Optimal operation of an integrated hybrid renewable energy system with demand-side management in a rural context

A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB (c) environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system's performance.Web of Science1514art. no. 517

Using Energy Conservation-Based Demand-Side Management to Optimize an Off-Grid Integrated Renewable Energy System Using Different Battery Technologies

Rural electrification is necessary for both the country’s development and the well-being of the villagers. The current study investigates the feasibility of providing electricity to off-grid villages in the Indian state of Odisha by utilizing renewable energy resources that are currently available in the study area. However, due to the intermittent nature of renewable energy sources, it is highly improbable to ensure a continuous electricity supply to the off-grid areas. To ensure a reliable electricity supply to the off-grid areas, three battery technologies have been incorporated to find the most suitable battery system for the study area. In addition, we evaluated various demand side management (DSM) techniques and assessed which would be the most suitable for our study area. To assess the efficiency of the off-grid system, we applied different metaheuristic algorithms, and the results showed great promise. Based on our findings, it is clear that energy-conservation-based DSM is the ideal option for the study area. From all the algorithms tested, the salp swarm algorithm demonstrated the best performance for the current study.This work was supported by the Researchers Supporting Project number (RSPD2023R646), King Saud University, Riyadh, Saudi Arabia

Grass Hopper Optimization Algorithm for Off-Grid Rural Electrification of an Integrated Renewable Energy System

A suitable alternative to grid expansion has been found in renewable energy sources like wind, solar, and biomass. To put it another way, relying solely on one of the major renewable sources is both inefficient and expensive. As a result, an integrated renewable energy system is a viable option. The purpose of this article is to discuss the use of the Grasshopper Optimization Algorithm (GOA) for renewable energy sizing in the current study area. For an autonomous microgrid network, the proposed technique finds the optimum system size on the basis of Loss of Power Supply Probability (LPSP). The proposed microgrid consists of PV panels, wind turbines, biomass generator and a battery storage system. The proposed GOA algorithm’s convergence efficiency in resolving the current optimization problem is investigated and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in the MATLAB software environment. The simulation results show that the GOA algorithm outperforms its counterparts, GA and PSO, in terms of system sizing.</jats:p

Grass Hopper Optimization Algorithm for Off-Grid Rural Electrification of an Integrated Renewable Energy System

A suitable alternative to grid expansion has been found in renewable energy sources like wind, solar, and biomass. To put it another way, relying solely on one of the major renewable sources is both inefficient and expensive. As a result, an integrated renewable energy system is a viable option. The purpose of this article is to discuss the use of the Grasshopper Optimization Algorithm (GOA) for renewable energy sizing in the current study area. For an autonomous microgrid network, the proposed technique finds the optimum system size on the basis of Loss of Power Supply Probability (LPSP). The proposed microgrid consists of PV panels, wind turbines, biomass generator and a battery storage system. The proposed GOA algorithm’s convergence efficiency in resolving the current optimization problem is investigated and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in the MATLAB software environment. The simulation results show that the GOA algorithm outperforms its counterparts, GA and PSO, in terms of system sizing

Appropriate analysis on properties of various compositions on fluids with and without additives for liquid insulation in power system transformer applications

Abstract Transformer is a well-known power system apparatus utilized in conjunction with solid insulations such as paper and press board, as well as liquid insulations like mineral oil, a petroleum-based fluid. Despite the notable drawbacks associated with mineral oil, such as limited resources for future generations and its non-eco-friendly nature, its usage remains ubiquitous. There is a growing imperative to explore alternative fluids that surpass mineral oil in terms of environmental impact and performance. Amidst the global shift towards green energy, this study focuses on vegetable seed oils such as corn oil, soybean oil, mustard oil, and rice bran oil as potential substitutes. The research evaluates these oils based on key transformer properties including breakdown voltage, water content, interfacial tension, viscosity, acidity, flash point, and fire point. Interestingly, rice bran oil and soybean oil exhibit promising characteristics that suggest they could effectively replace petroleum-based fluids in transformers. Furthermore, the study extends to blending mineral oil with vegetable seed oils in various compositions, incorporating natural and synthetic antioxidant additives ranging from 0 to 1%. Comparative analyses between samples with and without additives reveal that the inclusion of 1% propyl gallate yields outstanding performance improvements. For instance, a blend comprising 25 ml of mineral oil and 25 ml of soybean oil, supplemented with 1% propyl gallate, demonstrates 90% higher effectiveness compared to other blends and additives tested. Moreover, the research employs statistical regression analysis to establish relationships between different parameter variables, providing deeper insights into the performance and compatibility of these blended oils in transformer applications. This comprehensive investigation underscores the potential of vegetable seed oils as viable alternatives to mineral oil, contributing to the advancement of eco-friendly solutions in power systems