Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO

2018 ◽  
Vol 128 ◽  
pp. 91-107 ◽  
Author(s):  
Yachao Zhang ◽  
Jian Le ◽  
Xiaobing Liao ◽  
Feng Zheng ◽  
Kaipei Liu ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Thermal Wind ◽  
Multi Objective

scholarly journals Multi-Objective Optimal Dispatching of Microgrid With Large-Scale Electric Vehicles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 145880-145888 ◽  
Author(s):  
Haoyu Jiang ◽  
Shiyuan Ning ◽  
Quanbo Ge
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Multi Objective

scholarly journals Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

2021 ◽  
pp. 036119812110315
Author(s):  
Kyoungho Ahn ◽  
Youssef Bichiou ◽  
Mohamed Farag ◽  
Hesham A. Rakha
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Electric Vehicles ◽  
Large Scale ◽  
Traffic Assignment ◽  
Combustion Engine ◽  
Model Development ◽  
User Equilibrium ◽  
Multi Objective ◽  
Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

scholarly journals Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs)

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2595 ◽  
Author(s):  
Guozhong Liu ◽  
Li Kang ◽  
Zeyu Luan ◽  
Jing Qiu ◽  
Fenglei Zheng
Keyword(s):  
Case Studies ◽  
Traffic Flow ◽  
Electric Vehicles ◽  
Distribution System ◽  
Large Scale ◽  
Distribution Networks ◽  
Fuzzy Membership Function ◽  
Multi Objective ◽  
Charging Stations ◽  
The Impact

The optimal location and size of charging stations are important considerations in relation to the large-scale application of electric vehicles (EVs). In this context, considering that charging stations are both traffic service facilities and common electric facilities, a multi-objective model is built, with the objectives of maximizing the captured traffic flow in traffic networks and minimizing the power loss in distribution networks. There are two kinds of charging stations that are considered in this paper, and the planning of EV charge stations and distribution networks is jointly modelled. The formulated multi-objective optimization problem is handled by a fuzzy membership function. The genetic algorithm (GA) is used to solve the objective function. In case studies, a 33-node distribution system and a 25-node traffic network are used to verify the effectiveness of the proposed model. The location and capacity of two kinds of charging stations are designed in the case studies, after which the impact of the battery on the captured traffic flow is analyzed as well.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals A novel encoding for separable large-scale multi-objective problems and its application to the optimisation of housing stock improvements

2020 ◽  
Vol 96 ◽  
pp. 106650
Author(s):  
Alexander E.I. Brownlee ◽  
Jonathan A. Wright ◽  
Miaomiao He ◽  
Timothy Lee ◽  
Paul McMenemy
Keyword(s):  
Large Scale ◽  
Housing Stock ◽  
Multi Objective

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

Sign in / Sign up

Export Citation Format

Share Document