Abstract

Renewable energy-based distributed generators are widely embedded into distribution systems for several economical, technical, and environmental tasks. The main concern related to the renewable-based distributed generators, especially photovoltaic and wind turbine generators, is the continuous variations in their output powers due to variations in solar irradiance and wind speed, which leads to uncertainties in the power system. Therefore, the uncertainties of these resources should be considered for feasible planning. The main innovation of this paper is that it proposes an efficient stochastic framework for the optimal planning of distribution systems with optimal inclusion of renewable-based distributed generators, considering the uncertainties of load demands and the output powers of the distributed generators. The proposed stochastic framework depends upon the scenario-based method for modeling the uncertainties in distribution systems. In this framework, a multi-objective function is considered for optimal planning, including minimization of the expected total power loss, the total system voltage deviation, the total cost, and the total emissions, in addition to enhancing the expected total voltage stability. A novel efficient technique known as the Equilibrium Optimizer (EO) is actualized to appoint the ratings and locations of renewable-based distributed generators. The effectiveness of the proposed strategy is applied on an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal. The simulations verify the feasibility of the framework for optimal power planning. Additionally, the results show that the optimal integration of the photovoltaic and wind turbine generators using the proposed method leads to a reduction in the expected power losses, voltage deviations, cost, and emission rate and enhances the voltage stability by 60.95%, 37.09%, 2.91%, 70.66%, and 48.73%, respectively, in the 69-bus system, while in the 94-bus system these values are enhanced to be 48.38%, 39.73%, 57.06%, 76.42%, and 11.99%, respectively.

Highlights

  • The Equilibrium Optimizer (EO) is utilized for deciding the best allocation of the solar and wind units for minimization of the expected power losses, the system voltage deviation, the total cost, and the total emissions as well as enhancing the expected voltage stability considering the uncertainties of load demands and solar and wind power generators in an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal

  • The optimal power planning problem has been solved by the suggested algorithm (EO), and the optimal ratings and placement of wind turbines and solar PV units are assigned under the uncertainties of renewable energy and load demand

  • The proposed framework is based on application of the Equilibrium Optimizer (EO) and the scenario-based method for reducing the expected power loss, the expected system voltage deviations, the expected total cost, the expected total emissions, and maximizing the expected voltage stability

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Summary

Problem Statement

Uncertainty is essential in the optimal power planning problem of electrical systems, and it is a main consideration adding to its complexity, the uncertainties of the renewable energy resources (RERs) and load demands. Sustainability 2021, 13, 3566 on the planning of renewable DGs, and uncertainties effect the load demand and the output powers of solar and wind-based DGs in the distribution systems (DSs). The inclusion of the RERs in the distribution grids face numerous issues due to their intermittency and the fluctuations of the output power, which increases the uncertainties in electrical systems. In this way, the uncertainties in power systems should be taken into consideration for correct planning and the secure operation of power systems

Literature Survey
Contribution of Paper
Paper Layout
Problem Formulation
The Multi-Objective Function
Uncertainty Modeling
Modeling of Wind Speed
Modeling of Solar Irradiance
The Combined Load-Generation Model
Equilibrium Optimizer
Results and Discussion
The IEEE 69-Bus System
The IEEE 94-Bus System
Conclusions

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