Levelized-Cost-of-Electricity-Driven Design Optimization for Medium-Voltage Transformerless Photovoltaic Converters

Seo, Gab-Su; Mukherjee, Satyaki; Roy, Jinia; Goodrick, Kyle J.; Mallik, Rahul; Majmunović, Branko; Dutta, Soham; Maksimović, Dragan; Johnson, Brian

doi:10.1109/ecce.2019.8913063

Cited by 6 publications

(18 citation statements)

References 12 publications

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“…In [3], three key design parameters: number of C 2 modules, on-resistance for dual-active bridge (DAB) primary-side switches, and core volume for both transformer and inductor are optimized. To reduce the computation burden in the bruteforce method, other design parameters are deterministically computed from the three free variables.…”

Section: Comprehensive Optimization Design Variablesmentioning

confidence: 99%

“…As cost is a significant factor when installing new PV generation, reducing the LCOE is a high priority when designing PV systems. Recent work has shown that an LCOE improvement model can be used to evaluate the relative improvement in LCOE for a given topology [3]:…”

Section: Introductionmentioning

confidence: 99%

“…One topology of particular interest, the C 2 block architecture shown in Fig. 1, is described in [4] and the LCOE improvement offered by this topology is explored in [3]. In order to create a tractable design problem, [3] simplifies the design space by neglecting, linking, or constraining certain design variables based on engineering intuition of the architecture.…”

Section: Introductionmentioning

confidence: 99%

“…1, is described in [4] and the LCOE improvement offered by this topology is explored in [3]. In order to create a tractable design problem, [3] simplifies the design space by neglecting, linking, or constraining certain design variables based on engineering intuition of the architecture. In this paper, many of these simplifications are removed and the modeling is enhanced to more accurately determine the optimal system.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, many of these simplifications are removed and the modeling is enhanced to more accurately determine the optimal system. As [3] uses a brute-force parameter sweep to find the optimal parameters, the computation time increases exponentially as new variables are added and as the parameter sweep becomes finer. Therefore, a different solving strategy must be employed if a finer sweep or more variables are desired.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters

Goodrick

Seo

Mukherjee

et al. 2020

2020 IEEE Energy Conversion Congress and Exposition (ECCE)

Self Cite

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Section: Comprehensive Optimization Design Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters

Goodrick

Seo

Mukherjee

et al. 2020

2020 IEEE Energy Conversion Congress and Exposition (ECCE)

Self Cite

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Using cluster analysis and genetic algorithm for multi-objective optimisation of hybrid electricity supply systems: the case of a photovoltaic/wind/ battery grid-connected system in Yazd, Iran

Khatibi

Astaraei

Jahangir

2022

International Journal of Ambient Energy

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Levelized Cost of Energy-Oriented Modular String Inverter Design Optimization for PV Generation System Using Geometric Programming

et al. 2022

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Levelized cost of energy (LCOE) is a commonly used metric to assess the cost-to-benefit ratio over the lifetime of an energy resource such as photovoltaics (PV). However, power electronics engineers tend to rely on metrics such as efficiency and power density which do not guarantee lifetime cost optimality. Recent work has shown that an LCOE-focused optimization approach can yield improved system designs, leading to improved lifetime performance with balanced lifetime cost and energy generation. This paper outlines an LCOE optimization framework for PV power electronics that uses geometric programming. The large number of circuit parameters and nonlinear nature of the system equations pose significant barriers. Our approach allows for decoupling of design variables which in turn enables superior computational efficiency and a near-optimal solution. By incorporating the power electronics design process and magnetic loss mechanism into the convex design framework, the optimization engine yields practically implementable parameters for a PV converter that minimizes LCOE. An optimization example for a cascaded modular PV inverter architecture is presented that suggests 3.35% LCOE improvement can be achieved by the new power electronics and the advanced optimization. The proposed optimization framework can be applied to other power generation systems to evaluate the effect of the power electronics design on system lifetime costs and efficiency.INDEX TERMS Convex optimization, geometric programming, levelized cost of energy, lifetime energy cost, magnetic loss modeling, solar inverters, modular multilevel converter.

show abstract

Levelized-Cost-of-Electricity-Driven Design Optimization for Medium-Voltage Transformerless Photovoltaic Converters

Cited by 6 publications

References 12 publications

LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters

LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters

Using cluster analysis and genetic algorithm for multi-objective optimisation of hybrid electricity supply systems: the case of a photovoltaic/wind/ battery grid-connected system in Yazd, Iran

Levelized Cost of Energy-Oriented Modular String Inverter Design Optimization for PV Generation System Using Geometric Programming

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