Optimal Design of Hybrid Renewable Systems, Including Grid, PV, Bio Generator, Diesel Generator, and Battery

Abstract

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.

Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer

“…The output energy of PV panels at the time (t) ( E PV ( t )) in [kW], can be estimated using( 1 ) and ( 2 ) [ 37 ]. Where N PV is the number of PV panels, R PV is the PV-rated capacity[kW], f PV is the PV panels derating factor [%], G ( t ) is the solar radiation [W/m2], G ref is the standard episode solar radiation [1000 W/m2], ∝ P is the coefficient of temperature [%/℃], T c ( t ) is the cell temperature [℃], T ref is the standard cell temperature at test conditions [25 ◦C], T amb is the ambient temperature, NOCT is the nominal operating temperature of a PV panel, and μ is the generative disturbance coefficient.…”

Expanding Dengue Vectors Through the Border of Dhaka City: Vector Breeding Grounds in Gazipur City Corporation, Bangladesh

“…The power generation of PV modules is influenced by factors besides solar radiation, such as ambient temperature and irradiation conditions, and these characteristics vary from module to module. The output power of a PV module at any one time is determined by the following equation [22,39] 𝑃 𝑃𝑉 (𝑡, 𝛽) = 𝑁 𝑠 . 𝑁 𝑝 .…”

“…Popular optimization methods like Particle Swarm Optimization (PSO) [12], Aquila Optimizer (AO) [13], Pelican Optimization Algorithm (POA) [14], Dandelion Optimization Algorithm (DOA) [15,16], Gazelle Optimization Algorithm(GOA) [17][18][19], and Zebra Optimization Algorithm (ZOA) [20,21] frequently mimic biological behavior. An optimization case study of an off-grid hybrid system was done in Indonesia with PV panels, a bio generator, a diesel generator, batteries, and the grid optimally addressed energy needs [22]. Research demonstrated the technological advantages of a hybrid PV-BESS for renewable energy utilization and investigated the feasibility of a Building Integrated PV (BIPV) system with and without a battery [23].…”

A Comprehensive Study on the Performance of Various Tracker Systems in Hybrid Renewable Energy Systems, Saudi Arabia

“…Renewable energies are one of the best choices to supply load demand in both gridconnected and remote areas to reduce CO 2 emission [1]. Emission of greenhouse gases such as CO 2 is highly dependent on the energy systems of the countries [2], where the residential sector's share in emission is remarkable [3].…”