Optimal Allocation of Inverter-Based WTGS Complying With Their DSTATCOM Functionality and PEV Requirements

Abstract

Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimizationbased approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.

Enhancing hosting capacity of intermittent wind turbine systems using bi‐level optimisation considering OLTC and electric vehicle charging stations

“…The hourly wind speed is commonly modelled by using Weibull pdf and can be given, for each hour (t), as follows [20,27]:…”

“…Normal pdf is commonly used to model the uncertainty of the demand load [20,30]. Standard deviation (σ l ) and mean ( l ) at time segment t are used to represent the pdf of the load demand.…”

“…An approach based on multi-objective has been suggested in [17] for the allocation of WTSs for lessening carbon emissions and operational costs. Various metaheuristic-based methods have been proposed for optimising the capacity of WTSs in distribution systems, such as: cuckoo search algorithm [18], particle swarm optimisation [19], grey wolf optimisation [20], and simulated annealing algorithm [21]. In [22], the utilisation of energy storage systems for increasing the HC of renewables has been investigated in distribution systems.…”

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

“…For example, Finland has the second-highest part of renewable energy in the EU, while contributing to the wind power of 9% concerning all generation in 2018 [4]. Wind energy conversion systems (WECS) are a pollution-free and effective source to be integrated into both distribution and transmission systems [5], [6]. An essential requirement with the increasing trend of WECS in the unified power systems is to abstract the maximum accessible power from the wind power considering the fluctuations of the wind speed and direction [7], [8].…”

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

“…Particle Swarm Optimization (PSO) algorithm has been developed for determining the optimal allocation of battery in RDS to decrease annual operation cost of the system [42]. The optimal sizing of inverter with WT and electric vehicle using GWO algorithm for minimizing system energy loss has been presented in [43]. Although metaheuristic techniques are efficient in solving different optimization problems, they may require more simulation time in complex optimization problems.…”