Develop a micro grid control platform for sustainable energy management in distribution network

Abstract

The Senate Research Committee (SRC) promotes research by giving funding for research. In addition to the monthly stipend payment, the allocated payments for publications, hardware purchasing, and transportation is highly imperative to achieve the proposed objectives successfully within the time duration. Moreover, guidance of the supervisor who provided the SRC grant for the research is also essential factor to meet the requirements of the research. Microgrids and Battery Energy Storage Systems play a major role in the sustainable and clean energy sector. In this study, an accurate battery model is developed which can represent the electrical and the thermal behavior of the battery and all the battery parameters are investigated experimentally by implementing a test bench for the proposed model and moreover, each of the parameters is validated theoretically by developing a MATLAB simulation. To improve the accuracy of the battery parametrization, battery state of charge (SOC) level is estimated via machine learning algorithms. According to the results, the accuracy of the comprehensive electro-thermal battery model based on electrical and thermal parameters is at a satisfactory level and proves that designing such a model that achieves excellent accuracy and realistic behavior in real-time platform simulators. There are six different time series models are used to estimate the SOC level and according to the performance, auto regressive (AR) model and seasonal auto regressive integrated moving average (SARIMA) models are the best machine learning models for SOC level estimation when battery parametrization.