Use of an optimization model for optimization of Turkey’s energy management by inclusion of renewable energy sources

Abstract

In this study, Turkey’s power generation plan between 2018 and 2035 is obtained by using a new mathematical model that aims to make a generation expansion planning to have an acceptable level of a pollutant in the air considering the UN Framework Convention on Climate Change and Kyoto Protocol’s responsibilities of Turkey. The used method is a new fuzzy multi-objective linear programming (MOLP) method by considering the energy objectives of Turkey’s Ministry of Energy (MoE:MENR) and private sector’s energy targets. The multi-objective model aims are (1) energy generation cost minimization of energy production considering all energy-related costs in Turkey, (2) greenhouse gases emission’s reduction, (3) minimizing the imported energy, (4) maximizing efficiency of power plants and (5) minimizing the use of fossil fuels in power plants. By solving this mathematical model, Turkey’s 18-year power generation plan between the years 2018 and 2035 based on mainly renewable sources is formulated, and by 2035 the percentage of renewable energy sources in Turkey’s power generation is increased by 77% which includes 24.7% solar energy, 19.1% wind energy, 18.5% hydro energy, 12.3% biomass under high-demand scenario.

Appendix: Used notations

AC_g:

gth power plant’s CO₂ emissions

A_ik:

fuzzy set characterized by membership function A_ik(x_k)

A:

the highest weight

AF:

Discount factor = 10%

AS_g:

gth power plant’s SO₂ emissions

AN_g:

gth power plant’s NO_x emissions

CAM_g:

gth power plant’s CO₂ reduction amount per year

Ci:

fuzzy set characterized by membership function Ci(y)

cw_xy:

Weighted value that is computed (decision maker (yth) and the objective function (xth))

hdf :

Renewable energy target = 30%

EG:

Revenue (from exported energy)

Elek_g:

gth power plant’s electricity production cost

g :

Power plant

GCoO_g:

gth power plant’s revenue due to CO2 reduction

GS:

Power plant’s set

GSBM_g:

gth power plant’s maintenance cost

GS_f:

Power plant’s set (fossil fuel type)

GSFM_g:

gth power plant’s fuel cost

GSK_go:

gth power plant’s existing number

GSiM_g:

gth power plant’s operating cost

GSRM_g:

gth power plant’s rehabilitation cost

GSS_g:

gth power plant’s construction period

GSS_gz:

gth power plant’s used number in the year z

GS_v:

Power plant’s set (constructed)

GS_y:

Power plant’s set (renewable typed)

GSYM_g:

gth power plant’s construction cost

MEz:

Amount of imported energy in the year z

N:

total number of DMs

P :

Objective functions used in the study

PS:

Number of rules in the study

R :

Decision makers

SG:

Cost (from imported energy)

VE_g:

gth power plant’s efficiency ratio

Wi:

weighting given to each function by DM (ranging from 1 to 5)

WOF:

Weights of Objective Function

XE_z:

Amount of exported energy in the year z

xk:

input variables (where k = 1, 2, 3,…, K)

y:

output variable

YGHKO_g:

gth power plant’s transmission line energy losses

YGHTM_g:

gth power plant’s substation cost

YGHU_g:

gth power plant’s transmission length

YGHYM_g:

gth power plant’s transmission line construction cost

YGSS_gz:

gth new power plant’s construction number in the year z

Y _gz :

gth power plant’s energy supply in the year z

z :

Year

ZI_xy:

The chosen value by decision maker (yth) about the objective function (xth)