Using Model Predictive Control in Renewable Energy Sources Rosli, Anis Najwa; Yusoff, Siti Hajar; Mansor, Hasmah ...
2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA),
2023-Oct.-17
Conference Proceeding
The integration of renewable energy sources (RES) poses challenges due to their intermittent nature and the requirement for grid stability and reliability. This study explores the application of ...Model Predictive Control (MPC) as an advanced control strategy for effective RES integration. MPC utilizes mathematical models and prediction algorithms to optimize power flow, manage system dynamics, and mitigate the intermittent nature of RES. Simulations conducted using Simulink and MATLAB platforms demonstrate the effectiveness of the MPC controller in improving grid stability, optimizing power generation, and accommodating fluctuating RES output. The results highlight the potential of MPC to enhance the integration of RES into the power grid, paving the way for a sustainable and resilient energy infrastructure. Additionally, a comparison between the MPC controller and the Proportional Integral (PI) controller was performed, indicating that MPC outperforms the PI controller.
Greenhouse gas emissions and energy usage are increasing rapidly, posing a significant threat to the environment and the sustainability of the Earth. One of the ways to mitigate this issue is through ...the implementation of Energy Management Systems (EnMS) in industries, where overall efficiency improvement and energy expenditures cutback are targeted. This work aims to address the energy consumption in a can manufacturing factory. Here, machine learning approach is used to optimize the energy performance of the compressed air system (CAS) which is the major contributor to the factory's energy usage at 38%. In this work, the Energy Performance Indicators (EnPI) of the CAS is predicted using KNN by employing the variables from CAS itself. The collected variables are first analyzed using Dataiku software and correlation matrix and heatmap technique, giving two sets of key features. The predicted EnPI showed high accuracy when compared to the actual values.
In this age of technology, the higher demand of renewable energy has become the main factor that contributes to the increasing power consumption. The electrical power generated needs to match with ...the load demand or the power consumption of the consumers, or else it will lead to the mismatch of supply and demand problem. However, the spike in the load demand will impact the electricity cost of the consumers. Therefore, the purpose of this research is to find an optimal solution to shave the peak load to reduce the electricity bills of the consumers. There are a lot of controllers that can be used to control the scheduling of the battery, and this research will focus on battery scheduling by using Model Predictive Controller (MPC). The MPC technique is able to control the battery energy storage system (BESS) to discharge during the peak hours and also charge during the off-peak hours. However, the MPC has a complex and high optimization algorithm due to its dynamic model, but MPC technique ensures that it is environment-friendly compared to the traditional method of peak-shaving. This research proposed the battery logic control to schedule the charge-discharge of the battery and for the MPC to optimize and get the best output for the peak shaving. The MPC design will be done by using MATLAB as the main software as it is very ideal to simulate the peak shaving by using MPC optimization
