Effective management of Distributed Energy Resources (DERs) and optimization of grid operations are crucial responsibilities of Distribution System Operators (DSOs). Hence, this comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. These goals underscore the significance of clean and accessible energy, advancements in infrastructure, sustainable urban development, climate change mitigation, and collaborative partnerships. The review's core focuses on the DER forecasting techniques employed by DSOs. It explores various aspects, including data collection methods, load forecasting models, DER generation forecasting, aggregation and integration techniques, and the role of advanced technologies like machine learning and artificial intelligence. The review highlights the critical role of accurate DER forecasts in optimizing grid operations, managing energy flows, and facilitating the integration of renewable energy sources. Furthermore, the review examines the implications of DER forecasting for DSOs in achieving the SDGs. It discusses how DER forecasting facilitates the transition to affordable and clean energy, enhances industry innovation and infrastructure, builds sustainable cities and communities, drives climate action efforts, and fosters stakeholder partnerships. However, the review also identifies challenges and limitations in DER forecasting, including data availability, forecasting accuracy, uncertainty management, and regulatory barriers. It emphasizes further research and development in improved forecasting algorithms, advanced data analytics, and enhanced communication and coordination mechanisms. Finally, this comprehensive critical review highlights the importance of DER forecasting for DSOs in achieving the SDGs. Accurate forecasting can promote sustainable and clean energy practices, drive innovation, build resilient communities, mitigate climate change, and foster collaborative partnerships. The review emphasizes the necessity of advancing DER forecasting techniques and addressing associated challenges to fully realize the potential of DERs in contributing to a sustainable and inclusive future.

Battery energy storage systems (BESSs) and plug-in hybrid electric vehicles (PHEVs) are essential for microgrid operations to be financially viable. PHEVs can serve as mobile storage devices, storing excess energy during times of low demand and delivering it during times of high demand. By offering reliable on-site energy storage, BESSs improve cost efficiency by allowing the microgrid to store cheap, off-peak electricity and release it when prices increase. To minimize generation costs and alleviate grid stress during periods of high demand, load-shifting policies shift inelastic loads to off-peak hours when energy prices are lower. When combined, these tactics support dependable, affordable, and effective microgrid management. A recently developed RIME algorithm is used as the optimization tool to reduce the total operating cost (TOC) of an MG system for three cases and three situations. The cases emphasize a modified load demand style influenced by the optimal load-shifting method (OLSM) and order characteristics load-shifting policy (OCLSP), whereas the situations refer to the inclusion of ESS in the MG system. The TOC decreased from $2624 without ESS to $2611 and $2331 with PHEVs and BESSs, respectively. These costs were further reduced to $1192, $1162, and $1147, respectively, when OLSM was implemented to restructure the base load demand. Additionally, a balance between a minimal TOC and carbon emission was obtained when an OLSM-based load demand model was used with BESSs. The RIME algorithm outperformed many recently developed algorithms and is consistent and robust, yielding better quality solutions.

From time to time, a series of unpredictable and conflicting contingencies can lead to angular instability of the power system and even blackouts if not adequately handled by an out-of-step (OOS) protection system. The key contribution of this research work, to the theory of out-of-step protection, is the identification and isolation after a given disruption of many unstable swings. This paper presents a proposed method to avoid false operation for distance function by out-of-step blocking to improve the system stability by using optimally placed PMUs for the fast detection of system analogue quantities. The studies were performed on a modified Eskom transmission network in the Western Cape with 765 kV and 400 kV voltage levels. The aim is to investigate the IEC 61850-90-5 standard for predictive dynamic stability maintaining systems using PMUs for out-of-step conditions of synchronous generators. The power system modelling and simulation are performed in the RSCAD-FX for the proposed multi-area power system network. An experimental lab-scale implementation is built to test the proposed out-of-step algorithm in a real-time digital simulator. Software-based PMU is incorporated to test and validate the IEC 61850-90-5 standard sampled values. Simulation and experimental results are presented.