The study aimed to justify technical, organizational and digital solutions for improving the stability and reliability of Ukraine’s electricity distribution networks during recovery from large-scale war damage. A reconstruction-oriented analytical framework was applied that combined regional damage and network-loading indicators, System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) reliability metrics, renewable-energy integration, scenario-based assessment of energy storage systems (ESS), and quantitative international benchmarking of Smart Grid technologies. The analysis covered the Kyiv, Kharkiv and Lviv regions and used official regulatory data, distribution system operator materials and international reports for 2022-2025. Recorded damage to 0.4-10 kV power lines decreased from 7.8 thousand km in 2022 to 2.4 thousand km in 2025, while the number of damaged transformer substations declined from 340 to 120. Nevertheless, regional capacity utilization remained high, particularly in the Kyiv and Kharkiv regions. In 2022, SAIDI reached 1,200 min/consumer/year and SAIFI 4.1 outages/consumer/year, approximately 8.9 and 4.1 times the corresponding EU averages, respectively. By 2025, these indicators had declined to 850 min/consumer/year and 3.2 outages/consumer/year but remained approximately 7.1 and 3.6 times the corresponding EU averages. Scenario analysis showed that ESS support equivalent to 10% of regional load could reduce network utilization by approximately 8-9 percentage points in the most heavily loaded regions, while sensitivity analysis across the 5-15% support range confirmed the same direction of effect. International Smart Grid benchmarks indicated potential reductions in SAIDI of 29-38% and SAIFI of 18-28%, although these values were treated as comparative benchmarks rather than direct forecasts for Ukraine. The findings indicate that post-war reconstruction should combine physical restoration with increased redundancy, ESS deployment, automated control, secure digital monitoring and region-specific modernization priorities.
The efficient conversion of lignocellulosic biomass into fermentable sugars remains one of the main challenges for sustainable biorefineries. This study tested the hypothesis that dilute-acid hydrolysis of Eichhornia crassipes can be optimized to maximize fermentable sugar release while elucidating the physicochemical mechanisms governing the process. A Box–Behnken response surface design was employed to evaluate the effects of temperature (110–130 °C), sulfuric acid concentration (1–3%), and solid-to-liquid ratio (1:5–1:9) on total sugar production. Statistical analyses demonstrated that temperature was the dominant factor controlling hydrolysis, whereas acid concentration and solid-to-liquid ratio regulated catalytic selectivity and mass-transfer limitations. The quadratic model adequately described the experimental response (R2 = 0.81), and its mechanistic interpretation revealed that sugar production results from the competition between polysaccharide depolymerization and monosaccharide degradation. Experimental validation confirmed the robustness of the optimized conditions, with an average prediction error of 15% and a mean sugar concentration of 79.5 ± 6.31 g·L-1. Mass balance analysis showed an average recovery of 0.636 g of soluble sugars, corresponding to 47.1% of the theoretical structural carbohydrate potential.
This research investigates the techno-economic feasibility of an energy management system (EMS) for a grid-tied photovoltaic-battery energy storage system (PV-BESS) prosumer at a university campus. The study introduces an advanced Optimization-Based Control (OBC) framework, comparing Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) against conventional Rule-Based Control (RBC). A primary contribution is the systematic simultaneous parameter-tuning framework, utilizing a grid-search methodology with 30 independent simulations per configuration to ensure statistically reliable BESS scheduling. Results demonstrate that OBC-PSO and OBC-GA achieved annual net profit improvements of 13.50% and 11.72%, respectively, by effectively leveraging Time-of-Use (TOU) arbitrage. Furthermore, precise optimization significantly mitigated battery degradation, extending the replacement cycle from 8 to 10 years and drastically reducing long-term capital expenditure. A 25-year lifecycle analysis reveals that OBC-PSO attains a maximum Net Present Value (NPV) of 97,639 USD, representing a 98.57% improvement over RBC, while reducing the discounted payback period from 11.71 to 6.74 years. The Levelized Cost of Energy (LCOE) was also reduced from 0.085 to 0.078 USD/kWh. Sensitivity analysis confirms that the proposed tuning-centric OBC approach provides a robust financial buffer against electricity price volatility and BESS cost fluctuations. These findings underscore that rigorous algorithm configuration and initialization are essential for enhancing the commercial viability and reliability of grid-tied prosumer systems in modern smart cities.
As the photovoltaic penetration rate continues to increase, the dynamic characteristics of the multi-dimensional coupling of source, grid, load and storage in the distribution network have become increasingly significant. Traditional control methods often face response lag and regulation instability in this multi-time-varying scenario. Therefore, the study proposes a source-grid-load-storage multi-terminal collaborative control strategy for photovoltaic distribution network. A centralized and distributed two-level collaborative architecture is established to enable deep reinforcement learning agents to learn the optimal power regulation strategy. The multi-time scale control of voltage regulator taps, photovoltaic, energy storage systems, and static reactive power compensation devices is coordinated to achieve voltage optimization and efficient energy allocation. A comparative test was conducted using the IEEE 33-node system as the simulation platform. The results showed that the proposed method could increase the peak photovoltaic output by about 20%-25% on the power generation side, and the average power generation utilization rate reached 93%. The load transfer rate increased to 26.2%, and the load balance dropped to 0.21. In terms of voltage stability, the system fluctuation was controlled within 0.15 p.u., and the steady-state error was less than 0.01 p.u. In typical operating scenarios, deep reinforcement learning control increased the energy storage utilization rate to 86.3%-88.9% and reduced the network loss rate to 3.1%-3.5%. The results show that this method is significantly better than the comparison strategy in terms of multi-source collaboration and dynamic stability, and provides an efficient and intelligent optimization solution for the safe and economical operation of high-penetration photovoltaic distribution networks.
Steel decarbonization is a global priority due to its high energy use and emissions. In Thailand, this challenge is shaped by reliance on scrap-based EAF systems, where emissions depend on carbon-intensive electricity and limited scrap availability and quality. This study evaluates the environmental competitiveness and decarbonization potential of hydrogen-based direct reduced iron (H₂-DRI) integrated with electric arc furnace (EAF) steelmaking under Thailand-specific conditions. A cradle-to-gate life cycle assessment was conducted to compare 100% scrap-based EAF with four H₂-DRI-EAF scenarios (25%, 50%, 75%, and 100% DRI) using grey, blue, and green hydrogen. The ReCiPe 2016 method was applied to evaluate global warming and endpoint impacts on human health, ecosystem quality, and resource scarcity. Results showed that the baseline scrap-based EAF has the lowest environmental burden. Among hydrogen pathways, blue hydrogen achieves the greatest climate benefits, reaching 551.51 kg CO₂-eq/t, while grey hydrogen exhibits the highest impacts across all categories. Green hydrogen does not outperform the baseline under current conditions due to upstream burdens. Furthermore, endpoint contribution analysis revealed the overall emissions profile, highlighting trade-offs between climate change and other midpoint impact categories. Results suggest that Thailand should pursue a dual-pathway steel decarbonization strategy that combines optimized scrap-based EAF production with the gradual deployment of hydrogen-based DRI-EAF to address future constraints in scrap availability and quality. Compared with conventional natural gas-based DRI-EAF, blue and green hydrogen pathways achieved greenhouse gas reductions of 58.7% and 37.6%, respectively, highlighting their potential to support Thailand’s long-term climate and industrial development objectives.
