Strategic Network Expansion Planning with Electric Vehicle Smart Charging Concepts as Investment Options

Transmission network expansion planning is a very critical problem due to not only the huge investment cost involved, but also the associated security issues. Any long range planning problem is confronted with the challenge of non-statistical uncertainty in the data. Although large number of papers have been published in this area, the efforts to tackle the above mentioned security and uncertainty issues have been relatively very few, due to the formidable complexity involved. This paper tries to bridge this gap by proposing a technique to tackle these problems. Boundary DC power flow is used to ascertain the worst power flows on the lines. A simple basic binary Genetic algorithm is used to solve the optimization problem as an illustration. Results for two sample test systems have been obtained to demonstrate the potential of the proposed method.

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Comparison of particle swarm based meta-heuristics for the electric transmission network expansion planning problem

2011 IEEE Power and Energy Society General Meeting ◽

10.1109/pes.2011.6039571 ◽

2011 ◽

Cited By ~ 12

Author(s):

Santiago P. Torres ◽

Carlos A. Castro ◽

Rolando M. Pringles ◽

Wilson Guaman

Keyword(s):

Particle Swarm ◽

Planning Problem ◽

Transmission Network ◽

Network Expansion ◽

Expansion Planning ◽

Electric Transmission

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Urban underground network expansion planning

IEE Proceedings - Generation Transmission and Distribution ◽

10.1049/ip-gtd:19970850 ◽

1997 ◽

Vol 144 (2) ◽

pp. 118 ◽

Cited By ~ 8

Author(s):

Z. Božić ◽

E. Hobson

Keyword(s):

Network Expansion ◽

Expansion Planning

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Smart charging for electric vehicles to minimise charging cost

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650916688409 ◽

2017 ◽

Vol 231 (6) ◽

pp. 526-534 ◽

Cited By ~ 5

Author(s):

Yue Wang ◽

David Infield ◽

Simon Gill

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Distribution System ◽

Power Flow ◽

Optimal Power Flow ◽

Low Voltage ◽

Wholesale Price ◽

Heuristic Method ◽

Domain Model ◽

Smart Charging

This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system are available. Based on this information, an aggregator with access to this data can be employed to minimise electric vehicles charging costs to the owner whilst maintaining acceptable distribution system voltages. In this study, electric vehicle charging is assumed to take place only in the home. A single-phase Low Voltage (LV) distribution network is investigated where the local electric vehicles penetration level is assumed to be 100%. Electric vehicle use patterns have been extracted from the UK Time of Use Survey data with a 10-min resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of electric vehicles charging. A simple and effective heuristic method is proposed to minimise the electric vehicles’ charging cost whilst satisfying the requirement of state of charge for the electric vehicles’ battery. A simulation in OpenDSS over a period of 24 h has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimisation results are compared with those obtained using dynamic optimal power flow.

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