Abstract
Higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. However, during sudden changes in load, the thermal power plants support the grid, though at higher ramping rates and with inefficient operation. Hence, further renewable additions must be backed by battery energy storage systems to limit the ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, battery-integrated renewable energy systems that include floating solar, bifacial rooftop, and wind energy systems are evaluated for a designated smart city in India to reduce ramping support by a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability.
Highlights
IntroductionEnergy being one of the most imported commodities in developing nations, an increase in crude oil and coal prices witnessed widening fiscal deficits
ALA-QPSO techniques are applied, and the results are compared with four other benchmark techniques, four variants of differential evolution (DE) algorithms
The adopted algorithm is compared with variants of DE as it has proven to be an effective optimization technique for enhancing the exploration of the population
Summary
Energy being one of the most imported commodities in developing nations, an increase in crude oil and coal prices witnessed widening fiscal deficits. Renewable energy costs have drastically reduced from 2010 to 2019 owing to mass production. These favorable conditions allowed developing economies to invest in eco-friendly and costeffective renewable energy sources to comply with Sustainable Development Goals decided during the Paris Agreement. With its crude imports pegged at 82% of its energy needs, witnessed an 84% fall in renewable energy costs in this period, with tenders falling
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