Ktmu Hemapala

Low-voltage distribution networks (LVDNs) in urban and semi urban areas face increasing challenges in maintaining supply reliability due to limited automation, growing demand, and high penetration of distributed energy resources (DERs). This paper proposes a coordinated framework for supply restoration, demand management, and DER control using a novel multi parameter optimization framework with a priority-based objective function and real-time smart meter data. The approach integrates essential and non-essential load separation, smart field devices, and coordinated control strategies to enable adaptive network operation under normal and contingency conditions. The framework is evaluated using a modified IEEE 33 bus-based LVDN model with data derived from real smart meter measurements from a Sri Lankan LVDN. Results demonstrate that restoration time is reduced from approximately 2.5 h to a few minutes through automated switching. In addition, selective curtailment of non-essential loads improves service continuity, while coordinated DER control mitigates voltage rise under high photovoltaic penetration. A quantitative comparison confirms significant reductions in energy not served (ENS) and associated economic costs based on Public Utilities Commission of Sri Lanka values. The proposed framework provides a practical and scalable solution for enhancing reliability and enabling advanced distribution management in modern LVDNs.

The growing integration of variable renewable energy (VRE) sources into small-scale, isolated, low-inertia power systems has intensified frequency fluctuations, placing increased stress on conventional high-inertia generators and their governors. This study addresses the urgent need for a cost-effective, intermediate solution to mitigate these fluctuations, particularly in systems with limited access to high-capital technologies such as battery storage. A modified Particle Swarm Optimization (PSO) algorithm is proposed, integrating a non-linear inertia weight function and a storming mechanism to overcome the local optimum limitations of conventional PSO. The algorithm was applied to the Laxapana Hydro Complex in Sri Lanka, a key power generation and frequency regulation facility, and an ideal example of a small-scale isolated power system. Real-world system data were used in simulations conducted in MATLAB and PSSE environments. Simulation results across three operational scenarios: general generation fluctuations, solar intermittency, and wind intermittency, demonstrate that the optimized governor settings significantly reduce long-term mechanical movement while maintaining system stability. Specifically, under solar intermittency, governor actuator exhibited approximately a 20% reduction in movement compared to the baseline configuration. This reduction minimizes wear and tear, leading to lower maintenance costs and improved long-term reliability. These findings highlight the potential of the proposed method as a low-cost, adaptable solution for improving governor performance in renewable-integrated systems, offering a pathway toward more reliable and cost-effective power system operations.

In modern power systems, integrating distributed generation (DG) is essential to meet growing energy demands while optimizing operational and financial efficiency. However, arbitrary placement of DG units can significantly impact on power losses and voltage profiles of the network, depending on their capacity and location. Precise optimization of both the location and capacity of DG units is therefore crucial to maximizing their benefits and mitigating over-voltage issues. This paper presents an analytical approach that employs an Exact Loss Formula to determine the optimal capacity and location for DGs, specifically within unbalanced practical distribution networks, which remains an area not thoroughly addressed in previous literature. The proposed method is validated on the standard IEEE 33-bus network and further applied to a real-world distribution network in Kurunegala District, Sri Lanka, with rooftop solar as the DG. Test results demonstrate the effectiveness of the proposed method in minimizing power losses and addressing voltage-related challenges in unbalanced distribution systems.

Due to the sustainability requirements demanded by the world community, renewable energy-based power supply is highly appreciated at the consumer level. Also, the resiliency of the power supply is considered to ensure power system security. Microgrids are becoming role models of novel grid improvement and extension strategies due to their high controllability and ability to implement high-level grid functions, subjected to sustainability and resiliency. One of the key methods of establishing microgrids is by upgrading the existing distribution system into a controllable microgrid with required functionalities. Since this type of microgrid can be operated in either grid-connected or islanded modes, the microgrid controller plays a crucial role inside the microgrid setup. Smart Grid Research Lab (SGRL) of the University of Moratuwa is facilitated with 30kW research-level microgrid components and this paper discusses how the controlling structure of that microgrid is developed. This study comprises of two major components: simulation and hardware development. The simulation is conducted using a Real-Time Digital Simulator (RTDS). Furthermore, the hardware-level developments for establishing the microgrid controllers are based on the simulated outputs obtained from various case studies.

This research work explores the applicability of integrating storage solutions within solar power plants to mitigate plant intermittency by smoothing the plant’s output. The study employs a Moving Average algorithm to achieve this smoothing effect and validates its applicability using 30 days of continuous solar generation data. Additionally, the proposed approach calculates the minimum required storage by considering six distinct solar shading patterns. Moreover, this study extends to assess the viability of integrating storage in solar power plants within the Sri Lankan power system, incorporating an analysis of three different storage capacities while considering existing cost factors and tariff rates. Finally, this study concludes by stating recommendations to enhance the feasibility of integrating storage in solar power plants, aligning with the growing adoption of renewable energy within the Sri Lankan power system.

This research paper is a literature review of the integration of electric vehicles (EVs) with smart grids. An increase in the production of EVs results in power fluctuations in grid systems. The EV-grid integration requires massive communication technologies including collection, transmission, and distribution of real-time data. Modern vehicle-grid techniques could bring sustainable changes in electromobility, resulting in improvement of transport facilities and reducing pollution.

The main objective of the paper is home automation system using Bluetooth module and Arduino uno. In this study, we have made lights and fans be controlled by the app which is installed in our mobile phones. Single Pole Double Throw (SPDT) relays are used to control the amount of current flowing to the applications. The relays are connected to Arduino uno. We have done the coding part in Arduino IDE software which is installed in our device. Bluetooth module HC-05 is the connection between Arduino uno and the mobile phone. This module provides Universal Asynchronous Receive Transmit (UART) connection. LCD display is used as an indicator to the user about the working of the devices. This proposed system is useful for elders and physically handicapped persons to use it because it is tough for them to walk and use switches often. Since we have our phone at all time, we can switch ON/OFF the lights and fans remotely through our phone at all times.

To face the complexity of the system in several divisions, the systems like micro grids were upgraded to smart grids, with technological enhancements. Generally, the smart inverters play a considerable role as a main contributor on the smart grid in several ways. Improvements can be added to the current smart inverters in order to face the modern difficulties which the existing one cannot handle. The main objective of this research is to optimize the distributed energy generation either by limiting or stopping the unnecessary power injections to the utility grid. Therefore this novel energy management system is enriched with abilities such as changing its power output according to the condition of the utility grid under predefined grid codes and the dynamic battery charging and discharging ability. The controlling of this system is done as a separate function to the controlling mechanism of the inverter. The background of the energy management system is discussed, along with existing systems and their issues. A novel energy management system is presented in this paper with a simulation model, modeled on MATLAB Simulink software. The results of the proposed system show the benefits and the flexibility that can add to the existing power system.

State of charge (SOC) level can be considered as one of the imperative parts of the Battery Energy Management System (BEMS) for a microgrid power system to ensure that the battery is operating safely and reliably to give an optimum service for the system. There are several types of battery models are available and several methods are used to estimate the SOC level of a battery because there are certain types of drawbacks that can be identified in each battery model and estimation types. This study is focused on the Extended Kalman filter (EKF) method and machine learning time series forecasting method for the battery SOC level estimation and they are discussed in detail with the testing results by an electrothermal battery model. Finally, these results are compared and evaluated with the estimated results of the SOC level using the coulomb counting method. According to the final results, the mean absolute percentage errors of EKF and machine learning based SOC level estimation methods are 6.28% and 3.64% respectively.