Abstract
The chapter aims to optimize the levelized cost of energy (LCOE) for a sample hybrid renewable energy system (HRES) consisting of power sources such as solar photovoltaic, wind and diesel generators. The variation of life cycle cost of the system reflected by the LCOE is computed for different generation capacity factors for a time period of 24 h. The interest rate is taken as 10%, the capacity recovery factor is assumed to be 0.1175 and the life span of the hybrid generating system is considered to be 20 years. The optimal LCOE computed using a traditional solver is compared with the particle swarm optimization technique.
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Abbreviations
- CRF:
-
Capacity recovery factor
- IC:
-
Initial capital cost (€/kW)
- AE:
-
Annual operating expenses (€/kW)
- AEP:
-
Annual energy production (kW)
- i:
-
Interest rate (%)
- n:
-
Operational life (years)
- AC:
-
The annualized costs (insurance, other expenses) (€/kW/year)
- O&M:
-
Operation and maintenance cost (€/kW/year)
- CF:
-
Net capacity factor
- 8760:
-
Hours per year
- \({\text{NS}}_{1} {\text{NS}}_{2} \ldots {\text{NS}}_{N}\) :
-
Number of solar power sources for different capacity
- \({\text{NW}}_{1} {\text{NW}}_{2} \ldots {\text{NW}}_{M}\) :
-
Number of wind power sources for different capacity
- \({\text{ND}}_{1} {\text{ND}}_{2} \ldots {\text{ND}}_{P}\) :
-
Number of DG power sources for different capacity
- \({\text{NS}}_{it}\) :
-
Number of solar units generating at hour ‘t’
- \({\text{CS}}_{i}\) :
-
Capacity factor of solar ith unit
- \({\text{NW}}_{jt}\) :
-
Number of wind power units generating at hour ‘t’
- \({\text{CW}}_{j}\) :
-
Capacity factor of wind power ith unit
- \({\text{ND}}_{kt}\) :
-
Number of DG units generating at hour ‘t’
- \({\text{CD}}_{k}\) :
-
Capacity factor of DG ith unit
- \({\text{PD}}(t)\) :
-
Demand at hour ‘t’
- \(PL(t)\) :
-
Losses at hour ‘t’
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Singh, P., Pandit, M., Srivastava, L. (2020). PSO-Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System. In: Pandit, M., Dubey, H., Bansal, J. (eds) Nature Inspired Optimization for Electrical Power System. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-4004-2_3
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