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Financial Optimization in the Renewable Energy Sector

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Mitigating Climate Change (TELAC 2021)

Part of the book series: Springer Proceedings in Energy ((SPE))

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Abstract

Due to the rapid population growth worldwide, the energy demand increases daily with industrialization, urbanization, and technological development. Electric energy is an indispensable product used in all areas of life for human beings. Although the primary energy source used for electricity generation is fossil fuels, more active use of renewable energy resources has become very important because they are exhausted and the damage they cause to the environment. Many countries encourage electricity suppliers to use renewable energy sources more actively. Since both fossil fuels and renewable energy sources are limited, these resources must be used effectively. Electricity is such a kind of product that must be consumed as soon as it is produced due to limited resources. Therefore, many of the countries are continuously working on regulating their electricity markets in order to improve the effectiveness and the efficiency in the usage of electricity. Electricity suppliers must optimize their generation capacity and bidding strategies in this unregulated market environment. In this empirical study, hydraulic and wind power plants were used to emphasize using renewable energy sources in electricity generation. The mean-variance, mean-absolute deviation optimization models, and Sharpe ratio optimization were applied to Turkish electricity market. Each model has been optimized for two different renewable power plants with four different objective functions (minimum risk portfolio, maximum return portfolio, maximum utility portfolio A = 3, and maximum Sharpe ratio portfolio). All optimization results obtained as a result of the application have been analyzed. The portfolio performances of the models and renewable energy sector were compared with the Sharpe ratio performance criterion. It has been observed that the performance of the optimal portfolios calculated for the hydraulic power plant among the renewable energy sector and the MAD optimal portfolios among the models provide better results.

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Abbreviations

DAM:

Day-ahead market

DS:

Down-side

EPİAŞ:

Energy Market Management Company in Turkey

GWh:

Gigawatt-hour

HPP:

Hydraulic power plant

MAD:

Mean-absolute deviation

MaxUP:

Maximum utility portfolio

MaxRP:

Maximum return portfolio

MCP:

Market clearing price

MinRP:

Minimum risk portfolio

MPT:

Modern portfolio theory

MSRP:

Maximum Sharpe ratio portfolio

MV:

Mean-variance

MVS:

Mean-variance-skewness

MWe:

Megawatt electric

OECD:

Organization for Economic Cooperation and Development

SR:

Sharpe ratio

SV:

Semi-variance

WPP:

Wind power plant

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Authors and Affiliations

  1. Faculty of Political Sciences, Department of Management, Ankara University, 06590, Ankara, Turkey

    Fazıl Gökgöz

  2. Türksat Company, Ankara, Turkey

    Ahmet Yıldırım Erdoğan

Authors
  1. Fazıl Gökgöz
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  2. Ahmet Yıldırım Erdoğan
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Correspondence to Fazıl Gökgöz .

Editor information

Editors and Affiliations

  1. Turbulence and Energy Lab, University of Windsor, Windsor, ON, Canada

    David S.-K. Ting

  2. Fluid Mechanics Research Laboratory, Tennessee Technological University, Cookeville, TN, USA

    Ahmad Vasel-Be-Hagh

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Gökgöz, F., Erdoğan, A.Y. (2022). Financial Optimization in the Renewable Energy Sector. In: Ting, D.SK., Vasel-Be-Hagh, A. (eds) Mitigating Climate Change. TELAC 2021. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-030-92148-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-92148-4_6

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  • Online ISBN: 978-3-030-92148-4

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