Open AccessOptimization of the Midterm Electricity Generation Mix Considering the Effects of Water, Land and Carbon Footprints
Author(s) -
Mohsen Bozorg,
Hamed Mazandarani Zadeh,
Dragan Savić
Publication year - 2018
Publication title -
epic series in engineering
Language(s) - English
Resource type - Conference proceedings
ISSN - 2516-2330
DOI - 10.29007/vpvk
Subject(s) - electricity generation , photovoltaic system , electricity , environmental science , electric power , hydroelectricity , environmental economics , renewable energy , arid , carbon footprint , water resources , water scarcity , computer science , natural resource economics , power (physics) , engineering , greenhouse gas , economics , electrical engineering , paleontology , ecology , physics , quantum mechanics , biology
Electric energy plays a key role in the development of modern societies. Each of the electric power generation technologies (e.g., hydroelectric, wind, solar, thermal, etc.) has some advantages and disadvantages with respect to the fundamental resource indicators, including water footprint, land footprint, carbon footprint, as well as electricity generation costs. Due to the shortage and frequent crisis associated with the above resources, optimal selection of the mix of electricity generation technologies is very important and the share of each technology in the capacity expansion of the generation system must be carefully defined. Iran is in an arid and semi-arid region, with less than one third of the average world precipitation. Moreover, the available water resources are restricted due to the water crises in the Middle-East region. In this paper, we first estimated the peak power consumption of Iran in 2024, based on the time-series data from 2004 to 2014. Then, we formulated an optimization problem to find the share of each electric power generation technology to cover the required extra generation capacity for supplying the power consumption in the target year 2024, considering the effect of the four aforementioned performance indicators. The optimization problem is solved using Genetic Algorithm. Numerical results show that in the target year, 20 GW of electricity should be added to the generation capacity. The results also show that, solar thermal and solar photovoltaic are the best electric generation technologies regarding the available resources.