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Article
Publication date: 30 September 2020

Ali Mostafaeipour, Mojtaba Qolipour, Mostafa Rezaei, Mehdi Jahangiri, Alireza Goli and Ahmad Sedaghat

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from…

Abstract

Purpose

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives.

Design/methodology/approach

In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software.

Findings

Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified.

Originality/value

As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.

Details

Journal of Engineering, Design and Technology , vol. 19 no. 3
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 4 September 2019

Ali Mostafaeipour, Mojtaba Qolipour, Mostafa Rezaei and Hossein Goudarzi

This paper aims to investigate the techno-economic feasibility of wind power potential for a tribal region located in Gachsaran in the South-West of Iran.

Abstract

Purpose

This paper aims to investigate the techno-economic feasibility of wind power potential for a tribal region located in Gachsaran in the South-West of Iran.

Design/methodology/approach

Techno-economic feasibility study and analysis of data were conducted by HOMER v2.68 software. Simulations and calculations were performed for 10 kW turbines, 8 Trojan L16P batteries, 12 kW converter and 12 kW generator. To anticipate the pay back period (PBP) or the time required to reach profitability, an engineering economic method named net equivalent uniform annual was applied.

Findings

The power plant construction cost, including the initial cost, installing, replacing and project operating costs for useful life of 20 years was equal to $40970. The net income of the project for each year was $8538 and the calculations were carried out using interest rate of 18%. Results indicated that PBP was 13 years which is lower than 20 years useful life of the turbine. Therefore, it is economically feasible to use this type of turbine for the nominated region.

Originality/value

There has not been conducted a research regarding remote areas in Iran; therefore, this study aims at closing this research gap. Moreover, this method could be used for any remote areas in any other developing country.

Details

Journal of Engineering, Design and Technology , vol. 18 no. 2
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 5 June 2019

Samrad Jafarian-Namin, Alireza Goli, Mojtaba Qolipour, Ali Mostafaeipour and Amir-Mohammad Golmohammadi

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

Abstract

Purpose

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

Design/methodology/approach

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

Findings

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Originality/value

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

Details

International Journal of Energy Sector Management, vol. 13 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

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