Power electronics is seeing an increase in the use of sophisticated self-learning controllers as single board computers and microcontrollers progress faster. Traditional controllers, such as PI controllers, suffer from transient instability difficulties. The duty cycle and output voltage of a DC/DC converter are not linear. Due to this non-linearity, the PI controller generates variable levels of voltage fluctuations depending on the operating region of the converter. In some cases, non-linear controllers outperform PI controllers. The non-linear model of a non-linear controller is determined by data availability. So, a self-calibrating controller that collects data and optimizes itself as the operation goes on is necessary. Iteration and oscillation can be minimized with a well-trained reinforcement learning model utilizing a non-linear policy. A boost converter's output power supply capacity changes with a change in load, due to which the maximum duty cycle limit of a converter also changes. A support vector calibrated by reinforcement learning can dynamically change the duty cycle limit of a converter under variable load. This research highlights how reinforcement learning-based non-linear controllers can improve control and efficiency over standard controllers. The proposed concept is based on a microgrid system. Simulation and experimental analysis have been conducted on how reinforcement learning-based controller works for DC-DC boost converter.

Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.

Nepal is a Himalayan country with its 83% of its geography being composed of hills and mountains. Around 22% of the Nepalese population is not receiving electricity through the national power utility and is forced to identify alternative approaches to electrification. The Micro/Mini-Grid (MG) system is one of the promising approaches in terms of cost, reliability and performance for rural electrification, where electrification through national power utility is not techno-economically feasible. However, various issues must be identified and considered during the implementation of MGs in a rural community. In this paper, numerous technical, social and management issues are identified and discussed relating to the implementation and operation of reliable and stable MGs in the Himalayas. To our knowledge, this is the first scientific work that presents a comprehensive review of Himalayan MGs and their associated elements. This article reviews the available research articles, project documents, and Government reports on MG development, from which a clear roadmap is constructed. From the comprehensive study, it is observed that the existing MGs are not adequately designed for the specific area, considering the local resources and local information. Based on the identified issues, some practical and efficient recommendations have been made, so that future MG projects will address the possible problems during the design and implementation phase.

Accompanying the advancement on the Internet of Things (IoT), the concept of remote monitoring and control using IoT devices is becoming popular. Digital smart meters hold many advantages over traditional analog meters, and smart metering is one of application of IoT technology. It supports the conventional power system in adopting modern concepts like smart grids, block-chains, automation, etc. due to their remote load monitoring and control capabilities. However, in many applications, the traditional analog meters still are preferred over digital smart meters due to the high deployment and operating costs, and the unreliability of the smart meters. The primary reasons behind these issues are a lack of a reliable and affordable communication system, which can be addressed by the deployment of a dedicated network formed with a Low Power Wide Area (LPWA) platform like wireless radio standards (i.e., LoRa devices). This paper discusses LoRa technology and its implementation to solve the problems associated with smart metering, especially considering the rural energy system. A simulation-based study has been done to analyse the LoRa technology’s applicability in different architecture for smart metering purposes and to identify a cost-effective and reliable way to implement smart metering, especially in a rural microgrid (MG).