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 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 ramp rate 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 on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.
Highlights
One of the key operational activities in the power system is to schedule power production according to the predicted load demands
The Dynamic economic and environmental dispatches (DEED) problem is a multi-objective problem combining the economic dispatch objective denoted as F1 and environmental dispatch objective denoted as F2
Dynamic economic dispatch has long been an intractable problem for power system operators and the complexity is ever increasing with new participants such as Plug-in electric vehicles (PEVs) entering in the equation
Summary
One of the key operational activities in the power system is to schedule power production according to the predicted load demands. Four different PEV charging scenarios are modeled using charging time probability distribution, based on PEV charging data from Electric Power Research Institute (EPRI), a full peak charging scenario and a full off-peak charging scenario and a stochastic charging scenario These four charging time probability distributions are measured with a certain number of charging PEVs and integrated in the power demand of a 5-unit system and a 15-unit system respectively. Both the economic and environmental impacts are evaluated by solving the dynamic dispatch problems. For the four charging profiles, the comparative studies show that the off-peak charging scenario is the most economical and an environmental friendly choice
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