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Multi-objective optimization of a grid-connected PV system for a public building: Faculty of Sciences and Technology at Pau

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Abstract

In this paper, an optimal sizing of a grid-connected PV system to accommodate the load demands of a public building (i.e., Faculty of Sciences and Technology located in Pau—France) and its occupants' electric vehicles is presented. Both single-objective and multi-objective optimizations are performed to optimally size the system for a period of 20 years, while mitigating the carbon footprint, reducing the imported power from the grid and decreasing the cost of energy. The optimization problem was programmed and solved using MATLAB software. It was found that the optimal configuration fulfills attractive values of total net present cost of 1.11 × 106 $ and levelized cost of energy of 0.056 $/kWh. This optimum consists of 1093 kW installed PV power, with an annual energy generated of 1.42 × 106 kWh/year, which avoids emissions of 20 tonnes CO2eq/year. Therefore, the present study might help to achieve the decarbonization process of a France’s energy sector through the development of local green energy production platforms and the increase of the building’s self-sufficiency.

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Data availability

Enquiries about data availability should be directed to the authors.

Abbreviations

\(c_{1}\) :

Personal cognitive learning coefficients

\(c_{2}\) :

Global social learning coefficients

\(C_{{{\text{A}}\_{\text{cap}}}}\) :

Annual capital cost ($)

\(C_{{{\text{A}}\_{\text{O}}\& {\text{M}}}}\) :

Operation and maintenance cost ($)

\(C_{{{\text{A}}\_{\text{pr}}\_{\text{grid}}}}\) :

Cost of energy purchased from grid ($)

\(C_{{{\text{A}}\_{\text{REP}}}}\) :

Replacement cost ($)

\(C_{{{\text{A}}\_{\text{s}}\_{\text{grid}}}}\) :

Cost of energy sold to grid ($)

\(C_{{{\text{bat}}}}\) :

The capacity of the EV battery

\(C_{{{\text{inv}}}}\) :

Initial capital cost of inverter ($/kW)

\({\text{EM}}_{{{\text{e}}_{{{\text{grid}}}} }}\) :

CO2 emissions produced from grid (kgCO2eq/kWh)

\({\text{EM}}_{{{\text{e}}_{{{\text{PV}}}} }}\) :

CO2 emissions produced from PV (kgCO2eq/kWh)

\({\text{EM}}_{{{\text{av}}}}\) :

CO2 emissions avoided (kgCO2eq/kWh)

\(C_{{{\text{PV}}}}\) :

Initial capital cost of PV ($/kW)

\({\text{EF}}_{{{\text{pv}}}}\) :

Emission factor from PV

\({\text{EF}}_{{{\text{grid}}}}\) :

Emission factor from grid

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

Generated electrical energy (kWh)

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

Energy net related to grid (kWh)

\(E_{{{\text{grid}}\_{\text{p}}}}\) :

Energy purchased from grid (kWh)

\(E_{{{\text{grid}}\_{\text{s}}}}\) :

Energy sold to grid (kWh)

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

Electrical energy demand (kWh)

\(E_{{{\text{T}}\_{\text{grid}}\_{\text{S}}}}\) :

Total energy sold to grid

\(E_{{{\text{T}}\_{\text{grid}}\_{\text{p}}}}\) :

Total energy purchased from grid

G :

Solar radiation (W/m2)

\(G_{{{\text{best}}}}\) :

Best global position

\(k_{{\text{t}}}\) :

PV temperature coefficient (1/°C)

\(N_{{{\text{PV}}}}\) :

Numbers of PV arrays

\(P_{{{\text{best}}}}\) :

Best individual particle position

\(P_{{{\text{ch}}}}\) :

Charging power

\(P_{{{\text{pv}},{\text{out}}}}\) :

Output power of a PV system (kW)

\({\text{PPR}}\) :

Purchase price of energy ($/kWh)

\(P_{{{\text{rated}}}}\) :

Rated power of PV (kW)

\(r_{1} ,r_{2}\) :

Random numbers (0,1)

\({\text{SOC}}_{{\text{E}}}\) :

Expected state of charge

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

Initial state of charge

SPR:

Selling price of energy ($/kWh)

\(T_{{{\text{amb}}}}\) :

Ambient temperature (°C)

\(T_{{{\text{EV}}\_{\text{Ch}}}}\) :

Charging duration

\(T_{{{\text{ref}}}}\) :

Reference temperature (°C)

\(v_{i}^{k}\) :

Particle velocity

\(x_{i}^{k}\) :

Position vector

amb,0:

Ambient temperature

h :

Index of hour of years (h = 1, …, 8760)

k :

Time iteration

t:

Total

\(\eta_{{{\text{pv}}}}\) :

PV arrays efficiency (%)

\(\eta_{{{\text{cc}}}}\) :

Efficiency of the charge controller

\(\eta_{{{\text{conv}}}}\) :

Efficiency of the bidirectional converter

\(\eta_{{{\text{CE}}}}\) :

Charging efficiency

\(\omega_{0}\) :

Inertia weight

\(\lambda\) :

Inflation rate

ABC:

Artificial bee colony

CO2 :

Carbon dioxide

d :

Discount rate

DOD:

Depth of discharge

EV:

Electric vehicle

FS:

Faculty of Sciences

GOA:

Grasshopper optimization algorithm

GWO:

Gray wolf optimizer

HRES:

Hybrid renewable energy systems

LCOE:

Levelized cost of energy

LPSP:

Loss of power supply probability

MILP:

Mixed-integer linear programming

MPSO:

Multi-objective particle swarm optimization

N :

Lifespan

NPC:

Net present cost

NRU:

Non-renewable usage

PSO:

Particle swarm optimization

RU:

Renewable usage

TNPC:

Total net present cost

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Acknowledgements

This research was carried under the framework of E2S UPPA supported by the “Investissements d’Avenir” French program managed by ANR (ANR-16-IDEX-0002).

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The authors have not disclosed any funding.

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Authors and Affiliations

  1. E2S UPPA, SIAME, Université de Pau et des Pays de l’Adour, 64000, Pau, France

    M. A. Ben Taher, B. E. Lebrouhi, S. Mohammad, E. Schall & T. Kousksou

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  1. M. A. Ben Taher
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  2. B. E. Lebrouhi
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  3. S. Mohammad
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  4. E. Schall
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  5. T. Kousksou
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Correspondence to M. A. Ben Taher.

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Ben Taher, M.A., Lebrouhi, B.E., Mohammad, S. et al. Multi-objective optimization of a grid-connected PV system for a public building: Faculty of Sciences and Technology at Pau. Clean Techn Environ Policy 24, 2837–2849 (2022). https://doi.org/10.1007/s10098-022-02364-4

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  • DOI: https://doi.org/10.1007/s10098-022-02364-4

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