Real Weather Data Research Articles

Cyber attacks detection and mitigation in hybrid power system using type-2 fuzzy controller

Abstract This paper presents a novel control technique using Fuzzy Type-2 controller for regulating the frequency deviations in an independent Hybrid Power System (HPS). The HPS comprises of a wind system and a solar photovoltaic (PV) system as primary energy sources, along with Fuel Cells (FCs) as additional generation systems and Double Layer Capacitor (DLC) Banks as storage devices to meet the load demand. The frequency variations occur in the HPS due to the intermittency in power outputs from the primary sources, the load changes as well as any attacks in the system. The frequency regulation is achieved by effective coordination control of FC and DLC with the aid of a Fuzzy Type-2 controller and a high pass filter (HPF). The DLC technology adequately satisfies the residual power of hybrid systems resulting from the slow dynamics of FCs. The Fuzzy Type-2 controller is trained using an input-output data set of a PID controller whose gains are tuned to their optimal values using Monte-Carlo state estimators. The proposed system is simulated using real weather data to validate the capability of the Hybrid Power System (HPS) in supplying isolated loads while maintaining power frequency balance conditions. Also the HPS is subjected to attacks in daily load variation and the results are compared with the system without attacks. The simulation results show that the peak over shoots are reduced considerably but the settling time is 0.2 seconds in the system with no attacks where as the settling time is prolonged to 1.2 seconds with the attacks. This paper demonstrates the effectiveness of the Fuzzy Type-2 controller for the coordination control of DLC and FC to achieve generation-load balance condition in comparison with that of the PID controller for HPS system with and without attacks.

Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system

This paper describes a decentralised smart grid system containing renewable energies, storage systems and distributed generation with human control and intervention. The importance of each element and the interaction between them leads to think about a decision-making strategy. In fact, the integration of a Photovoltaic Panel (PVP) is used due to its availability and its participation in the carbon emissions reduction. Also, a battery is required to fill a power gap or absorb extra generated energy. Moreover, an optimal sizing is needed to get an efficient system with minimum cost. Also, an energy management strategy (EMS) is essential to ensure the power resources scheduling in order to keep a continuous equilibrium supply-demand of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. In the first part, the proposed smart grid optimal sizing is determined under real weather data (solar radiation) of the city of Sousse, Tunisia, using the Hybrid Optimization of Multiple Energy Resources (HOMER) software technique. This approach is chosen thanks to its simplicity, effectiveness, and high precision compared to traditional techniques. In this paper, several configurations (Grid, (Grid-battery), (Grid-PVP), (Grid-PVP-battery)) are studied. The obtained results prove that the (Grid-PVP-battery) system configuration is the most efficient and economical solution. In the second part, a robust energy management strategy (EMS) is proposed for two smart grid configurations (grid-battery, grid-PVP-battery). This strategy is based on Fuzzy Logic Control (FLC) thanks to its non-linear modelling and its ability to make decisions relating to energy management. The primary goal of the suggested (EMS) is to ensure the energy resources scheduling in order to keep a continuous equilibrium among the production and consumption of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. As input data, (FLC) used time-varying price electricity (Price (t)) to solve an instant decision problem by choosing, at each instant, the optimal energy source (which provide electricity at the cheapest price possible). The obtained results, carrying out Matlab simulation, prove the efficacy of the proposed strategy, not only, in the energy resources scheduling to meet the load, but also, for the system cost reduction since the PVP has been used as much as possible since it is inexpensive relative to the costs of battery capacity and the grid.

Does local non-farm income moderate the intensity of climate change induced migration in India?

While the impact of climate change on migration is well documented in the literature, very little is known about the moderating impact of local non-farm income on climate change induced migration. To fill this gap in the literature, the paper combines real time weather data provided by Indian Meteorological Department with a very rich household data on Indian states belonging to the eastern and semi-arid tropic regions collected by ICRISAT to investigate whether farmers always resort to migration or take it up as a second-best option only after they fail to diversify locally on the face of climate change induced crop loss. Using recursive econometric model, it is observed that households first try to diversify their income by engaging in local non-farm activities and availability of local non-farm income further moderates the effect of climate change induced yield loss on migration. Higher rural out migration may adversely impact the agricultural sector and affect sustainable urbanization, Also, decision to migrate is often challenging as it necessitates leaving behind social and community networks. Therefore, our findings offer valuable insights and discusses important policy recommendations towards improving the access to local non-farm activities.

Techno-economic optimization of photovoltaic (PV)-inverter power sizing ratio for grid-connected PV systems

- The accurate sizing of the inverter, specifically the power sizing ratio (PSR) plays a vital role in maximizing energy production and economic benefits. Existing studies often overlook the complex interplay between maximizing energy capture and minimizing inverter-related economic costs when selecting the optimal PSR. This research presents a techno-economic approach to optimizing the PSR for grid-connected photovoltaic (PV) systems. A simulation model is developed, incorporating real weather data and inverter efficiency curves. The model is calibrated using a Pattern Search Algorithm (PSA) to ensure an accurate representation of real-world inverter behavior by achieving a minimum relative difference of 13.78 % (Norm-RMS) in AC power output. The analysis explores the trade-off between PSR, annual energy yield, and inverter clipping. An optimal PSR of 1.19 is identified, balancing energy capture (up to 2000W inverter capacity) and economic efficiency. This approach promotes cost-effective inverter selection and wider PV adoption.

DEVELOPMENT OF A NEAR REAL-TIME WARNING AGRICULTURAL SYSTEM FOR DISASTER PREDICTION

The effects of disasters are not uniform across all individuals and communities. One’s preparedness and socioeconomic factors determine how much they will pay. Preparedness plays a crucial role in reducing the impact of disasters and aiding in their management and recovery. However, effectively preparing for unforeseen disasters becomes difficult when there is insufficient knowledge and expertise. Without accurate predictions and timely alerts about disasters, a community's level of readiness remains extremely low. Namibia has experienced various unavoidable climate-related dangers over time, making it vulnerable in the future. Inconsistencies in disseminating weather information and early disaster notifications are factors that obstruct the mitigation of climate-related disasters. Furthermore, disaster early warning system implementation has been slower in most African countries due to limited technical resources. Another notable challenge is upholding internationally standardized early warning systems in rural settings without overlooking the dynamics of the rural community, which leads to system ineffectiveness. Therefore, this study employed a mixed-methodology approach to gather qualitative and quantitative data from local farmers through the use of questionnaires to review the readiness to adopt early warning systems in northern Namibia, assess available technical resources, study the existing disaster mitigation practices in Namibia. Subsequently, historic and near real time weather data was collected from weather agencies, which ultimately designed and developed an early warning system to enhance resilience and preparedness for hazards and risks in farming communities by issuing comprehensive and timely alerts. The study confirmed the dire need for disaster prediction systems in Namibia, although it also highlighted pressing accessibility concerns that future researchers could study, especially under-resourced potential users.

Assessment of crop water requirement and irrigation scheduling of potato using weather models under sub temperate climatic condition of North-Western Himalaya

To optimize irrigation water and enhance yield, appropriate amount of irrigation water and its scheduling according to real time weather data can help in reducing crop water foot prints. The evapotranspiration (ET) based irrigation scheduling using present weather conditions reflecting changed climatic scenarios in the study regions in the past few decades has proved to be sagacious use of water. To study the ET based irrigation scheduling, field experiment conducted with the treatments comprising four ET based weather models irrigation scheduling methods viz. no irrigation or rainfed, Thornthwaite, Hargreaves method, Temperature and modified Penman Monteith under sowing windows of 20th December and 20th January were conducted at research farm, of Department of Agronomy, CSK HPKV, Palampur, Himachal Pradesh during Rabi season of 2015-16. The estimated crop evapo-transpiration (ETc) with different ET estimation methods under North Western Himalayas showed variation during crop season. The highest estimated ET was observed in December as compared to January sown potato crop with Hargreaves method (376.8 mm) followed by Temperature (354 mm), Thornthwaite (329.5 mm) and the lowest in modified Penman Monteith methods (241.9 mm). The estimated ETc from pan evaporation observed to be the highest to the tune of (382.3 mm) compared to other methods of ETc estimation. The crop coefficients modeled at different pheno-phases were used for estimating water requirement of potato crop. It was found that irrigation scheduling based on modified Penman Monteith of ET estimation saved one irrigation to the tune of 60 mm during crop period without affecting significantly growth, and tuber yield of the potato as compared to other methods, viz., However, the irrigation schedule based on Thornthwaite method has recorded highest total potato tuber yield (171.76 q ha-1) being at par with irrigation schedules worked out from other ET estimation methods. The total water footprints were the lowest in modified Penman Monteith to the tune of 300 liters per kg of potato yield compared to other methods.

Daylight Comfort Performance of a Vertical Fin Shading System: Annual Simulation and Experimental Testing of a Prototype

This study aims to develop and evaluate a vertically rotated fin shading system for an energy-efficient, user-friendly office space. The system was designed to protect a 4 × 8 m office room with a south-facing facade from excessive solar radiation and glare. The shading system was modelled and simulated using Rhino/Ladybug 1.6.0 software with Radiance engine, based on real-weather data (*.epw file) for Wrocław, Poland at 51° lat. The simulation calculated the useful daylight illuminance (UDI) for 300–3000 lux and the daylight glare probability (DGP) for ten static and four kinetic variants of the system. The optimal angle of the fin rotation for the static variant was found to be α = 40°. The kinetic variants were activated when the work plane illuminance exceeded 3000 lux, as detected by an internal sensor “A”. The simulation results show that the kinetic system improved the daylight uniformity in the office room, achieving UDI300–3000 values above 80% for more than 40% of the room area. A prototype of the system in a 1:20 scale was built and tested on a testbed at Wrocław University of Science and Technology, using TESTO THL 160 data loggers. The measurements were conducted for a week in early November 2023, and three clear days were selected for analysis. The measurement results indicate that the low solar altitude on clear days causes high illuminance peaks (15–18 Klux) and significant contrast in the room, leading to unsatisfactory DGP values consistent with the simulation outcomes. Therefore, the study concludes that the proposed system may need an additional shading device to prevent glare during periods of low solar altitudes.

Control Concept for a Molten Salt Receiver in Star Design

A promising approach to reduce the system costs of molten salt solar receivers is to enable the irradiation of the absorber tubes on both sides. The star design is an innovative receiver design, pursuing this approach. The unconventional design leads to new challenges in controlling the system. This paper presents a control concept for a molten salt receiver system in star design. The control parameters are optimized in a defined test cycle by minimizing a cost function. The control concept is tested in realistic cloud passage scenarios based on real weather data. During these tests, the control system showed no sign of unstable behavior, but to perform sufficiently in every scenario further research and development like integrating Model Predictive Controls (MPCs) need to be done. The presented concept is a starting point to do so.

Temporal Precursor Discovery Using Long Short-Term Memory with Feature Attention

The continuous growth of demand on commercial airlines has made it crucial to guarantee the safety of airspace operations. Although adverse events are rare, once they happen, they can cause unpredictable risky factors and degrade airspace efficiency. Thus, studying historical air traffic data to discover precursors, features, or events that contribute to the occurrence of the adverse event in the future is important and has gained interest in recent years. In this paper, a novel and real-time applicable temporal precursor discovery (TPD) framework based on the long short-term memory neural network and the feature attention mechanism is proposed. The feature attention mechanism enables the framework to pay attention to certain features at a certain time, and the attention score is defined as the temporal precursor. The temporal precursor reflects the rationale behind the neural network’s prediction at each time step, providing a data-driven explanation of how the adverse event occurs. The proposed TPD framework was tested with real air traffic data and weather data recorded at Incheon International Airport in South Korea in 2019.

Design and simulation of a greenhouse in a computational environment (ANSYS/FLUENT) and an automatic control system in a LABVIEW environment

Greenhouses have been used to increase agricultural production. With the development of technology, they can now be automated. Many studies have been done on the automatic control of their microclimate, from intelligent control systems to Computational Fluid Dynamics (CFD) analyses, with the main purpose of optimal control of the microclimate and at the same time saving energy. This research concerns the process of modeling, design, and simulation of an automatic control system in greenhouses. More specifically, a virtual greenhouse (digital twin) is designed, and in it, the natural phenomena that take place in a real greenhouse are simulated. The program used for the simulations is Ansys FLUENT, suitable for CFD analyses. A branch of artificial intelligence, fuzzy logic, which is a method of replicating human thinking was utilized. To find the optimal control system, four fuzzy controllers were tested, and the optimal control system that the simulations indicated was implemented on an Arduino board using the LabVIEW program. The control was done at the temperature inside the greenhouse, with real weather data from a real greenhouse.

Economic feasibility assessment of a solar aided liquid air energy storage system with different operation strategies

In response to severe environmental problems, the proportion of new energy consumption worldwide is on an unprecedented upward trend, bringing energy storage technologies into focus. Among various energy storage systems, the solar aided liquid air energy storage (SALAES) system shows great prospects for development due to its cleanliness and high efficiency. This paper develops a basic operation strategy based on the idea of peak and valley reduction, considering the temporal fluctuation characteristics of solar energy. Further, operation modes for replacing the oil-air heat exchangers are proposed and an optimized strategy is developed accordingly. Real weather data of Beijing are used to estimate the annual power output of each system, and economic analyses are carried out on this basis using the levelized cost of storage (LCOS) method. The results show that the introduction of solar energy can reduce the LCOS of the liquid air energy storage system by 4.1 %–13.67 % and the proposed optimized operation strategy can increase the annual power output, resulting in a 0.2 %–0.4 % reduction in LCOS compared to the basic operation strategy. In addition, the impact of the molten salt tank capacity on the investment and return of the SALAES system is investigated. The optimal molten salt tank capacity obtained in this paper is 5 h, with the lowest LCOS of 0.1477 $/kWh and the shortest dynamic payback period of 10.29 years when operating with the optimized strategy.

A Study on Short‐Term Wind Power Forecasting Method Based on Wind Speed Spatio‐Temporal Calibration and Power Self‐adaptive Correction

Accurately predicting wind power is premise for the large‐scale integration of wind energy into the power grid and realizing reliable power grid scheduling and efficient wind farm operation. The spatial–temporal differences between the acquisition points of numerical weather prediction and wind farm sites should be considered because they significantly affect the accuracy of wind power output. Meanwhile, since there are different actual constraints on wind farm electrical networks and units, and the weather conditions may vary, there are no universal methods for identifying and correcting wind power output. To this end, this paper proposes a wind speed calibration model based on an Attention‐Bi‐LSTM network and a self‐adaptive recognition‐correction model based on SHAP‐CART classification for wind power output prediction. First, a spatial–temporal dataset is established by using real weather data from wind farm sites and data from the surrounding NWP points. Then, wind speed is accurately calibrated using the Attention‐Bi‐LSTM model. Next, considering the practical constraints on wind farm electrical networks and units, the self‐adaptive recognition‐correction model based on SHAP‐CART classification is employed to effectively recognize and predict the operating states and power output of the units, thus improving the accuracy and stability of wind power output prediction. Finally, the effectiveness and superiority of the proposed method is verified by using a wind farm in Eastern China as an example and analyzing the prediction results in various approaches. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A detailed investigation of an evacuated flat plate solar collector

The evacuated flat plate collector is a pioneering solar device for operation at medium temperatures (<200 °C) with high efficiency and without the need for tracking. The goal of this investigation lies in the detailed parametric analysis of an efficient evacuated flat plate collector and its daily performance for the city of Athens (Greece). The parametric analysis presents the instantaneous thermal and exergy performance of the collector for different inlet temperatures, solar irradiation levels, ambient temperatures, specific mass flow rates and the number of riser tubes. The daily analysis presents results for the thermal and exergy performance of the collector for different daily profiles of solar irradiation and ambient temperatures using real weather data. Also, the main innovation of this study lies in the development of a correlation of the daily useful output by taking into account the operating temperature level, the daily irradiation level and the mean ambient temperature. According to the yearly analysis, the optimum exergy efficiency is found at 13.33% for operation at 125 °C and the respective thermal efficiency is found at 47.24%. For this case, the levelized cost of energy and exergy were calculated at 0.0274 €/kWh and 0.1043 €/kWh respectively.

Cooling benefit of implementing radiative cooling on a city-scale

The map of radiative cooling potential shows that the radiative cooling power can reach up to 100 W/m2 for its application in different climatic zones. However, the effect of building deployment on the city-scale application of radiative cooling is generally ignored in majority of existing studies, which overestimates the cooling benefit. This paper investigates the cooling benefit of implementing radiative cooling on a city-scale by considering building deployment. To determine the effect of building deployment on the radiative cooling potential, the city of Xi'an in China is modeled using COMSOL and verified with real weather data. Then, the thermal response of buildings with radiative cooling envelopes is discussed in detail. Finally, carbon emission reduction is derived at the city-scale by applying radiative cooling considering building deployment. The results show that the estimated radiative cooling power is approximately decreased by 14.7% for the ideal broadband surface and 47.1% for the ideal selective surface on the selected day by considering building deployment on a city-scale. Furthermore, carbon emission reductions of 0.52 gCO2/(m2⋅h) and 0.16 gCO2/(m2⋅h) can be achieved from the roof and walls on the selected day by applying radiative cooling material on both the roof and walls. This study can provide guiding significance for the large-scale application of radiative cooling in cities.

Thermo-economic analysis of a low-cost greenhouse thermal solar plant with seasonal energy storage

Reduction of greenhouse gas emissions is today mandatory to limit the increase of ambient temperature. This paper provides a numerical study of a thermal solar plant using a seasonal dual-media sensible heat thermal energy storage system for supplying the total energy demand of a greenhouse located in the South of Italy, avoiding the use of the gas boiler. The aim of the work is to assess the technical and economic performance of a low-cost pit storage system, made of gravel and water, placed under the greenhouse to save surface. The study provides an original analysis of the charging and discharging phases during one year of operation on the basis of the real hourly heating demand and on real weather data. A sensitivity analysis of the levelized cost of heat is carried on with respect to the solar-collector area and to the storage-pit volume. The analysis shows that a minimum-cost design solution exists to cover 100% of the heat demand with an estimated levelized cost of heat of 153.3 EUR/MWh. The results demonstrate that dual-media thermal energy storage systems with solar thermal collectors represent a viable solution for reducing the environmental impact of greenhouses.

Convolutional neural network for fast adaptive beamforming of phased array weather radar

PurposeThe purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.Design/methodology/approachThe beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.FindingsTo validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods.Originality/valueMotivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

Techno-economic analysis of a combined heat and power system integrating hybrid photovoltaic-thermal collectors, a Stirling engine and energy storage

This paper presents a comprehensive analysis of the energetic, economic and environmental performance of a micro-combined heat and power (CHP) system that comprises 29.5 m2 of hybrid photovoltaic-thermal (PVT) collectors, a 1-kWe Stirling engine (SE) and energy storage. First, a model for the solar micro-CHP system, which includes a validated transient model for the SE micro-CHP unit, is developed. Parametric analyses are performed throughout a year to evaluate the effects of key component sizes and operating parameters, including collector flow rate, storage tank size, SE micro-CHP flow rate, and battery capacity, on the energetic, economic and environmental performance of the proposed system using real hourly weather data, and thermal and electrical energy demand profiles of a detached house located in London (UK). The optimum component sizes and operating parameters are determined accordingly. The daily and monthly operating characteristics of the system are evaluated, and its annual performance is compared to those of a reference system (gas boiler plus grid electricity), as well as of other alternative solar-CHP systems including a PVT-assisted heat pump system and a standalone PVT system. The results indicate that the installation of such a system can achieve an annual electricity self-sufficiency of 87% and an annual thermal energy demand coverage of 99%, along with annual primary energy savings and carbon emission reduction rate of 35% and 37% relative to the reference system. Over 30 years of operation, the net present value (NPV) of the proposed system is £1990 and the discounted payback period is 28 years. The economics of the proposed system is very sensitive to utility prices, especially the electricity purchase price. Relative to the alternative solar systems, the proposed system offers greater environmental benefits but has a longer payback period. This implies that although the energy saving and emission reduction potential of the proposed system is significant, the initial/capital investment, especially of the SE CHP unit and the PVT collector array, are currently high, so efforts should focus on the cost reduction of these technologies.

Backhaul-Aware User Association and Throughput Maximization in UAV-Aided Hybrid FSO/RF Network

Free-space optical (FSO) communication is expected to play an indispensable role with high data rates and low system complexity in beyond fifth-generation (B5G) networks. However, infrequent adverse weather conditions can incapacitate its performance. The combination of FSO and radio frequency (RF) has emerged as an effective alternative for meeting the growing need for high data rates in wireless communication networks. Unmanned aerial vehicles (UAVs) are also anticipated to play an instrumental role in B5G networks due to their flexible movement and deployment. In this paper, a UAV-aided hybrid FSO/RF backhauling system using a matching game theory (GT) and reinforcement learning (RL) framework is investigated. We deploy a UAV to provide a user offloading service to an already existing ground base station (GBS), which is facing a reduced backhaul capacity due to weather attenuation (e.g., fog). It is considered that the GBS has a pre-installed FSO backhaul connection to a macro-base station (MBS). However, during adverse weather conditions, the FSO backhaul is severely affected, compromising the reliability of the FSO link. With the reduced FSO backhaul capacity, the GBS needs an additional backhaul link to support its backhaul data transmission to the destination MBS. As a result, instead of building an expensive permanent parallel RF link for the rare foggy situation, a UAV can be hired to serve a portion of the users, thereby reducing the GBS load. The users perform a matching game-based procedure to select the base station (BS) of their choice to maximize their utility. The UAV is deployed at an optimal altitude, and the bandwidth partition between the GBS and the UAV is optimized to maximize the system throughput using RL. Real weather data from the cities of Edinburgh and London in the U.K. are used to evaluate the performance of the system. The numerical results show the superiority and effectiveness of the proposed scheme compared to conventional methods.

Desensitization method of meteorological data based on differential privacy protection

Artificial intelligence (AI) profoundly affects the research of meteorology. As an AI method, deep learning greatly improves the accuracy of weather forecasting. Deep learning training requires a lot of data, while the collection of meteorological data faces some problems, such as the long collection cycle, the high cost, and the desensitization requirement. We propose a deep learning model called MDPGAN (Meteorology Differential Privacy Generative Adversarial Network), which introduces a differential privacy framework to reduce the risk of identifying real data by querying synthetic data. The MDPGAN model can generate synthetic weather data with similar statistical characteristics to real weather data. The data generated by the MDPGAN model meets the requirements of data augmentation and data desensitization at the same time. In this paper, the meteorological data set of Kennedy Airport published by NOAA (National Oceanic and Atmospheric Administration) was used for the experiments of the MDPGAN model. The reliability and validity of generated meteorological data were analyzed and tested. The comparison between the generated data and the real data shows that they have similar statistical characteristics, and the synthetic data has achieved good results in the time series prediction of temperature changes. The MDPGAN model provides a convenient tool for the meteorology researches based on deep learning, which can automatically generate a large amount of safe and reliable data, especially suitable for the meteorology researches with small sample data.

General Extremal Field Method for Time-Optimal Trajectory Planning in Flow Fields

We present an algorithm to compute time-optimal trajectories for light vehicles in unsteady flow fields, with applications to long-range, low-power aircraft as well as underwater vehicles in ocean currents. The proposed approach aims at unifying various works from the literature on extremal fields and extends it by several features. First, we propose an exact scheme to deal with still obstacles. While being directly useful for pure obstacles, it is also of particular interest to ensure the validity of computation at the borders of the problem domain. Second, we demonstrate the method ability to deal with trajectory planning for long-range airborne missions with real weather data. Lastly, the source code, written in Python, is made open to the community to accelerate research in the domain.