A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

doi:10.1007/s40565-014-0087-6

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A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

DOI:

10.1007/s40565-014-0087-6

Author:

Zhile YANG
Zhile YANG
School of Electrical, Electronics and Computer Science, Queen’s University Belfast, Belfast, BT9 5AH, UK
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Kang LI
Kang LI
School of Electrical, Electronics and Computer Science, Queen’s University Belfast, Belfast, BT9 5AH, UK
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Qun NIU
Qun NIU
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China
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Yusheng XUE
Yusheng XUE
NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing, 211106, China
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Aoife FOLEY
Aoife FOLEY
School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, BT9 5AH, UK
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Affiliation:

1. School of Electrical, Electronics and Computer Science, Queen’s University Belfast, Belfast, BT9 5AH, UK 2. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China 3. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing, 211106, China 4. School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, BT9 5AH, UK

Fund Project:

UK Engi-neering and Physical Sciences Research Council (EPSRC) , UK EPSRC under grant EP/L001063/1 and China NSFC under grants51361130153 and 61273040.

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Abstract:

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two schedu ling problems are commonly formulated with non-smooth cost functions respectively con-sidering various effects and constraints, such as the valve pointeffect, power balance and ramp rate limits. The expected increasein plug-in electric vehicles is li kely to see a significant impact onthe power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profil es are comparatively integratedinto a 24-hour load demand in an economic and environment dispatch model. Self-learning t eaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known bench mark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Key words:Economic dispatch, Environmental dispatch, Plug-in electric vehicle, Self-learning, Teaching learning based optimization, Peak charging, Off-peak charging, Stochastic charging

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Online: May 22,2015
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