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This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV's five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources such as solar and wind causes fluctuations in power generation. Hence energy storage is necessary in standalone power systems. It is challenging to use batteries as power backups because they cannot handle rapid power fluctuations without compromising battery life. This paper proposes a hybrid energy storage system (HESS) for wind energy-based power systems that includes a battery for long-term energy management with a super capacitor for quick dynamic power regulation. Pulse charging of the proposed HESS is carried out with the help of a dual active bridge (DAB) converter. Pulse charging of the HESS will result into reduced stress on battery and eventually to a longer self-life. The operation of the wind energy fed hybrid battery-supercapacitor energy storage was investigated through simulation using MATLAB-Simulink. For validating the simulation results, an experimental test bench is created using a real DAB prototype and TI Piccolo-F280049 microcontroller. [ABSTRACT FROM AUTHOR]

The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria's power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system's dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.

• Development of generalized probabilistic model for wind speed intermittency by utilizing real-time acquired cloud-based IoT wind speed data for wind energy system in a SA paradigm. • Simplified reliability analysis through multi-state modeling and probability distribution function for wind-integrated systems, departing from conventional convolution-based approaches. • Forecasting of wind speed and wind power generation for two days in advance through a time-series non-linear autoregressive model with exogenous inputs (NARX), and estimation of the operational reliability using the forecasted wind speed and wind power data. The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations. [ABSTRACT FROM AUTHOR]

Optimizing energy structure is important for coping with the increasing environmental problems in iron and steel industry. Wind energy is one of typical zero-carbon, inexhaustible, and accessible energy source for alleviating energy dilemma. In this paper, the electric arc furnace steelmaking processes integrated with wind energy system (EAF-WES) is developed. The input-output data of 1214 and 1536 heats with 30 % and 50 % hot metal ratio are traced and collected. Life cycle assessment and techno-economic analysis of EAF route integrated with traditional energy system (EAF-TES) and EAF-WES are further researched and compared from perspectives of life cycle carbon footprint (LCCF), production cost, wind energy price (WEP), carbon efficiency, carbon reduction benefit (CRB), and carbon tax. The results indicate that EAF-WES enjoys lower LCCF but higher production cost and carbon efficiency than EAF-TES. Considering the price fluctuations, the optimal EAF steelmaking pattern with lowest production cost under different extent of WEP and carbon tax is suggested. When the electricity price is 0.043 and 0.046 $/kWh, the CRB is balanced. It is concluded that EAF-WES is a low-carbon, high-productive, environmentally friendly, and economical route. This research is expected to supply a technical idea for energy conservation and emission reduction in future steel production. [Display omitted] • EAF steelmaking route integrated with wind energy system (EAF-WES) is developed. • Comparative life cycle assessment and techno-economic analysis of EAF-WES is conducted. • Life cycle inventories of EAF route with hot metal ratio of 30 % and 50 % are converged. • EAF-WES with lower hot metal ratio is beneficial for energy conservation and emission reduction in green steelmaking route. [ABSTRACT FROM AUTHOR]

• Advanced situational awareness for operational reliability for wind energy systems (WES). • Comprehensive uncertainty analysis of wind speed and the development of a multi-state model for accurately capturing the variability of wind generator power output. • Time-series forecasting of wind speed and power generation for two days ahead employing a non-linear autoregressive with exogenous input ANN (NARX-ANN) model to project operational reliability. • Implementation of an IoT-based real-time monitoring framework to enhance the projection and management of WES operational reliability. Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively. [ABSTRACT FROM AUTHOR]