Search Results (6,033)

Increasing demand in radial distribution systems intensifies active power losses, voltage drops, and power factor deterioration, leading to reduced network efficiency and increased annual operating costs. Shunt capacitor banks can mitigate these effects by supplying reactive power locally; however, determining their locations and discrete ratings constitutes a nonlinear optimization problem governed by the radial network structure, nonlinear load flow equations, reactive power compensation limits, power factor requirements, and commercially available capacitor sizes. This paper presents a hybrid methodology that combines Loss Sensitivity Factors (LSFs) for candidate-bus screening with the Flower Pollination Algorithm (FPA) for discrete capacitor sizing. The LSF stage ranks buses according to the local sensitivity of active power losses to reactive power variations and applies a voltage-based screening criterion to reduce the number of decision variables. The FPA subsequently explores the reduced search space through global and local pollination, while a projection-and-repair procedure maps every continuous trial vector onto admissible capacitor ratings in 50 kVAr increments. The objective function minimizes the annual cost of active power losses, capacitor bank installation, and installed reactive power capacity. The method is evaluated on the IEEE 33-bus, 69-bus, and 141-bus radial distribution systems. For the IEEE 33-bus system, active power losses decrease from 202.7 to 133.5 kW and the power factor increases from 0.8502 to 0.9827. For the IEEE 69-bus system, losses decrease from 225.0 to 145.9 kW and the power factor increases from 0.8159 to 0.9705. For the IEEE 141-bus system, losses decrease from 632.7 to 453.7 kW and the power factor increases from 0.8500 to 0.9876. The corresponding cost savings in terms of annual loss are USD 36,371.52, USD 41,574.96, and USD 94,082.40. The compensation configurations improve the voltage profiles in all three systems; nevertheless, the resulting minimum voltages of 0.939, 0.932, and 0.948 p.u. remain below the 0.95 p.u. lower reference because voltage deviation is evaluated as a performance indicator rather than enforced through a hard constraint or penalty term. The results demonstrate that the proposed LSF–FPA framework provides effective loss reduction, power factor correction, and voltage profile improvement, while the economic comparison shows that the solution with the greatest loss reduction benefit does not necessarily produce the lowest total annual cost. Full article

This study extends an established bi-level dynamic optimization framework for grid-connected regional integrated energy systems (RIES) to address the escalating renewable energy penetration (REP) within integrated power systems (IPS). While traditional models treat REP as static, our approach integrates its dynamic growth into both the design and operational scheduling phases, utilizing a genetic algorithm paired with the Gurobi solver. Applied to a case study in Changsha, the extended model is systematically benchmarked against conventional static REP scenarios. The research shows that the introduction of REP growth factors in the optimization model can increase the installation capacity of ground source heat pumps (GSHPs), reduce the installation capacity of combined heat and power units and absorption chillers, and reduce the initial investment of the system by 12.20%. Affected by the difference in equipment capacity configuration, the primary energy consumption and total cost of the grid-connected RIES decrease during the planning period, while the cumulative carbon dioxide emissions show a slight increase. The primary energy consumption and total cost under winter operating conditions were reduced by 2.44% and 2.38%, respectively. In addition, with the increase of REP growth rate in IPS, the reductions in the system’s primary energy consumption, carbon dioxide emissions, and total cost all increase accordingly. Therefore, in macroscopic RIES planning, incorporating dynamic REP reveals a clear trade-off: improving economic and energy efficiency may temporarily increase carbon emissions when renewable penetration is low. Full article

Multi-objective optimization algorithms are essential for solving complex engineering problems. However, conventional approaches often struggle with limited convergence accuracy and poor solution diversity. This paper proposes the Improved Multi-objective Artificial Lemming Algorithm (IMOALA), incorporating three novel components: an elite selection strategy, a differential-guided external archiving strategy, and a non-uniform mutation strategy. The performance of the IMOALA is benchmarked against other algorithms across twelve test functions and further validated on IEEE 30-bus and 39-bus systems to solve environmental economic dispatch that balances minimal fuel cost and pollutant emission. Results across Inverted Generational Distance (IGD), Maximum Spread (MS), and Generational Distance (GD) metrics demonstrate that the IMOALA achieves superior convergence precision and solution diversity in numerical tests. In engineering applications focusing on fuel cost and emission reduction, the IMOALA consistently yielded higher Normalized Distance (ND) and lower Spacing (SP) values compared to mainstream competitors, delivering evenly distributed Pareto trade-off solutions for cost–emission coordination. These findings verify the feasibility, robustness, and superiority of the IMOALA, offering a highly effective optimization tool for complex, multi-objective power system dispatch and broader engineering challenges. Full article

India’s rapid expansion of utility-scale solar photovoltaic (PV) capacity is increasingly constrained by aging assets and the temporal mismatch between generation and peak demand. This study develops a techno-economic framework integrating battery energy storage systems (BESSs) with repowered solar PV projects, using repowered electricity as a low-cost charging source. A capacity-based assessment estimates national repowering potential of 7.2 GWp under power purchase agreement constraints and 10.9 GWp under technical limits. The levelized cost of repowered electricity is ₹1.40/kWh, significantly lower than prevailing utility-scale solar tariffs under stated assumptions. Levelized storage costs range from ₹5.08 to ₹4.12/kWh for 2–6 h durations, declining with improved inverter and balance-of-system utilization. Financial analysis under a ₹10 per kWh peak tariff arbitrage scenario yields internal rates of return between 17.5% and 24.2%, with positive project viability across configurations. Sensitivity analysis identifies capital cost as the dominant economic driver. Environmental benefits include annual greenhouse gas reductions of 13.8–17.2 MtCO₂, accumulating to 411–514 MtCO₂ over the project lifetime. These findings demonstrate that repowering-integrated battery storage offers a cost-effective, scalable pathway to enhance renewable integration, displace fossil fuel peak generation, and support India’s low-carbon transition, highlighting a viable framework for improving system flexibility and overall system performance. Full article

Climate change has become one of the most urgent challenges of the 21st century, shaping environmental, economic, and social dynamics on a global scale. In the Mediterranean basin, temperatures have risen by 1.5 °C, with projections suggesting a further increase of 5.6 °C by 2100. The building sector represents a critical intervention point, accounting for 30% of final energy consumption and 27% of global carbon dioxide emissions. While passive strategies mitigate these impacts, current energy policies fail to consider future variations in heating and cooling demand due to climate change. Using OpenStudio and EnergyPlus, three reference models (small office, residential building, and hospital) were simulated across five Spanish climatic areas (Almería, Córdoba, Cáceres, Lleida, León). Current conditions were compared against 2050 projections under four Shared Socioeconomic Pathways (SSP1-2.6 to SSP5-8.5) utilizing the morphing method. The results revealed a systemic shift toward cooling-dominated profiles. Small office heating demand drops by 18–34%, while cooling demand increases by 80–274%. Residential heating decreases by 19–36%, while cooling increases by 85–706%. Hospitals show milder relative variations, yet cooling demand rises 64–114%. These findings, based on thermal energy demand (kWh/m²), emphasize the need for climate-responsive design and efficient retrofitting. Conclusions regarding peak power grid stress, carbon emissions, or energy poverty are indirect implications; translating thermal demand to final energy, primary energy, or emissions requires accounting for HVAC system efficiency, energy source mix, and emission factors, which are outside the scope of this study. Full article

Agricultural production faces increasing pressure to improve efficiency while reducing chemical inputs and environmental impacts. Laser-based technologies have attracted attention because their wavelength, energy input, exposure duration, and spatial delivery can be adjusted for different biological targets and operational purposes. This review provides a stage-oriented synthesis of laser-based biostimulation, optical sensing, and targeted physical control across crop production and postharvest stages, including seed treatment and seedling establishment, field growth management, ripening and quality assessment, and postharvest preservation. Rather than treating laser-based technologies as a single uniform approach, this review focuses on technical functions, key operating parameters, possible interaction mechanisms, and evidence levels. Particular attention is given to how wavelength, power density, delivered energy dose or fluence, spot size, and exposure duration affect biological responses, sensing performance, and physical treatment efficacy. Current evidence indicates that many applications remain limited to laboratory studies, controlled-environment tests, field demonstrations, or engineering prototypes. Major challenges include parameter standardization, mechanistic validation, environmental robustness, model transferability, crop or product safety, equipment cost, and system integration. Future research should emphasize mechanism-informed dose–response assessment, adaptive sensing and control, multi-source data fusion, product-specific validation, and techno-economic evaluation. Full article

To address the high dependence on diesel generation, renewable energy variability, and limited demand-side flexibility of remote islanded microgrids, this study develops a low-carbon economic dispatch framework for an islanded photovoltaic–wind–diesel–battery energy storage system with coordinated demand response. The proposed model minimizes the operating cost, pollutant treatment cost, and load-loss penalty cost while satisfying generation-output, battery state-of-charge, charging and discharging, demand-response, and islanded power-balance constraints. To solve the resulting high-dimensional, nonlinear, and strongly constrained optimization problem, a rotation quantum particle swarm optimization algorithm (RQPSO) is proposed. In contrast to the conventional velocity–position update, RQPSO independently encodes each decision variable using a full-dimensional quantum phase representation and performs the search through a shortest-path rotation-guided phase-updating mechanism. Adaptive angular mutation, elite local refinement, and stagnation-aware restart are further incorporated to balance global exploration, local exploitation, and convergence stability. The algorithm is evaluated using nine 30-dimensional benchmark functions and representative 24 h forecasted load and renewable-generation profiles for Island data. Under the reliability-priority scheduling scheme, RQPSO achieves a total scheduling cost of 69,017.69 CNY, diesel fuel consumption of 6636.20 kg, and estimated CO₂ emissions of 18,332.49 kg. Compared with conventional PSO, these three indicators are reduced by 9.34%, 12.25%, and 12.25%, respectively. RQPSO also reduces the total cost by 6.16–27.36% relative to six comparison algorithms. The results demonstrate that the coordination of demand response and battery storage can improve peak–valley regulation, reduce diesel dependence and emissions, and maintain feasible and economical operation under different renewable-generation conditions. Full article

Grid-connected photovoltaic (PV) systems paired with battery storage are becoming a core element of low-carbon distribution networks. This paper develops a complete closed-form formulation together with an independent, simulation-based verification of a single-phase grid-connected PV-battery microgrid sized for high-irradiance residential conditions in Saudi Arabia, using measured solar-resource and tariff data for Riyadh. A 6.25 kW monocrystalline array feeds a 400 V DC link through a perturb-and-observe boost stage; a bidirectional converter couples a 13.5 kWh LiFePO₄ battery; and an IEEE 1547 smart inverter interfaces a 230 V grid through an LCL filter. Governing equations for every subsystem are derived and evaluated numerically, and a Python re-implementation of the phasor power-flow model verifies the analysis over a 24 h cycle run to periodic steady state, reproducing the reference design values with a mean absolute error of 0.5%. Using measured monthly solar-resource and temperature data for Riyadh, a full twelve-month analysis gives an annual self-sufficiency of 51.8% and a PV self-consumption of 72.9% for the optimised energy-management scheme. A dedicated time-domain switching simulation with FFT analysis shows that the LCL filter limits grid-current total harmonic distortion to 0.8%, far below the L-filter value of 6.2% and below the 5% current-distortion reference of IEEE 519 (full compliance additionally requires the PCC short-circuit ratio). Twelve-month, battery-size and load-sensitivity studies confirm robustness, and a techno-economic assessment based on the Saudi Electricity Company residential tariff quantifies levelized cost, payback and battery degradation, showing that economic viability hinges on tariff reform. Full article

The increasing penetration of renewable energy and rising computing demands intensify the need for flexible regulation resources. In extremely cold regions, low temperatures degrade energy storage and adjustable-load performance, challenging integrated energy systems (IESs). This study proposes a sodium-ion battery–data center coordinated scheduling framework tailored to seasonal cold-region operations. A unified model captures renewable generation uncertainty, temperature-dependent sodium-ion battery behavior, and computing load flexibility. A hierarchical mechanism is adopted, in which the upper layer employs an improved NSGA-III (I-NSGA-III) and the lower layer executes dynamic operations via fuzzy logic control. Case studies for winter and summer show operating cost reductions of 11.9% and 27.4%, renewable energy absorption rates of 90.87% and 86.55%, and loss-of-load rates of 1.28% and 0.53%, respectively. Compared with lithium-ion batteries, sodium-ion batteries further reduce the loss-of-load rate by 38.8%, improve absorption by 1.9%, and reduce operating costs by 10.7% under low temperatures. Unlike existing studies, this work integrates temperature-dependent battery behavior, constrained data center flexibility, and adaptive optimization–control coordination within a unified scheduling framework for cold regions. The proposed framework provides a practical engineering solution that improves economic performance, renewable energy utilization, and power supply reliability. Full article

This study presents an integrated design-to-operation framework for a PV–battery solar highway lighting system supported by IoT-based monitoring for highway-scale deployment. The proposed framework combines pole-level PV-battery sizing, annual energy-reliability simulation, road-level techno-economic optimization, and IoT-based digital monitoring. The Shoubra–Banha Freeway in Egypt, a 40 km corridor with a two-sided lighting arrangement, was selected as the case study. In the pole-level phase, dimming strategies, PV capacities, battery sizes, and battery technologies were evaluated using sequential parametric analysis under a reliability constraint of Loss of Load Probability (LLP) below 1%. The S2 aggressive dimming profile achieved the best operating performance, with an LLP of 0.003, energy reliability of 99.70%, and 2.89 kWh annual unmet load. The minimum feasible PV capacity was 0.8 kW, while the smallest acceptable storage capacity was 4.8 kWh nominal capacity, corresponding to approximately 3.84 kWh usable capacity under an 80% allowable depth of discharge. Among the tested battery technologies, LiFePO₄ achieved the best reliability performance, with an LLP of 0.007 and a 99.25% battery deficit coverage ratio. In the road-level phase, three deployment configurations were compared. Case B, using 12 m pole height and 36 m spacing, was selected as the best-balanced solution, requiring 2224 poles, achieving 99.30% energy reliability, an LLP of 0.007, annual PV generation of 3,178,341 kWh, and total CAPEX of approximately 2.88 × 10⁸ EGP, equivalent to about 5.76 million USD based on an assumed exchange rate of 1 USD = 50 EGP. Lastly, the development of an IoT-based monitoring system design utilizing sector gateways, telemetry variables, alarm conditions, MQTT protocols, and dashboard displays was carried out. The scenario for gateways at 5 km intervals was advised due to its better fault isolation, lower gateway workload, and scalability. This indicates that the suggested approach offers a viable, cost-efficient, and technologically enabled solution for automated solar-powered street lighting systems. Full article

The optimal expansion of unbalanced three-phase distribution networks in non-interconnected zones requires the simultaneous resolution of two highly complex planning decisions: the selection of feeder routes and the sizing of conductors. This problem, formulated as a non-convex mixed-integer nonlinear program (MINLP), poses significant computational challenges due to the combinatorial explosion of radial topologies, discrete conductor choices, and the nonlinearity of three-phase power-flow equations. While metaheuristics offer flexible exploration, they lack optimality guarantees and repeatability, whereas exact MINLP solvers provide rigorous solutions but become computationally intractable for systems of realistic size. To overcome these limitations, this paper introduces a novel hybrid exact–metaheuristic framework that synergistically combines the global exploration capabilities of the Equilibrium Optimizer (EO) with the rigorous evaluation power of an exact MINLP model. In this cascade architecture, EO efficiently navigates the discrete space of radial topologies, while the exact MINLP stage, solved using BONMIN with an interior-point branch-and-bound scheme, optimizes conductor selection and evaluates the full annualized cost, rigorously enforcing voltage, ampacity, and physical constraints. The proposed methodology was validated on 10-, 30-, 50-, and 110-node test systems derived from real Colombian non-interconnected zones (Nuquí, Leticia, San Andrés, and a large-scale urban case). Comparative analysis against pure metaheuristics (SSA, GWO, VSA) and standalone MINLP demonstrates that EO-MINLP consistently yields the lowest total annualized costs, achieving savings of up to 0.42%, 0.71%, and 1.36% over the best pure metaheuristic for the 10-, 30-, and 50-node systems, respectively. Crucially, the hybrid strategy dramatically enhances scalability, reducing the standalone MINLP computational time by 15.79%, 78.68%, and 88.95% for these cases, while preserving solution quality and improving repeatability (standard deviation reduced from over 1.2% to as low as 0.11%). For the challenging 110-node system, where the standalone MINLP proved computationally infeasible, the proposed method successfully delivered a feasible, high-quality solution with a standard deviation of just 0.43%, confirming its practical applicability to large-scale planning. These results demonstrate that the EO-MINLP framework provides a robust, scalable, and economically superior tool for the cost-effective design of unbalanced distribution networks, effectively bridging the gap between the flexibility of stochastic search and the rigor of mathematical programming. Full article

Electrocoagulation (EC) is a promising technology for hypereutrophic wastewater treatment, yet reactor geometry, electrode passivation, and energy inefficiency remain key limitations. This study developed a graphene oxide–phosphomolybdate (GO–POM) composite cathode integrated into an asymmetric electrode configuration (anode:cathode area ratio = 1:10) to simultaneously address charge-transfer resistance, passivation, and energy consumption in the EC treatment of Lake Mariut wastewater (Cairo, Egypt). The GO–POM composite exhibited a charge-transfer resistance of 2.34 ± 0.09 Ω·cm², significantly lower than that of a graphite rod (4.12 ± 0.31 Ω·cm²), carbon felt (3.28 ± 0.24 Ω·cm²), and SS316 (6.84 ± 0.45 Ω·cm²). Under optimized conditions (j = 10 mA/cm², pH 6.0, 60 min), the asymmetric GO–POM system achieved 92.2 ± 1.8% TOC removal with a specific energy consumption of 4.4 ± 0.3 kWh/m³—a 37% reduction compared to the symmetric conventional baseline (6.1 ± 0.4 kWh/m³). The treated effluent met the discharge limits set by Egyptian Law 48/1982 for COD, BOD, and TSS. Preliminary techno-economic and life-cycle analyses identified Al electrode consumption as the dominant cost and carbon driver, with a solar-powered continuous-flow operation as the priority pathway for further energy reduction. Full article

China’s power sector is accelerating its transition to spot-market clearing with increasing offshore wind penetration. This transition poses compounded operational and economic challenges, as the interaction between generation variability and price volatility affects both producer revenues and real-time system balancing costs. This study utilizes full-year hourly generation and spot price data from an offshore wind farm in eastern Guangdong, which represents the largest offshore wind industry cluster and a premier high-wind-resource area along China’s near-sea coasts. This empirical dataset provides significant value for characterizing real-world market behaviors under Guangdong’s dual-settlement framework. By employing a settlement-consistent Monte Carlo framework to quantify the joint effects of forecast errors, our results reveal that while downside risk is primarily driven by generation volume errors under normal conditions, the negative correlation between wind output and prices intensifies revenue volatility. Furthermore, under high-stress scenarios characterized by extreme market volatility and large deviations, price uncertainty emerges as the dominant driver of tail risk. Ultimately, these findings demonstrate that probabilistic forecasting for both prices and generation is essential not only for producer risk management but also for supporting dispatchable decision-making and reliable operation of power systems with high shares of renewable energy. Full article

Rankine cycle CO₂ pumped thermal energy storage (R-CPTES) offers high-density, emission-free grid storage, but existing designs are limited by modest turbine inlet temperatures and produce electricity only, leaving their thermal potential unused. This paper introduces a Rankine CO₂ storage cycle driven by a high-concentration parabolic dish collector (PDC) and configured solely for combined heat and power, representing a combination of point focus solar energy with CO₂ pumped thermal storage that has received limited attention in the literature. During discharge, the dish superheats the working fluid and raises the high temperature turbine inlet from 456 °C to 500 °C, boosting net power. A heating recovery exchanger placed ahead of the second regenerator then extracts useful heat from the turbine exhaust for district or process supply, without the absorption refrigeration subsystem used in comparable cooling inclusive designs. The aim is to characterise this system through energy, exergy, and parametric analysis. A closed, pinch-consistent model is developed under steady-state assumptions using the Span–Wagner equation of state, with the discharge low pressure, discharge mass flow rate, and PDC outlet temperature varied independently and jointly at a fixed 10 MPa high-pressure boundary. The analysis reveals a power-versus-heat trade-off governed by the discharge pressure and bounded by physical limits rather than interior optima, shows that the solar superheat is a prerequisite for cogeneration, and identifies the system as heat-transfer destruction dominated, with the latent cold storage the largest single source of irreversibility. At the design point the system delivers 16.1 MW of power and 2.5 MW of heat, attaining a storage round-trip efficiency of 73.2% (electricity-only), a solar-inclusive electrical efficiency of 58%, an energy utilization factor of 67%, and an overall exergy efficiency of 61.3%. A preliminary economic assessment gives a levelised cost of storage of 0.10–0.18 $/kWh, competitive with comparable CO₂ storage systems. The proposed system thus provides a simple, fossil-free cogeneration solution for high-DNI regions based on a modular, point focus solar configuration. Full article

Under the “dual carbon” strategy, the organic Rankine cycle (ORC) represents a key technology for efficiently recovering low-grade industrial waste heat in inland factories. This study addresses ORC operational instability caused by seasonal temperature fluctuations and high cooling-water costs in inland regions. An ORC system powered by industrial waste heat is investigated. A steady-state simulation model is developed in Aspen Plus to analyze the effects of expansion pressure, condensation pressure, cooling-water flow rate, and working-fluid flow rate on system performance, and to filtrate parameters to maximize economic returns. The results demonstrate that optimal expansion pressure yields maximum net shaft power. Condensation pressure and the cooling-water flow rate are closely linked, necessitating a balance between power generation revenue and cooling costs. An optimal range for working-fluid flow rate is also identified. Seasonal temperature variations significantly influence system performance. Higher summer temperatures increase condensation pressure and reduce revenue, while lower winter temperatures enhance revenue when filtrated parameters are used. This research provides theoretical and technical references for achieving efficient, cost-effective, year-round ORC operation in inland factories. In the case study, the analysis for seasonal temperature variations increases the total annual revenue by CNY 2.217 million, with an annual average system efficiency of 10.59%. Full article