Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation

Abstract

The increasing penetration of distributed generations (DGs) and electric vehicles (EVs) offers not only several opportunities but also introduces many challenges for the distribution system operators (DSOs) regarding power quality. This article investigates the network performances due to uncoordinated DG and EV distribution. It also considers power quality-related performances such as the neutral current, energy loss, voltage imbalance, and bus voltage as a multiobjective optimization problem. The differential evolution optimization algorithm is employed to solve the multiobjective optimization problem to coordinate EV and DG in a distribution grid. This article proposed a method to coordinate EV and DG distribution. The proposed method allows DSOs to jointly optimize the phase sequence and optimal dispatch of DGs to improve the network's performance. If the network requires further improvement, the EV charging or discharging rate is coordinated for a particular location. The efficacy of the proposed method is tested in an Australian low-voltage distribution grid considering the amount of imbalance due to higher penetration of DG and EV. It is observed that the proposed method reduces voltage unbalance factor by up to 98.24%, neutral current up to 94%, and energy loss by 59.45%, and improve bus voltage by 10.42%. Index Terms-Compensating neutral current, coordinated/ uncoordinated charging, distributed generation (DG) dispatch, electric vehicle (EV), mitigating voltage unbalance.

Empirical continuous metaheuristic for multiple distributed generation scheduling considering energy loss minimisation, voltage and unbalance regulatory limits

“…The search-space is explored through four main equations, which are chosen via biased chance x n, g t + 1 = x n, g t + rw (14) x n, g t + 1 = x n, g t + rand ⋅ x * − x n, g t (15) x n, g t + 1 = x New (16) x n, g t + 1 = x n, g t + rand ⋅ X bests g − x n, g t + rw (17) An individual n, belonging to a group g can take: (i) a random walk rw by (14); (ii) a direct step towards best solution x * by (15); (iii) a completely new random position x New in search-space through (16); (iv) a combined step towards the best of its group X bests g pe, plus random step rw, by (17). As highlighted in Fig.…”

“…A multiobjective PSO is employed while Monte Carlo simulations cope with the uncertainties of the renewable sources. In [15], the authors propose a multiobjective formulation to minimise power loss, voltage deviation and neutral current, which is a way to tackle unbalance levels. The chosen optimisation algorithm is the metaheuristic differential evolution (DE).…”

Convex Model for Estimation of Single-Phase Photovoltaic Impact on Existing Voltage Unbalance in Distribution Networks

“…The I max is an arbitrary large positive value and is equal to 100 in current calculations. The sets are defined in (16) and are valid for all the equations presented above. All presented variables are non-negative.…”

“…While minimizing VU, load flow algorithm was executed with the holomorphic embedding method. As a part of multiobjective electric network optimization, VU minimization was presented in [16]. A novel differential evolution algorithm was employed for the optimization and VU was calculated based on nonconvex sequence components, that were provided by nonlinear load flow algorithm.…”

A New Two-Stage Approach to Coordinate Electrical Vehicles for Satisfaction of Grid and Customer Requirements

“…There have been a number of studies that try to schedule/manage time charging/ discharging of EVs properly to avoid these problems and is commonly known as smart V2G in Reactive Power [30] --- [31] ---- [32] --- [33] ---- [34] -- [35] --- [36] --- [37] --- [38] ----Proposed Method Thus, this paper tries to address the research gaps outlined in the paragraphs above. It proposes a two-stage strategy to mitigate different electrical issues, such as VUF, voltage deviation, and prosumer costs, using V2G technology.…”