Colombia is well-positioned for the development of sustainable energy due to its abundance of natural resources, which include water, wind, and sun. Regulating the safe and sustainable use of offshore wind energy, which is considered non-conventional, is lacking in the nation, nonetheless. The development of offshore wind technology in Colombia shows potential to meet energy needs during dry hydrological conditions and El Niño/Southern Oscillation events when the hydroelectric system power supply is low. This study examines global initiatives that have encouraged nations to develop plans for cutting their CO2 emissions, stressing both their successes and shortcomings in putting offshore wind technology into practice. An examination of Colombia\'s renewable energy administrative framework finds a lack of data required to carry out offshore wind projects. Furthermore, a review of previous research on marine energy emphasizes how important it is to expand our knowledge of offshore wind generation. Although the majority of local renewable energy projects concentrate on terrestrial sources, an analysis of wind speed and wind power density in Colombia at different altitudes shows promising magnitudes and good trends.Digital finance plays a crucial role in this context by providing innovative funding mechanisms, enhancing financial accessibility, and reducing investment risks through improved financial technologies. These advancements support the mobilization of capital necessary for the development and expansion of offshore wind energy projects.As a result, the technical, economic, administrative, and legal data pertinent to renewable energy in Colombia is compiled in this study. It proposes to provide information to stakeholders involved in decision-making processes and promotes the possible installation of offshore wind farms in regions close to Colombia\'s Caribbean coast. Because of its plentiful resources, Colombia offers a great chance to implement offshore wind energy technology, which will lessen dependency on fossil fuels and provide a backup energy source in case of supply shortages. The integration of digital finance is key to unlocking the economic potential of these projects, ensuring sustainable and scalable energy solutions for the future.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model\'s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Controlling wind flow on vertical axis wind turbine blades is an effective technique for enhancing their performance. Modern equipment such as plasma actuators have gained significant attention for their ability to control, and improve the flow behavior in wind turbines. Previous studies have primarily focused on investigating plasma actuators with constant force. In this study, plasma actuators with varying forces over time were applied to the turbine blades. An unsteady 2D model was used to analyze the wind turbine. The sliding mesh model was employed to simulate rotor rotation, and the SST k - ω model was utilized for turbulence modeling. Initially, the performance of the clean turbine was examined. In the next step, the plasma actuators with different force waveforms were applied to the wind turbine blades, including constant, sine, cosine, positive ramp, negative ramp, pulse in the first half-cycle, and pulse in the last half-cycle waveforms. The results indicated that the cosine, and sinusoidal waveforms, led to the greatest improvement with 37.28% and 35.59% increase in the net energy produced by the turbine, respectively, compared to the baseline case.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

As the population of Somaliland continues to grow rapidly, the demand for electricity is anticipated to rise exponentially over the next few decades. The provision of reliable and cost-effective electricity service is at the core of the economic and social development of Somaliland. Wind energy might offer a sustainable solution to the exceptionally high electricity prices. In this study, a techno-economic assessment of the wind energy potential in some parts of the western region of Somaliland is performed. Measured data of wind speed and wind direction for three sites around the capital city of Hargeisa are utilized to characterize the resource using Weibull distribution functions. Technical and economic performances of several commercial wind turbines are examined. Out of the three sites, Xumba Weyne stands out as the most favorable site for wind energy harnessing with average annual power and energy densities at 80 m hub height of 317 kW/m2 and 2782 kWh/m2, respectively. Wind turbines installed in Xumba Weyne yielded the lowest levelized cost of electricity (LCOE) of not more than 0.07 $/kWh, shortest payback times (i.e., less than 7.2 years) with minimum return on investment (ROI) of approximately 150%.

Energy harvesting techniques can exploit even subtle passive motion like that of plant leaves in wind as a consequence of contact electrification of the leaf surface. The effect is strongly enhanced by artificial materials installed as \'artificial leaves\' on the natural leaves creating a recurring mechanical contact and separation. However, this requires a controlled mechanical interaction between the biological and the artificial component during the complex wind motion. Here, we build and test four artificial leaf designs with varying flexibility and degrees of freedom across the blade operating onNerium oleanderplants. We evaluate the apparent contact area (up to 10 cm2per leaf), the leaves\' motion, together with the generated voltage, current and charge in low wind speeds of up to 3.3 m s-1and less. Single artificial leaves produced over 75 V and 1µA current peaks. Softer artificial leaves increase the contact area accessible for energy conversion, but a balance between softer and stiffer elements in the artificial blade is optimal to increase the frequency of contact-separation motion (here up to 10 Hz) for energy conversion also below 3.3 m s-1. Moreover, we tested how multiple leaves operating collectively during continuous wind energy harvesting over several days achieve a root mean square power of ∼6µW and are capable to transfer ∼80µC every 30-40 min to power a wireless temperature and humidity sensor autonomously and recurrently. The results experimentally reveal design strategies for energy harvesters providing autonomous micro power sources in plant ecosystems for example for sensing in precision agriculture and remote environmental monitoring.

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Energy transitions are knowledge-intensive processes where a multitude of actors are trying to cope with inevitable knowledge gaps, surprises, and uncertainties. In this context, we focus on two techno-physical phenomena that are gaining practical relevance with the expansion of wind and geothermal energy extraction, and are surrounded by significant unknowns: wake effects and temperature plumes. Both phenomena can potentially affect the efficiency of energy production, but the extent of their impact is not yet known. Based on 28 semi-structured interviews with experts in the fields of wind and geothermal energy, we explore how different central actors perceive and interpret non-knowledge of wake effects and temperature plumes, and how they deal with it. We show that there are strategies for either using non-knowledge as a basis for action or simply ignoring it and sweeping knowledge gaps under the rug. Both strategies serve the function of protecting agency and keeping things going.

of wind characteristics and assessment of wind energy resource is carried out at a location in Tonga with the help of twelve months of measurements carried out at 34 m and 20 m heights above ground level. The daily, monthly and annual averages are computed. The wind shear analysis and its diurnal variation were studied and compared with the temperature variation. The turbulence levels at the two heights were estimated for the entire measurement period as well as for some typical days. For estimating the Weibull parameters, eleven methods were employed along with goodness of fit and error estimates to find the best method. The overall averaged wind speed for the entire period of study is estimated to be 4.41 m/s at 34 m above ground level. The predominant wind direction was south-east for Tonga. \'Moments\' is seen to be the best method to determine accurate Weibull parameters. The average net annual energy production from one Vergnet 275 kW wind turbine is 198.57 MWh. A payback period of 8.95 years by installing five turbines near the measurement location was estimated, which is very encouraging in terms of investment. Installing wind turbines will lower the heavy reliance on the imported fossil fuels in the country and also help in achieving Sustainable Development Goal 7.