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Optimized scheduling for electric lift trucks in a sugarcane agro-industry based on thermal, biomass and solar resources

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Abstract

Resource scheduling for both cost and pollution minimization in the power system is so crucial. To reduce the greenhouse gas emission, employing renewable energy resources, especially solar and wind energy, and beside them plug-in hybrid electric vehicles are effective solutions. In industrial factories, using biomass resources for power generation is both economic and environmental approach. In sugarcane company, bagasse is plant fiber residue which is used as fuel. Electric lift trucks, capable of being connected to power grid, could decrease the pollution in industrial transportations. In this paper, scheduling problem for a large-scale sugarcane factory including solar resources, a thermal unit, and electric lift trucks is presented and solved by CPLEX solver in GAMS software. In order to consider uncertainties, different scenarios are noticed. To contribute better understanding of optimization problem, cost, pollution, and charging regime of electric lift trucks are carefully analyzed. The results show that implementation of the biomass electric power generation is effective for reducing cost and amount of emission.

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Notes

  1. Green House Gas.

Abbreviations

\(\psi_{\hbox{min} } /\psi_{\hbox{max} }\) :

Min/max. State of charge

\(I_{i} \left( t \right)\) :

Status of unit i at hour t

\(I_{\text{ch}} \left( t \right)\) :

Charging status of lift trucks at time t (if \(I_{\text{ch}} \left( t \right) = 1\), then lift trucks are in charge mode)

I dch(t):

Discharging status of lift trucks at time t (if it equals to one, lift trucks are in discharge mode)

\(N_{\text{LF}} \left( t \right)\) :

Number of electric lift trucks connected to the grid at time t

n :

Number of all electric vehicles in investigated place

α i , β i , γ i :

Emission coefficients of unit i

a i , b i , c i :

Cost coefficients of unit i

I :

If it equals to one, the unit is thermal power plant and if it equals to two, the unit is biomass resource

H :

Scheduling hours

S :

Sets of scenario

η :

Efficiency of batteries

w c :

Weight factor for cost

w e :

Weight factor for emission

\({\text{fc}}_{i} ()\) :

Fuel cost function of unit i

\({\text{Sc}}_{i} ()\) :

Start-up cost function of unit i

\({\text{fe}}_{i} ()\) :

Emission function of unit i

D(t):

Load demand at time t

pfe i :

Emission penalty factor of unit i

P ssolar (t):

Power of solar farm at time t considering scenario s

P LF :

Capacity of the electric lift truck’s batteries

P sLF (t):

Power of the electric lift trucks at time t considering scenario s

R :

Efficiency of the battery

\(E^{S} \left( t \right)\) :

Total energy of all batteries at time t considering scenario s

\(E_{\text{F}}\) :

Energy of battery at final hour of day

\(E_{0}\) :

Primary energy of battery at starting time for scheduling

\(E_{\hbox{min} } ,E_{\hbox{max} }\) :

Maximum and minimum energy of battery

P sch (t):

Charging power of all plug-in lift trucks at time t considering scenario s

\(P_{\text{dch}}^{s} \left( t \right)\) :

Discharging power of all plug-in lift trucks at time t considering scenario s

\(i1_{\text{ch}} \left( t \right), i1_{\text{dch}} \left( t \right)\) :

Status of charging and discharging plug-in lift trucks

Randn:

A random number between 0 and 1 in normal probability distributed curve

P s i (t):

Power of unit i at time t considering scenario s

P max i , P min i :

Max/min output limit of unit i

p pv :

Output power of solar panel

p pv-r :

Rated output power of PV array

F pv :

De-rating factor considering shading, wiring, etc.

G :

Solar radiation in current time

G stc :

Solar radiation under the standard test condition

α T :

Temperature coefficient of power

T stc :

Temperature on PV cells under standard test condition

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Acknowledgements

The authors would like to thank Dr. Babak Mozafari, the dean of Electrical and computer faculty of science and research branch Islamic Azad University, for supporting this research.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Electrical and Computer, Science and Research Branch, Islamic Azad University, Tehran, Iran

    R. Pashangpour, F. Faghihi & S. Soleymani

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  1. R. Pashangpour
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  2. F. Faghihi
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  3. S. Soleymani
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Correspondence to F. Faghihi.

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Pashangpour, R., Faghihi, F. & Soleymani, S. Optimized scheduling for electric lift trucks in a sugarcane agro-industry based on thermal, biomass and solar resources. Int. J. Environ. Sci. Technol. 15, 2349–2358 (2018). https://doi.org/10.1007/s13762-017-1535-4

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  • DOI: https://doi.org/10.1007/s13762-017-1535-4

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