期刊
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
卷 2, 期 4, 页码 298-307出版社
SPRINGEROPEN
DOI: 10.1007/s40565-014-0087-6
关键词
Economic dispatch; Environmental dispatch; Plug-in electric vehicle; Self-learning; Teaching learning
资金
- UK Engineering and Physical Sciences Research Council (EPSRC)
- UK EPSRC [EP/L001063/1]
- China NSFC [51361130153, 61273040]
- Engineering and Physical Sciences Research Council [EP/L001063/1, 1283156] Funding Source: researchfish
- EPSRC [EP/L001063/1] Funding Source: UKRI
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 scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramprate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results onwell-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.
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