Hybrid Renewable Power Generation for Modeling and Controlling the Battery Storage Photovoltaic System

A major portion of the global energy demand was likely to be fulfilled by an extensive supply of renewable power. Renewable energy outputs, on the other hand, are changeable due to the dynamic nature of their sources. The integration of these variable sources of power into current power grids is proving difficult for electrical power system operators all around the world. The fundamental issue with renewable energy systems is that, due to the stochastic nature of renewable power, electricity production varies from period to period. Recent research and development on renewable technologies can ensure the islands’ long-term electricity supply. Renewable energy sources, on the other hand, are limited by their unpredictable nature and significant reliance on weather conditions. To offset this disadvantage, several renewable energy sources and converters must be joined. To balance the power generation and load power, a hybrid renewable power generation for standalone application is proposed. The solar plant model is made up of a 170 W photovoltaic (PV) panel connected in series, and conversion of energy is done using the maximum power point tracking (MPPT) algorithm, which regulates a buck-boost converter modulation. The MPPT method used in the converter’s control step is based on perturb and observe (P&O) and enhanced with a PI controller. The bidirectional buck-boost DC-DC converters (BBDC) are utilized to preserve a DC-link voltage stable. This is also storing additional hybrid energy in a large battery and is distributed to the system load; then there is a shortage of hybrid power. The load current power is regulated in terms of the frequency and enables it to be achieved using three vector control technique voltage source inverters (VSI). The results were offered to demonstrate a hybrid performance of this organization

Evaluating the performance of a hybrid cooling and heating power system using Carbon dioxide energy storage

Energy storing could correct an imbalance in the solar to electric power ratios among a Combined Heating, cooling and power system and its consumers, improving energy performance dramatically. Energy storage, on the other hand, adds to the complexities of the device’s operational efficiency. While assessing the device’s CO2 emissions in the operational state, the analysis comprises complete thermodynamics and thermo-economic evaluation. The effect of designing element modification on application functionality was examined next by varying the designed characteristics. Lastly, the proposed hybrid CCHP system is optimized for three objective operations: normalized exergy performance, CO2 emissions, and energy effectiveness. This paper presents a unique tri-generation method depending on the Trans-critical Brayton cycle and carbon dioxide energy storage (CO2ES). The stored capacity has a minor impact on the framework’s operating and cooling ranges, but the development in pressure change through the first throttle valve and temperature conditions broadens them. The capacity for heating and cooling rises in lockstep with stored pressures and falls in lock-step with differential pressure via the first throttle pressure regulator and ambient temperature. Moreover, the computational power is adequate for power system management. The proposed approach could also be used to optimize the functioning of a CCHP framework while taking into account demand-side responses

IoT Based Virtual E‑Learning System for Sustainable Development of Smart Cities

Globally, cities are emerging into Smart Cities (SC) as a result of sustainable cities and the adaption of recent Internet of Things (IoT) technology. It is becoming essential to involve students in sustainability as engineering and technology are crucial elements in fixing the past adverse effects on our globe. Engineering e-learners are being educated on the sustainable development of SC in many Smart e-learning Tools (SeT) and infrastructure faculties around the world, especially in developing Asian countries such as India. This research paper presents an advanced solution for interactive Smart Learning Environment (SLE) systems based on new emerging technological trends of the IoT. The IoT-Ve- LS method used in the design and implementation allows flexible usage and integration of the online courses by SLE. The impacts of empirical E-learning evaluation on implementing IoT techniques in online tutoring have been analysed to find out its research hypothesis. Our IoT-sensor-based Reservoir Computing allows the classification of short-term learning language sentences relatively quickly, highlighting the minimal training time and optimized solution of real-time cases for controlling temporal and sequential signals at the cloud computing level. The triangulation analysis in information gathering endorses the theoretical models that use computable and personalized approaches