatility, optical and electrical properties, chemical inertness allows the use of material of multifunctional prospects. Currently Strontium titanate (SrTiO₃), a vital perovskite oxide having a band gap nearly ~3.2 eV, is showing significant function for photocatalytic water splitting, carbon dioxide conversion and degradation of organic pollutants. Though within the UV spectrum, its intrinsic photocatalytic behavior is limited to approaches such as graphene junctions, noble-metal support, and post-synthetic heat treatment seem to promote the adsorption within visible-light. Strontium titanate also demonstrates photo charge separation efficiency, and long-term catalytic durability. Moreover, modifications and hydrothermal synthesis have proven extremely efficient for nano-based engineering, control over crystal diameter, defects, and shape, which can result in magnificent composites that can be promising substitutes. Therefore, further research is imperative regarding these material application prospects. This comprehensive review provides insights into details on the potential of nanoengineering and composite approaches to reduce the inherent limitations of perovskite oxides, especially Strontium titanate, and enabling additional applications in next-generation photovoltaic and solar energy harvesting technologies. Full article

We present a transdisciplinary Teaching–Learning Sequence (TLS) for lower secondary school students that integrates climate change education with the promotion of gender equality in science. The TLS connects theoretical understanding with practical engagement through laboratory demonstrations, simulations, and accessible experiments. The sequence addresses key topics in sustainability education, including incoming and outgoing radiation, the greenhouse effect, energy transformations, and energy sources, through activities involving the electromagnetic spectrum, infrared imaging, selective transparency, absorption, and albedo. It also includes inquiry-based explorations of electromagnetic induction, miniature hydroelectric and wind power systems, Stirling engines, photovoltaic and concentrated solar technologies, and combustion-related CO₂ acidification. A distinctive feature of the TLS is the explicit integration of the social dimension of sustainability through discussion of the Matilda Effect and the historical case of Eunice Newton Foote, with the aim of challenging persistent gender stereotypes in STEM. The intervention was implemented with 12–13-year-old students and evaluated through pre- and post-tests, written explanations, closed-ended questions, drawings, and the Draw-A-Scientist Test. The results indicate a significant improvement in students’ understanding of climate-related scientific concepts and in their critical awareness of misinformation and climate denial strategies. While the sequence did not significantly increase students’ engagement in climate action, the gender-focused activities promoted strong critical reflection on stereotypes and on the role of women in science. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article