Search Results (916)

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

Static wake steering can increase wind-farm power production, but its application to floating offshore wind farms requires assessment of the coupled wake, platform, structural, and station-keeping response. This study evaluates whether power-maximizing static yaw setpoints identified using the steady, control-oriented FLORIS model retain their benefit when transferred without re-optimization to a coupled FAST.Farm floating wind-farm model. The reference farm comprises four IEA Wind 15 MW turbines mounted on VolturnUS-S semi-submersible platforms. Greedy and static wake-steering operations are compared at three below-rated wind speeds, three sea states, and five matched turbulent-inflow realizations, resulting in 90 farm-level FAST.Farm simulations. Wake behavior is characterized through wake-center deflection, meandering, and velocity-deficit profiles, while turbine and mooring fatigue responses are evaluated using paired damage-equivalent-load statistics. Static wake steering increases mean farm power under all nine investigated wind–wave conditions. The gains are approximately 5.1–$5.2\%$ at $7\mathrm{m}{\mathrm{s}}^{-1}$, $5.0$–$5.1\%$ at $8\mathrm{m}{\mathrm{s}}^{-1}$, and $4.0$–$4.2\%$ at $9\mathrm{m}{\mathrm{s}}^{-1}$, with all paired $95\%$ confidence intervals remaining above zero. The gain results from a power redistribution in which the intentionally yawed upstream turbine incurs a local loss that is exceeded by the combined recovery of the downstream turbines. The fatigue response is strongly component- and turbine-dependent. The paired farm-mean blade-root DEL decreases by $0.82$–$2.24\%$, whereas the tower-base DEL increases by $0.76$–$2.78\%$, and the FairTen1 response generally increases by $0.88$–$2.92\%$. The farm-mean yaw-bearing response is mixed, ranging from a $1.15\%$ reduction to a $4.32\%$ increase. Turbine-level analysis reveals larger localized penalties, reaching approximately $10.4\%$ for the yaw-bearing DEL and $12.8\%$ for FairTen1. Spectral analysis associates the yaw-bearing response with yaw-induced aerodynamic and structural excitation, while the tower-base response is strongly influenced by low-frequency wave–platform dynamics. A complementary FLORIS sensitivity analysis demonstrates that the optimized aerodynamic benefit depends strongly on wind direction, spacing, wind speed, and turbulence intensity. For a Tampen-derived 11-turbine layout, resource weighting over the modeled 4–$13\mathrm{m}{\mathrm{s}}^{-1}$ interval produces an annual energy-contribution increase of $3.653\mathrm{GWh}{\mathrm{year}}^{-1}$, or $0.921\%$. These results provide numerical evidence that static wake steering can retain a positive power benefit in a coupled floating wind-farm environment, but controller assessment must include turbine- and component-specific dynamic loads rather than farm power alone. Full article

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems. Full article

Wind energy, an abundant renewable resource, remains difficult to harness efficiently due to fluctuating speeds and unpredictable directions. In this work, we present a wind bell-inspired triboelectric nanogenerator (WB-TENG) designed for omnidirectional, variable-speed wind harvesting, utilizing layered triboelectric polymers such as polytetrafluoroethylene (PTFE), polyethylene terephthalate (PET), and polyamide (PA) to enhance energy capture performance. The developed device demonstrates the ability to generate electrical output even under extremely low wind speeds as low as 0.5 m/s. Additionally, it successfully captures wind energy from all directions within a full 360° range. Through structural optimization, the WB-TENG achieves a peak output voltage of 25.1 V and a maximum power of 3.5 μW, representing substantial improvements of 170% and 1232%, respectively, over the performance of our previous prototype. To verify its practical capability, the optimized WB-TENG is employed to power several electronic devices, including a digital watch and 50 commercial LEDs, confirming its potential for real-world energy harvesting applications. This work presents a novel and effective strategy for harnessing wind energy under dynamic environmental conditions, offering a sustainable approach for decentralized energy collection in low-speed and omnidirectional wind settings. Full article

Standalone DC microgrids offer a promising solution for providing reliable and sustainable electricity to remote communities in developing countries. However, the intermittent nature of solar and wind resources, combined with continuously varying load demand, presents considerable operational challenges in maintaining real-time power balance, stable DC bus voltage, and ensuring reliable continuous supply. Therefore, there is dire need for user-friendly control solutions tailored to the specific needs of isolated communities. As such, this paper presents a coordinated control and power management strategy for an isolated hybrid solar–wind–battery integrated DC microgrid for rural electrification applications. A comprehensive mathematical model of the standalone DC microgrid incorporating photovoltaic generation, wind energy conversion, battery storage, bidirectional DC-DC conversion, and common DC bus dynamics is developed at the very first stage of the proposed coordinated control framework. The framework utilizes principal local device loops and a secondary dynamic power management strategy to ensure efficient renewable power extraction, dynamic source–storage–load coordination, stable DC bus voltage, and real-time energy management within the developed standalone DC microgrid. It is worthwhile to mention that, instead of using synthesized or online available wind speed and solar irradiance data, this research utilized real-time recorded metrological data obtained from the Mehran University Jamshoro, Pakistan. The obtained results establish a stable DC bus voltage regulation within acceptable operating limits, continuous power balance, seamless bidirectional battery operation, and safe battery state-of-charge (SoC) management to prevent deep discharging or overcharging, thus ensuring reliable operation. The overall performance confirms the technical robustness, operational flexibility, and practical suitability of the proposed standalone hybrid DC microgrid architecture for its resilient operation and rural electrification applications. Full article

Following extreme disasters, hybrid wind farms comprising grid-following (GFL) and grid-forming (GFM) turbines can serve as black-start resources for power system restoration. However, wind power uncertainty, wake effects, and collection-grid restoration constraints complicate the startup sequence planning of GFL turbines within hybrid wind farms. To address these challenges, this paper proposes a multi-objective mixed-integer linear programming (MILP) model that jointly considers electrical impedance paths, wind uncertainty, and spatial wake effects. Information Gap Decision Theory (IGDT) is incorporated into active power support constraints to account for wind uncertainty through a robust adjustment of available generation capacity, while the Dijkstra algorithm is employed to convert collection-cable parameters into impedance-based cost factors for identifying minimum-impedance restoration paths and mitigating transient overvoltage risks. In addition, dynamic wake losses under non-uniform turbine layouts are quantified to capture the influence of startup sequences on local flow fields, and the resulting nonlinear terms are reformulated using the big-M linearization technique. Case studies considering different wind directions, time-varying wind speed conditions, and cable-impedance sensitivities demonstrate the effectiveness of the proposed framework. The results show that the proposed strategy provides a favorable balance between impedance-cost minimization, wake-effect mitigation, and robustness enhancement, while maintaining reliable restoration performance under diverse operating conditions. Full article

Electrical power systems are exposed to interacting electrical, thermal, environmental, and resource-related faults such as leakage current, voltage and frequency deviations, overcurrent, harmonic distortion, phase-sequence error, humidity, fire, wind-speed variability, water insufficiency, and solar-resource loss. This study introduces a software-based database-generation and smart-learning framework that converts 22 candidate risk factors into six normalized severity levels and then maps the simultaneous system state to low-, medium-, and high-level protection decisions. The main novelty is that the software not only evaluates existing measurements; it also produces a literature- and standards-informed synthetic database when long-term real field measurements are not yet available. The database is generated by defining variable limits, sampling realistic operating states, computing severity labels, and storing input–output pairs that can later train or validate predictive maintenance models. The proposed framework therefore, links protection logic, database construction, and reusable training data in a single workflow. The results show how simulated annual operating scenarios can be transformed into structured risk records, warning classes, and shutdown decisions, supporting early fault detection, maintenance planning, and resilience improvement in renewable-integrated electrical networks. Full article

The main objective of the current study is to assess the techno-economic feasibility, climate change adaptability, and sustainability of photovoltaic energy systems in six large hospitals in Turkey (Adana, Başakşehir, Bursa, Elazig, Gaziantep, and Yozgat) to achieve United Nations recommendations as Sustainable Development Goal 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action). This study aims to determine the impact of long-term climate change on the availability of photovoltaic (PV) energy resources. To achieve this goal, this research was conducted through a multi-step approach combining (1) the detection of long-term climate trends using linear regression on the TerraClimate database, (2) the spatial analysis of photovoltaic solar energy potential using high-resolution satellite imagery (Google Maps) for roof suitability and parking areas, (3) the estimation of photovoltaic electricity generation and the calculation of the capacity factor, (4) the application of the Response Surface Methodology (RSM) based on NASA Giovanni data to model the nonlinear reciprocal relationships between precipitation (R), aerosol optical thickness (AOT), photovoltaic solar energy production, and (5) the techno-economic analysis using the Levelized energy cost (LCOE), payback period, and CO₂ emission reductions. The results show statistically consistent warming trends across all sites with trends for Tmax ranging from +0.0205 to +0.0268 °C/year and for Tmin from +0.0208 to +0.0300 °C/year. The temperature of PV cells increases at a rate of +0.0197 °C/year and the wind speed decreases by −0.0031 to −0.0149 m/s/year, which indicates a reduction in convective cooling. Solar radiation, on the other hand, is relatively constant with small trends ranging from +0.0002 to +0.0566 W/m²/year, and confirms the consistent solar resource availability. Seasonal PV resource potential varies from ~70–95 W/m² in winter to 290–310 W/m² in summer. Furthermore, the installed PV capacities are between 6 MW (Yozgat) and 47 MW (Başakşehir) with capacity factors of 17.0–19.7% and payback periods of 4.31–4.88 years. RSM models have high explanatory power (R² = 0.57–0.74) with AOT as the most important negative driver of PV performance. Consequently, the results show that while the solar resource of Türkiye is stable and highly exploitable, PV efficiency is increasingly determined by climate-induced thermal stress and reduced wind cooling. The study highlights the economic viability, environmental advantages, and strategic relevance of PV systems at hospitals for resilient, low-carbon healthcare infrastructure in future climate scenarios. Full article

Automated guided vehicles (AGVs) are key transportation resources in automated container terminals, where operational scheduling and charging decisions exhibit strong spatiotemporal coupling characteristics. When AGVs are assigned to transport “reefer” containers, interruptions in external power supply during transit may lead to temperature fluctuations, posing potential risks to cargo quality and transportation safety. To address this issue, this paper proposes a two-stage coordinated optimization framework for AGV operations and charging, considering reefer container temperature constraints. Specifically, an AGV transportation scheduling model is first developed to characterize quay-crane operations, yard allocation, AGV travel processes, battery dynamics, and reefer container transit-time limitations associated with temperature maintenance requirements. Subsequently, a port microgrid scheduling model integrating charging stations, photovoltaic generation, wind power, and energy storage systems is established to coordinate AGV charging strategies with energy system operations. Based on these models, a two-stage optimization framework is constructed, in which AGV task assignment and yard allocation are optimized in the first stage to improve operational efficiency, while energy scheduling is optimized in the second stage to minimize system operating costs under the operational decisions obtained in the first stage. Numerical results demonstrate that the proposed method effectively reduces the transportation time of reefer containers, alleviates temperature-related transportation risks, enhances the coordination between logistics operations and energy management, and improves terminal operational efficiency while ensuring the safety and quality of reefer container transportation. The proposed framework provides an effective solution for the integrated optimization of logistics and energy systems in automated container terminals. Full article

Reliable medium–long-term wind energy projection is essential in the planning, financing, and operation of large-scale offshore wind development. This study classified projection methods into three categories: statistical/empirical and climate-signal-driven methods, dynamical models with reanalysis and regional downscaling, and machine/deep learning and hybrid methods for bias correction, downscaling, and direct data-driven projection. Then, this study reviewed the technical framework, representative studies, and comparative strengths and limitations. The main finding was that the state of the art increasingly converged on “dynamical simulation plus statistical or machine learning correction”. Next, seven main bottlenecks, along with the countermeasures, were systematically presented: (i) difficult data quality control and insufficient observational representativeness, especially offshore; (ii) divergent, even contradictory, conclusions for the same region across data sources and research groups; (iii) large uncertainty in extrapolating 10 m winds to the continually rising turbine hub height; (iv) difficulty in quantifying and communicating non-stationarity and uncertainty to decision-makers; (v) engineering conversion errors from projected “wind resource” to deliverable “electricity”; (vi) systematic biases in the marine atmospheric boundary layer, strong winds, and extreme conditions; and (vii) unresolved reliability, interpretability, and out-of-distribution generalization of AI models. Correspondingly, three mutually reinforcing strands of countermeasures were proposed: first, strengthening the observational and benchmarking foundation through unified, open, quality-controlled observation networks with data-provenance standards and shared reference datasets and intercomparison protocols; second, advancing physics–data integration and uncertainty quantification through hybrid and physics-informed correction, regime-specific bias correction of boundary-layer and extreme-wind errors, and probabilistic frameworks that delivered and clearly communicated credible intervals; and third, closing the resource-to-electricity gap by embedding power-curve convolution, wake-loss modeling, and availability and technology derating into the projection workflow, with the aim of improving medium–long-term wind energy projection accuracy. Full article

To meet the demand for large-capacity and long-distance transmission of deep-sea offshore wind power and coordinated export of multiple energy sources in coastal clean-energy bases, this paper investigates the topology configuration and operating characteristics of a multi-energy DC collection system for offshore wind power integration. Based on the characteristics of offshore wind power, nuclear power, onshore photovoltaic generation and pumped storage, a source-type–converter-topology–control-function matching relationship is established. A ±800 kV true-bipolar DC large-bus system is then constructed, in which the four sources are configured as a lightweight offshore wind export branch, a stable power-export branch, a fast controllable renewable-energy branch and a system regulation resource, respectively. A polarity-interface conversion link is introduced to match the local offshore wind export structure with the main true-bipolar DC system, and the power-balance relationship among sending-end injection, receiving-end absorption and DC-bus voltage is formulated. PSCAD/EMTDC simulations are performed under steady-state operation, wind-speed step disturbance, a sending-end AC three-phase metallic grounding fault and a submarine-cable pole-to-ground fault. The results confirm that the proposed differentiated topology can support multi-energy collection, true-bipolar voltage coordination and continuous stable operation under typical disturbances. Full article

With the fast-increasing integration of offshore wind power, coordinating generation resources with demand-side flexibility has become essential for secure and efficient power system operation. This paper proposes a coordinated dispatch approach considering the demand response (DR) of load aggregators (LAs). A bi-level optimization model is developed, where the upper level describes system operation based on optimal power flow, and the lower level captures the demand response behavior of LAs, including curtailable and transferable loads. To improve computational tractability, the lower-level problem is reformulated using Karush–Kuhn–Tucker (KKT) conditions, and the original bi-level optimization problem is transformed into a single-level mixed-integer optimization model. Case studies are conducted on a modified IEEE 33-bus system with offshore wind integration and distributed LAs. The results show that the proposed approach effectively reshapes load profiles through peak shaving and load shifting, resulting in smoother system load characteristics. Meanwhile, the coordination between load response and renewable generation improves the accommodation of offshore wind power. It also reduces wind curtailment and decreases grid import. Compared with conventional dispatch without DR, the proposed method enhances system flexibility and provides an effective operational scheme for power systems with high renewable penetration. 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

This research evaluates the feasibility of implementing photovoltaic solar systems in the Ecuadorian Amazon to harness solar energy and increase energy security in the region. It is based on the need to reduce direct dependence on fossil fuels and existing hydroelectric systems. The overall framework is to transform the energy matrix to utilize incident solar energy, integrating it with current hydroelectric and thermal generation. The fundamental goal is to evaluate the energy resource using specialized software such as Homer Pro and develop designs for the proper operation of photovoltaic solar technology, which will contribute its surplus energy to the National Interconnected System (SNI) and, therefore, reduce the country’s high dependence on the hydrological cycle. The results obtained demonstrate that solar power plants can be of great benefit to the country, especially when combined with wind and existing hydroelectric power. This will contribute to the diversification of energy sources and, consequently, to energy security through the increase in renewable energy. In the worst-case scenario, the cost of energy can be 7 cents per kWh, and in the best-case scenario, in a combined dispatch, 3 cents per kWh. Full article