Abstract

This paper presents a study on the technical, economic, and environmental aspects of renewable energy resources-based distributed generation units (DGs). These units are connected to the medium-voltage network to create a new structure called a microgrid (MG). Renewable energies, especially wind and solar, are the most important generation units among DGs. The stochastic behavior of renewable resources increases the need to find the optimum operation of the MG. The optimal operation of a typical MG aims to simultaneously minimize the operational costs and the accompanied emission pollutants over a daily scheduling horizon. Several renewable DGs are investigated in the MG, consisting of biomass generators (BGs), wind turbines (WTs), and photovoltaics (PV). For the proposed operating strategy of the MG, a recent equilibrium optimization (EO) technique is developed and is inspired by the mass balance models for a control volume that are used to estimate their dynamic and equilibrium states. The uncertainties of wind speed and solar irradiation are considered via the Weibull and Beta-probability density functions (PDF) with different states of mean and standard deviation for each hour, respectively. Based on the developed EO, the hourly output powers of the PV, WT, and BGs are optimized, as are the associated power factors of the BGs. The proposed MG operating strategy based on the developed EO is tested on the IEEE 33-bus system and the practical large-scale 141-bus system of AES-Venezuela in the metropolitan area of Caracas. The simulation results demonstrate the significant benefits of the optimal operation of a typical MG using the developed EO by minimizing the operational costs and emissions while preserving the penetration level of the DGs by 60%. Additionally, the voltage profile of the MG operation for each hour is highly enhanced where the minimum voltage at each hour is corrected within the permissible limit of [0.95–1.05] Pu. Moreover, the active power losses per hour are greatly reduced.

Highlights

  • Due to the continuous increase in power demand and rapid depletion of fossil fuels, researchers all over the world have no other option but to look for alternative energy sources by utilizing small-scale distributed power generation (DG) and energy storage systems (ESS) [1]

  • The price where Nw is the number of wind turbines (WTs) units; Npv is the number of PV units; NBG is the number of biomass generators (BGs) units; Cpw is the operational costs of WT ($); Cpv is the operational costs of PV units

  • The equilibrium optimization (EO) is applied to determine the optimal operation of MG to achieve technical and economic benefits with respecting the associated operational and emission costs for two distribution systems

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Summary

Introduction

Due to the continuous increase in power demand and rapid depletion of fossil fuels, researchers all over the world have no other option but to look for alternative energy sources by utilizing small-scale distributed power generation (DG) and energy storage systems (ESS) [1]. In [9–12], the operation of distribution systems was optimally controlled via DERs commitment, Capacitor Banks (CBs) switching, SVC, and reconfiguration using the jellyfish search algorithm and manta ray foraging optimization algorithm, respectively In both studies, the wasted energy of power losses was minimized considering the daily load variations, but the uncertainties of the DERs were not taken into account. OPTimizer (SNOPT) solver was utilized using Generalized Algebraic Modeling Systems (GAMS) software In both studies [24,25], the outputs of WTs and PVs were directly evaluated from the hourly wind velocity and solar irradiance, respectively. In [37], EO was used to deal with the energy management optimization (EMO) in the MG considering the variations of WTs, PVs, and load demand for cost minimization and voltage magnitude improvements.

Problem Formulation
Types of DGs
Photovoltaic DGs
Wind Turbine DGs
Biomass DGs
Evaluation of of WT
Objective
Operational Costs
Constraints
Equilibrium Optimization for Optimal Operation Strategy in MGs
Simulation Results
Test Systems
Second
Cases Studied
Case 1
Case 2
Case 3
12.Results
15. Active power losses at each hourhour for different cases cases for the first
Simulation Results of a large-Scale 141-Bus Test System
Simulation Results of a Large-Scale 141-Bus Test System
18.Results
19. Hourly
20. Results
It is declined from
23. The losseslosses at each greatly reduced from case to cases
Despite
Conclusions
Methods
Full Text
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