Urban Electricity Energy Planning by using Stochastic Programming Approach (Case Study Metropolitan Cities Tehran and Isfahan)

Document Type : Research Paper

Authors

1 Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

2 Master, Department of Urban Economics, Isfahan University of Arts

3 Professor, Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

4 Department of Economics and Entrepreneurship, Art Universiy of Isfahan, Esfahan, Iran

Abstract

This research provides a copula-based stochastic programming that is able to determine the optimal amounts of primary energy resources and different technologies to supply the required electrical energy. In this model, the uncertainty caused by random variables is presented in different scenarios, and uncertain interactions between random variables are shown using the copula functions with the different probability distributions and previously unknown correlations. Then, based on the developed approach of Copula-based stochastic programming, urban energy system planning for Tehran and Isfahan is formulated. The results obtained from the solution of the model indicate that the current trend is not consistent with the use of technologies with the optimization results, and it shows that in each case, solar technology compared with combined cycle technology, gas Turbine and steam in providing a part of the demand for electricity in terms of Economic and environmental benefits, and should be prioritized in investment policies. In order to compensate for the supply shortage, the remaining electrical energy should also be supplied by the grid, which will reduce the amount of pollution, compared with the situation at the same cost. In addition, the results show that the uncertainty in the components of the system has significant effects on the output of decision variables and system cost.
JEL Classification: C02, L11, Q40, R00

Keywords

References

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History

  • Receive Date: 11 December 2020
  • Revise Date: 23 June 2021
  • Accept Date: 26 June 2021

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