Following a rise in population, load demand is increasing even in the remote areas and islands of Bangladesh. Being an island that is also far from the mainland of Bangladesh, St. Martin’s is in need of electricity. As it has ample renewable energy resources, a renewable energy-based microgrid system seems to be the ultimate solution, considering the ever-increasing price of diesel fuel. This study proposes a microgrid system and tests its technical and economic feasibility in that area. All possible configurations have been simulated to try and find the optimal system for the island, which would be eco-friendly and economical with and without considering renewable energy options. The existing power supply configuration has also been compared to the best system after analyzing and investigating all technical and economic feasibility. Sensitivity and risk analysis between different cases provide added value to this study. The results show that the current diesel-based system is not viable for the island’s people, but rather a heavy burden to them due to the high cost of per unit electricity and the net present cost. In contrast, a PV /Wind/Diesel/Battery hybrid microgrid appeared to be the most feasible system. The proposed system is found to be around 1.5 times and 28% inexpensive considering the net present cost and cost of energy, respectively, with a high (56%) share of renewable energy which reduces 23% carbon dioxide.

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW), $16,235 ($528,825 − $512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.

One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m², which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.

The rapid growth of renewable energies specifically wind and solar with their intermittent phenomena has recently become an exciting and important topic among researchers worldwide. The integration of such devices in distribution networks comprising of battery energy storage systems (BESSs) is considered as efficient and complementation approach which their well-managed configuration and scheduling via optimal arrangement yield not only productive and reliable power delivery to end-users, but cost-saving opportunity for utility managers. When dealing with photovoltaic (PV), reactive power capability of PV inverter is another aspect of interest that can contribute in enhancing effective penetration of wind, solar, and storage system into distribution networks. In this paper, a robust and novel optimization technique epsilon multiobjective genetic algorithm $\epsilon$ -MOGA is proposed and applied to a real 162-bus distribution network. A three-objective function optimization encompassing environmental, technical, and economic aspect is developed to size the centralized wind-farm and BESS parameters, distributed rooftop PV, and PV inverter reactive power control in an optimal manner. The proposed arrangement is simulated in MATLAB and compared with several cases to show its superior implementation concerning carbon emission reduction, better technical aspect, annual saving, and grid power alleviation.