Multi‐objective optimal planning of FCSs and DGs in distribution system with future EV load enhancement

Abstract

Current trends suggest that electrical vehicle (EV) is a promising technology for road transportation. There is a substantial increase in the number of EVs due to improved energy efficiency and reduction in environmental impact as compared with internal combustion engine vehicles. The improper planning of fast charging stations (FCSs) and distributed generations (DGs) hurts the distribution system. So the distribution system operator has a significant challenge to identify the optimal location and sizing of FCSs in the distribution power network. This study presents optimal planning of FCSs and DGs with the account of the present and future increase in EV population. A multi-objective optimisation problem is formulated for optimal planning of FCSs and DGs with the objective of minimising the voltage deviation, distribution network power loss, DGs cost and the energy consumption of EV users. This problem is solved for different levels of increase in EV population for different cases. A novel hybrid shuffled frog leap-teaching and learning based optimisation algorithm is proposed and implemented to solve the considered multi-objective problem. The performance of the proposed algorithm is compared with prior-art algorithms in the literature.

Hybrid Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy System: A Review of the State-of-the-Art

“…Battapothula et al [ 109 ] adopted the shuffled frog leap algorithm (SFLA) to enhance the exploitation capability of TLBO and create a novel algorithm named shuffled frog leap-teaching and learning-based optimization (SFL-TLBO). The proposed algorithm is used to determine the optimal location and size of a hybrid distributed renewable energy system and electric vehicles charging station with the minimization of voltage deviation, distribution network power loss, distributed generations (DGs) cost, and the energy consumption of electric vehicle users as objectives.…”

Integration of PV Distributed Generator in Electrical Distribution System with Electric Vehicles Charging Stations Considering Uncertainties using Chaotic SSA Algorithm

“…Various researchers on the planning problems concerning DGs and EVCSs in EDS can be found in numerous literatures. In the literature, numerous techniques, and algorithms to this problem: applied Mixed Integer Non-Linear Programming (MINLP) to maximize the benefit of the PEV-parking lot's owner [4], Mixed integer second-order cone programming (MISOCP) for minimize various annual cost in EDS [5], MISOCP for minimizing the total losses with maximizing the total DG and EV charging station [6], Probabilistic method and fuzzy theory for minimisation of DG installation, and maintenance costs [7], Genetic algorithm (GA) for maximize the profit measured by its net present value [8], Particle Swarm Optimisation (PSO) for maximisation of annual revenue for the power supply company [9], Differential evolutionary particle swarm (DEEPSO) algorithm to minimised electricity markets and wind DG cost [10], Grasshopper Optimizer Algorithm (GOA) for energy loss and voltage stability indexes reduction [11], Artificial Bee Colony (ABC) algorithm to minimize the power loss [12], Salp Swarm Algorithm (SSA) to minimize the fluctuation in the DC-bus voltage to the grid [13], Improved Differential Evolutional Algorithm (IDEA) for reduction the investment cost [14], Hybrid Optimization platform HOMER to minimizing the total net present cost [15], and recently proposed Hybrid optimization algorithm for minimising the voltage deviation, with power loss and DGs costs [16].…”

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

“…Step 10) If q≥0.5, update the hawk's position by using (20) otherwise update by using (25) and go to Step 11.…”

“…Compared to PSO, BO does well in achieving the desired goals. In [20], a multi-objective hybrid Shuffled Frog Leap Teaching Learning Based Optimization (SFL-TLBO) algorithm was proposed for better planning of EVCSs and DGs in the combined electrical distribution and transportation network, considering the objectives of voltage deviation, power loss, DG power cost, and energy consumption. The proposed model has been assessed on the IEEE 118 bus system and was confirmed to be accurate and stable for different EV population levels.…”