Nowadays, hybrid renewable systems can be the best solution for meeting electricity demand, especially where grid extension and environmental issues are important. This study aimed to find the best combination of the typical components used in East Java, Indonesia. In this regard, four types of photovoltaic (PV) panels, four types of wind turbines, and two types of batteries were selected, and the HOMER software simulated all possible combinations of the systems, including 32 scenarios (Sen). Then, considering the most important 15 parameters, such as pollutant emissions and economic values, the results were analyzed and sorted by the multicriteria decision-making (MCDM) method to find the best scenario for the case-study region. The results showed that SunPower E20-327 as PV, Eocycle EO10 10 kW as wind turbine, and Generic 1 kWh Li-Ion as the battery could be the best selection to design a hybrid renewable system for the case-study region since it can fulfill both economic and environmental needs. The cost of energy (COE) of the best-designed system and net present cost (NPC) are 0.24 ($/kWh) and 1.64 million $, respectively, where the renewable fraction (RF) is 55.1% and the scaled annual average load is 1126 kWh/day. The results of the sensitivity analysis on the best scenario’s parameters (where the capital cost of PV, battery, and wind turbine changes from 0.6 to 1.2, from 0.7 to 1.2, and from 0.7 to 1.4 of the current price, and diesel price from 0.5 to 1.1 ($/L)) showed that the RF, COE, and NPC values ranged between 51% to 93%, 0.2 to 0.3 ($/kWh), and 1.4 to 2.1 (million $), respectively.

Renewable energies are the best solutions to reduce CO₂ emissions and supply reliable electricity. This study aims to find the best combination of various components considering economic, environmental, and technical factors together. The most important consideration factors are the limitation of using PV panels due to the land constraints and applying CO₂ penalties where diesel generators and the grid are generating electricity. Findings show that providing electricity by hybrid systems would be useful even in the well-provided electricity regions by the grid with the least blackouts. The best combination of the proposed components, including PV, bio generator, diesel generator, batteries, and grid for the case study region where the load demand is 890 kWh/day and peak load is 167.2 kW, would be an off-grid hybrid system including PV, bio generator, diesel generator, and battery. The optimization results show an NPC (present value of the costs of investment and operation of a system over its lifetime) of $1.02 million and a COE (the average cost per capital of useful electricity produced by the system) of 0.188 $/kWh. Finally, due to the showing of the effect of different conditions on the optimization results and making the study usable for other circumstances of the case study region, some sensitivity analyses have been carried out.

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households.

A solar photovoltaic (PV) array is part of a PV power plant as a generation unit. PV array that are usually placed on top of buildings or the ground will be very susceptible to dirt and dust. Thus, this dirt and dust will be able to reduce the performance and work efficiency of the generation unit. Cleaning PV arrays by manpower requires high effort, cost, and risk, especially in higher location. This study presents the design of a mobile robot that is used to replace human labor to clean PV arrays. That way, the PV array maintenance steps can reduce operational costs and risks. This intelligent controlled mobile robot can maneuver safely and efficiently over PV arrays. gyroscope and proximity sensors are used to detect and follow the sweep path over the entire PV array area. Proportional integral derivative (PID) control test makes the robot can stabilize in about 5.72 seconds to keep on the track. The smart PV cleaning robot has average operation time about 13 minutes in autonomous mode and 20-24 minutes in manual mode. The operation of the robot is effective to give more efficiency on the use of energy, time, and maintenance costs of PV array system.

The demand for renewable sources-based micro-grid systems is increasing all over the world to address the United Nation’s (UN) sustainable development goal 7 (SDG7) “affordable and clean energy”. However, without proper viability analysis, these micro-grid systems might lead to economic losses to both customers and investors. Therefore, this paper aims to explore the feasibility and sustainability of a hybrid micro-grid system based on available renewable resources in remote hill tracts region of Bangladesh. Nine different scenarios are analyzed here, and a combination of solar, hydro, biogas, and diesel generator systems are found to be the best feasible solution in regard to the least cost of electricity and emission. The optimized result shows that with a renewable fraction of 0.995, the unit levelized cost of energy of the micro-grid system is $0.182 and it emits 54 and 117 times less CO2 compared to grid-based and diesel-based systems. Further, the fuel share of the system being 0.5% and greenhouse gas per energy being 0.06425 kg/KWh, validate the system as highly sustainable and eco-friendly. With the ability to fulfill load demands without interrupting supply, and reducing the emissions of greenhouse gases, the designed microgrid can provide sustainable energy solutions to any hill-tracts of Bangladesh.