Recently, clean power generation has received a lot of attention in modern power systems as a means to provide sustainability and high flexibility in the power industry. Due to this, modern energy systems are now reliant on Microgrids (MGs), which can easily access and operate Renewable Energy Systems (RES) and other Distributed Generations. The use of Microgrids plays a vital role in implementing clean and renewable energy. This will enhance energy security, making considerable financial savings, and lowering greenhouse gas emissions. In this paper, a grid-connected Microgrid system is considered as test model that includes solar photovoltaic (PV), wind turbine (WT), micro gas turbine (MT), fuel cell (FC), and battery energy storage system (BESS). The developed system is presented as a multi-objective function with constraints that can be resolved by a significant optimization method. Various commonly used Swarm Intelligence (SI) based meta-heuristic optimization techniques such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), Bacteria Forging Optimization (BFO), Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Cuckoo Search (CS), Bat Algorithm (BA), Firefly Algorithm (FFA), Krill Herd (KH), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) can be used to optimize above stochastic multi-objective problem of Microgrid. To assist researchers in selecting the best optimization approaches for their study, a comparison of above-mentioned SI based optimization techniques is provided in this paper. The discussion also includes upcoming research issues for Microgrid operation optimization.
This paper presents a hybrid methodology for improving load forecasting in electric power networks by combining the time-frequency data analysis method based on Empirical Mode Decomposition (EMD) with the Random Forest (RF) technique. The performance of the hybrid EMD-RF model is tested on real-time load data of Bengaluru city, Karnataka (India) from 01st January 2019 to 30th June 2019. An ensemble empirical mode decomposition is applied to decompose original load data into various signals known as intrinsic mode functions (IMF). The meteorological variables (MV) such as moisture content, dew point, dry bulb temperature, humidity, and solar irradiance (SR) are also taken into consideration for the day ahead seasonal STLF. The decomposed signals are further analysed using the ensemble learning-based Random Forest (RF) technique. The result obtained from the model is aggregated to obtain the final forecasted result. The superiority of the hybrid EMD-RF model is established through a comparative statistical error analysis with other non-decomposition and decomposition methods based on EMD-Bagging, EMD-ANN, Artificial Neural Network (ANN), Bagging, and Random Forest (RF).
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