The unprecedented growth of Renewable Energy Sources (RES) positions solar power as a leading contender in the global energy mix. Solar energy offers a sustainable alternative to fossil fuels, mitigating carbon emissions and promoting environmental sustainability. This study explores the crucial role of forecasting algorithms within photovoltaic (PV) systems. We aim to provide a comprehensive understanding of methodologies, datasets, and recent advancements for enhancing predictive accuracy in solar power generation forecasting. While machine learning has dominated previous research, recent studies highlight challenges in achieving optimal efficiency and accuracy. A significant obstacle lies in the deficiency of real-world application for large-scale specifically for solar power generation forecasting. To address this gap, this study defines prevalent forecasting methodologies and illuminates datasets with diverse characteristics and their relevance. This study meticulously provides and explore recent advanced methods and datasets, emphasizing their impact on forecasting performance. This study not only deepens our understanding of existing methodologies but also provides valuable insights for future advancements in solar power generation forecasting.

Renewable energy (RE) is alternative energy to replace fossil fuels in electric power generation and has evolved into microgrid technology. Integration of RE has caused voltage stability issues in the power system. Reports in earlier studies have included three voltage control methods such as Model Predictive Control (MPC), Proportional Integral (PI) controller, and negative feed-forward voltage control implemented to ensure the voltage stability of microgrids. However, very few research reports apply voltage control in hybrid biomass (BM)-solar photovoltaic (PV)-wind microgrid, choosing only to focus on energy management, economic analysis, and best sizing. For this reason, the main objective of this paper is the integration of a hybrid BM-Solar PV-Wind off-grid microgrid comprising PI controller as the voltage control based on an actual input database at a location in a small rural town named Mersing in Malaysia. Additionally, this paper also intends to illustrate the implemented efforts to support the voltage stability of the system in Mersing. PI controller with a harmonic filter voltage controller is implemented in this study to reduce the total harmonic distortion (THD) percentage. Concurrently the measured THD voltage and current at each RE and distribution line show a percentage below 10%. It is thus shown that a hybrid BM-Solar PV-wind microgrid is stable, especially for distribution lines, according to harmonic standards in the electricity supply application book by the local electricity provider Tenaga Nasional Berhad (TNB). Indeed, a PI controller with a harmonic filter has proven as an effective method for controlling the voltage instability in this study.

Over the past few decades, the electric power industry evolved in response to growing concerns about climate change and the rising price of fossil fuels. The usage of renewable energy sources (RES) rose as a remedy for these problems. The increased penetration of RES in the existing generation system increased the need for an intelligent energy management system (EMS) so that the system can operate in any possible circumstances. Many sectors of society, including the education sector, are working to realize the importance of this sustainable energy system. This paper reviews the process of selecting an efficient control technique for continuous power flow from different RES to meet the load demand requirement using an enhanced model predictive control (MPC)-based EMS framework. This EMS is a software platform to provide fundamental support services and applications to deliver the functionality needed for the effective operation of electrical generation and transmission facilities to ensure adequate security of energy supply at minimum cost. The centralized EMS with technical objectives focusing on power quality and seamless power flow can be achieved through dynamically enhanced MPC.

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

The new electrical motor used as the automotive engines seen to be very efficient which use only clean energy generated as one of the efforts to minimize the carbon emissions. The high usage of electric vehicle around the world for responding to the demands of taking care the ecological system has establish many Electric Vehicles (EV) charging station to accommodate the user. However, high charging loads of fast charging stations result in increased peak load demand, reduced reserve margins and voltage instability. Thus, the optimal distribution network reconfiguration with interconnection of charging station is this solution for this issue. The finding of the optimal reconfiguration in this study are used to improve the voltage profile and minimize the power loss of the distribution system. The best reconfiguration is searched by using Cuckoo Search Algorithm (CSA). The result of the finding then will be verified by comparing to other research papers for its efficiency of the method used.

The distribution system has the most portion power loss compared to the transmission and generation systems. One of the effective methods to reduce the power loss in the system is by reconfiguring the existing network. In distribution system, there are two types of switches, which are sectionalizing switches and tie-switches. Reconfiguration process changes the status of those switches until the objective is achieved. In this study, the reconfiguration method is proposed for distribution system using the Cuckoo Search Algorithm (CSA) method. The system used is a standard IEEE 33-bus radial distribution system. The main objective is to reduce the power loss in the system while satisfying the distribution constraints. The proposed method is used to give an optimal configuration of distribution network for power loss reduction and its validity is done by comparing it with Particle Swarm Optimization (PSO).