Abstract

The use of renewable and sustainable energy sources (RSESs) has become urgent to counter the growing electricity demand and reduce carbon dioxide emissions. However, the current studies are still lacking to introduce a planning model that measures to what extent the networks can host RSESs in the planning phase. In this paper, a stochastic power system planning model is proposed to increase the hosting capacity (HC) of networks and satisfy future load demands. In this regard, the model is formulated to consider a larger number and size of generation and transmission expansion projects installed than the investment costs, without violating operating and reliability constraints. A load forecasting technique, built on an adaptive neural fuzzy system, was employed and incorporated with the planning model to predict the annual load growth. The problem was revealed as a non-linear large-scale optimization problem, and a hybrid of two meta-heuristic algorithms, namely, the weighted mean of vectors optimization technique and sine cosine algorithm, was investigated to solve it. A benchmark system and a realistic network were used to verify the proposed strategy. The results demonstrated the effectiveness of the proposed model to enhance the HC. Besides this, the results proved the efficiency of the hybrid optimizer for solving the problem.

Highlights

  • According to the authors of [1], it is expected that the electricity generation from renewables will increase to 23,477 Billion KWh by 2050 to reduce the reliance on fossil fuel-based generation units

  • Regarding the potential transmission projects, it was supposed that the maximum number of circuits is four to reinforce the network to host more renewable and sustainable energy sources (RSESs) and supply the demand centers in the period between the year 2020 and the year 2040

  • To increase the dependency on RSESs, it was suggested that buses 1, 5, 14, 17, and 18 were the candidate locations for new wind units

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Summary

Introduction

Renewable and sustainable energy sources (RSESs) have been widely used to cope with the increase in global energy consumption and environmental pollution. According to the authors of [1], it is expected that the electricity generation from renewables will increase to 23,477 Billion KWh by 2050 to reduce the reliance on fossil fuel-based generation units. Incorporating the RSESs into modern power systems minimizes the systems’ security and increases the hazards [2]. The output of RSESSs is unpredictable, and the enormous use of RSESs increases the power systems’ uncertainties. Unplanned integration of RSESs increases the power system losses, overloading of power system’s components, and reliability issues such as exceeding the limits of short circuit capacity [3]

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