Day-ahead resource scheduling including demand response for electric vehicles

Joao Soares; Hugo Morais; Tiago Sousa; Zita Vale; Pedro Faria

doi:10.1109/PESGM.2014.6939118

Day-ahead resource scheduling including demand response for electric vehicles

Joao Soares, Hugo Morais, Tiago Sousa, Zita Vale, Pedro Faria

Porto Engineering Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

Abstract

Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Original language	English
Title of host publication	2014 IEEE PES General Meeting : Conference & Exposition
Number of pages	1
Publisher	IEEE
Publication date	2014
DOIs	https://doi.org/10.1109/PESGM.2014.6939118
Publication status	Published - 2014
Event	2014 IEEE PES General Meeting - National Harbor, Washington, United States Duration: 27 Jul 2014 → 31 Jul 2014

Conference

Conference	2014 IEEE PES General Meeting
Location	National Harbor
Country/Territory	United States
City	Washington
Period	27/07/2014 → 31/07/2014

Keywords

demand side management
distributed power generation
electric vehicles
energy management systems
integer programming
nonlinear programming
particle swarm optimisation
scheduling
smart power grids
Engineering Profession
Power, Energy and Industry Applications
33-bus distribution network
computational intelligence techniques
day-ahead energy resource scheduling
day-ahead energy resources management
demand response
deterministic techniques
distributed generation
Distributed power generation
distributed resources
DR programs
Educational institutions
Electric vehicles
Energy resources
EV management approaches
full AC power flow calculation
Load management
massive gridable vehicle
mixed integer nonlinear programming
modified particle swarm optimization
network constraints
smart charging
Smart grids
uncontrolled charging
V2G approach

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PESGM.2014.6939118

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Cite this

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abstract = "Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.",

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AU - Morais, Hugo

AU - Sousa, Tiago

AU - Vale, Zita

AU - Faria, Pedro

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ER -

Day-ahead resource scheduling including demand response for electric vehicles

Abstract

Conference

Keywords

UN SDGs

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