Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Abstract

There has been a growing presence of electric vehicles in many countries including Thailand, where many forms of incentives have been provided to build integrated infrastructure, and to encourage drivers to switch to electric vehicles (EVs). Because the immediate entry of EVs unavoidably can alter household load profiles, reinforcement on the existing system based on traditional planning may not be sufficient and can introduce over or under capital and operating expenditure over the time horizon. Therefore, if distribution systems are unreadily prepared for such an uptake, three obvious problems can be expected: 1) voltage regulation, 2) overloads of the distribution feeders and the distribution transformers, and 3) high energy loss. In this paper, an activity-based, time-sequential Monte Carlo Simulation algorithm was comprehensively developed for uncontrollable and smart charging, given annually updated information of EV locations and number of EVs, their energy consumption, hourly average vehicle speed, number of daily trips, travel distance per trip, size of EV batteries, time to arrive home and time to leave home. Minimizing the annual sum of investment and operating costs over a planning period could then be sequentially solved by a Particle Swarm Optimization (PSO) algorithm. The results from a practical 122-bus, 24 kV/400 V distribution system with different scenarios of uncontrollable and smart charging show that the sequential optimization embedded with deterministic decision can help improve customer voltage profile, keep feeder and transformer loading within acceptable operating limits and offer significant cost savings from energy loss. As far as a large number of low-voltage networks, and the associated large sum of cost savings are concerned, the proposed planning framework is practical to be applied and expected to be served as a new guideline for future implementation in Thailand.

Charging Management of Electric Vehicles on Loading Capability of Distribution System Equipment, Voltage Quality, and Energy Loss by Monte Carlo Simulation and Linear Programming

“…Direct control through smart charging algorithms was implemented to improve voltage quality and avoid customer inconvenience. Tis research is consistent with reference [34], showing that solutions to the voltage drop problem could be solved by adding capacitors. Installing capacitors provided a short-term solution, while adding a new distribution transformer ofered a more sustainable long-term solution, but at a higher cost.…”

Developing an Integration of Smart-Inverter-Based Hosting-Capacity Enhancement in Dynamic Expansion Planning of PV-Penetrated LV Distribution Networks

“…To model dynamic expansion planning of active LV distribution networks, some logical constraints are proposed. The upper limit of (26) shows that for transformer t at Year y, only one alternative size can be selected. In addition, the lower limit shows that a line from transformer t to load point l can be invested if the transformer t is invested.…”

Optimal multi‐period planning of the distribution network in the presence of wide‐spread penetration of small‐scale solar resources

“…In [17], a model is proposed for the optimal planning problem of the large‐scale urban electricity distribution network in the presence of low voltage direct current distribution technology and the imperialist competitive algorithm to find the minimum construction costs and system losses. The model presented in [25] minimizes the total annual cost due to equipment, installation, removal, and energy loss costs under the annual penetration of electric vehicles in a dynamic planning horizon. This model is formulated as a hybrid nonlinear optimization problem.…”