DC power systems have emerged as a cost-effective solution for electric power generation and transmission, challenging the dominance of AC distribution systems. However, a comprehensive efficiency comparison between DC and AC microgrids remains understudied. This study seeks to explore and conduct a thorough survey on development and designing of DC microgrids to address this gap. Firstly, a comprehensive literature review comparing the efficiencies of AC and DC microgrids has been presented. The analysis highlights the superior efficiency of DC distribution systems over AC systems, supported by detailed advantages. Secondly, hardware implementation has been performed to directly compare the efficiency of DC versus AC systems. Research validity and application are further improved by the hardware prototype’s scalability, which in simulation allows for a thorough assessment of system stability over a range of scenarios from four to six terminals. Test results from the built hardware prototype demonstrate an astounding 15% increase in efficiency using the DC system compared to the AC system, demonstrating its potential for improved performance in real-world scenarios. In simulation results, the designed DC microgrid demonstrates stable voltages of 500V under steady state operation and rapid recovery within 80 ms under both symmetrical and asymmetrical faults has been observed. The research being investigated utilizes hardware implementation and simulation to provide useful insights into the efficiency and stability of DC microgrids in comparison to AC systems. These results are important for developing robust power distribution networks in modern energy environments, promoting sustainability and dependability in infrastructure growth.

Grid-connected microgrids consisting of renewable energy sources, battery storage, and load require an appropriate energy management system that controls the battery operation. Traditionally, the operation of the battery is optimised using 24 h of forecasted data of load demand and renewable energy sources (RES) generation using offline optimisation techniques, where the battery actions (charge/discharge/idle) are determined before the start of the day. Reinforcement Learning (RL) has recently been suggested as an alternative to these traditional techniques due to its ability to learn optimal policy online using real data. Two approaches of RL have been suggested in the literature viz. offline and online. In offline RL, the agent learns the optimum policy using predicted generation and load data. Once convergence is achieved, battery commands are dispatched in real time. This method is similar to traditional methods because it relies on forecasted data. In online RL, on the other hand, the agent learns the optimum policy by interacting with the system in real time using real data. This paper investigates the effectiveness of both the approaches. White Gaussian noise with different standard deviations was added to real data to create synthetic predicted data to validate the method. In the first approach, the predicted data were used by an offline RL algorithm. In the second approach, the online RL algorithm interacted with real streaming data in real time, and the agent was trained using real data. When the energy costs of the two approaches were compared, it was found that the online RL provides better results than the offline approach if the difference between real and predicted data is greater than 1.6%.

Real-time energy management of battery storage in grid-connected microgrids can be very challenging due to the intermittent nature of renewable energy sources (RES), load variations, and variable grid tariffs. Two reinforcement learning (RL)–based energy management systems have been previously used, namely, offline and online methods. In offline RL, the agent learns the optimum policy using forecasted generation and load data. Once the convergence is achieved, battery commands are dispatched in real time. The performance of this strategy highly depends on the accuracy of the forecasted data. An agent in online RL learns the best policy by interacting with the system in real time using real data. Online RL deals better with the forecasted error but can take a longer time to converge. This paper proposes a novel dual layer Q-learning strategy to address this challenge. The first (upper) layer is conducted offline to produce directive commands for the battery system for a 24 h horizon. It uses forecasted data for generation and load. The second (lower) Q-learning-based layer refines these battery commands every 15 min by considering the changes happening in the RES and load demand in real time. This decreases the overall operating cost of the microgrid as compared with online RL by reducing the convergence time. The superiority of the proposed strategy (dual-layer RL) has been verified by simulation results after comparing it with individual offline and online RL algorithms.

In today's world, telecommunications infrastructure holds a significance to that of roadways in the early 19th century, serving as a vital link for governments and individuals alike. With heightened market competition and decreasing calling rates, coupled with rising expectations for Quality of Service (QoS), operators are striving to enhance QoS while optimizing resources to manage operational costs. However, challenges such as power shortages and fuel theft persist, leading to frequent network outages. To address these issues, a remote monitoring systems is proposed to prevent fuel theft and report electrical parameters remotely. This system proposes a redundant communication pathway using existing cell site’s physical alarms, eliminating the need for additional servers and SIM cards. Notably, it enhances fault detection capabilities, particularly in detecting gradual fuel theft and addressing voltage fluctuations. These advancements promise to significantly reduce operational expenditure and increase network availability, thereby positively impacting cellular operators' revenue streams.

Owing to its essential features, such as modularity and exceptional power quality, the modular multilevel converter (MMC) emerges as the optimal converter topology for high-voltage direct current (HVDC) applications. Traditionally, MMCs are controlled through a method called nearest level modulation (NLM), which generates N+1 AC output voltages, where N represents the number of sub modules (SMs) per arm. In this paper, we introduce a modified NLM technique designed to yield 2N+1 and 4N+1 levels, with a focus on efficiently controlling internal dynamics. The proposed MMC is evaluated using a hardware-in-the-loop (HIL) environment to obtain real-time simulation outcomes. This MMC topology demonstrates a reduction in circulating currents and capacitor voltage ripple.