The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

Novel Battery Management with Fuzzy Tuned Low Voltage Chopper and Machine Learning Controlled Drive for Electric Vehicle Battery Management: A Pathway Towards SDG

Objective: The target of this paper is to implement an economical chopper and electrical converter for the hybrid windsolar system. Method: A replacement topology of a mix of CUK and SEPIC convertor is used rather than a private DC/DC convertor. The motor load is provided by victimization the two diodes that connects each wind and solar energy singly or along depending on accessibility. With the improved management in DC/DC convertor and within the electrical converter that associated with the auxiliary circuit in accumulation by the closed-loop system PI controller a more robust control is achieved. Findings: The fused chopper replaced the separate solar and wind choppers and rather than typical electrical converter the novel pulse width modulated electrical converter used supplied with a correct pulse width modulation (PWM) control using 3 equal reference signals. The closed-loop system management is achieved with the digital PI controller and MPPT draws the maximum power from the solar that improves the response with the fast raise in load. The system is validated with MATLAB/Simulink and therefore the output voltage of fused chopper for various solar irradiance and wind speed are tabulated. The output voltage of electrical converter and therefore the motor characteristics are analyzed. Applications/Improvements: For standalone applications within the agriculture water pumping the requirement will satisfy with the hybrid wind solar system.

The novel hybrid power system integrating energy storage, electric vehicle (EV) charging infrastructure and renewable energy sources improve sustainability and resilience. This work proposes a power management system controlled by artificial intelligence for EV charging infrastructure. The battery’s state of charge (SoC) is continuously monitored by artificial neural network (ANN) algorithm improves the health of the battery and efficient operation of the system. The buck boost DC-DC converter performs dynamic switching mechanism, which adjusts to changing solar conditions and energy demands, guarantees a steady power supply. Depending on the situation, the ANN algorithm used in the battery’s SoC control mechanism decides whether to priorities the EV charging or the inverter to supply the grid. The work is evaluated with the MATLAB simulation for different conditions and results are compared with different controllers such as PI, PID, and ANN. The experiment performed uses internet of things (IoT) for transferring the data from the EV motor, acts as an input for the controller to perform the novel hybrid power management operation with cloud technology. The experimental prototype evaluates the results connected to the photovoltaic (PV) system and battery management system (BMS) which lowers reliance on non-renewable resources, increases overall energy efficiency, and ensures a steady supply of power under a various condition.