Abstract
In this paper, the uncertainty of wind, solar and load; smart charging and discharging of plug-in hybrid electric vehicles (PHEVs) to and from various energy sources; and the coordination of wind, solar power, PHEVs and cost-emission are considered in the smart grid unit commitment (UC). First, a multi-scenario simulation is used in which a set of valid scenarios is considered for the uncertainties of wind and solar energy sources and load. Then the UC problem for the set of scenarios is decomposed into the optimization of interactive agents by multi-agent technology. Agents’ action is represented by a genetic algorithm with adaptive crossover and mutation operators. The adaptive co-evolution of agents is reached by adaptive cooperative multipliers. Finally, simulation is implemented on an example of a power system containing thermal units, a wind farm, solar power plants and PHEVs. The results show the effectiveness of the proposed method. Thermal units, wind, solar power and PHEVs are mutually complementarily by the adaptive cooperative mechanism. The adaptive multipliers’ updating strategy can save more computational time and further improve the efficiency.
Highlights
In term of economic development and environmental protection, the power and energy industry is one of the most important sectors in the world, since every aspect of industrial productivity and daily life are dependent on electric power
Several other research efforts of plug-in hybrid electric vehicles (PHEVs) in recent years examine the impact of PHEVs on the power system but do not take renewable energy into account [3,4]
Cost effects for integrating PHEVs charging into unit commitment (UC) are investigated [6], and economic savings of up to 7% are found for the overall system through smart charging strategies
Summary
In term of economic development and environmental protection, the power and energy industry is one of the most important sectors in the world, since every aspect of industrial productivity and daily life are dependent on electric power. Cost effects for integrating PHEVs charging into unit commitment (UC) are investigated [6], and economic savings of up to 7% are found for the overall system through smart charging strategies. These studies only consider PHEVs charging states. Stochastic commitment decisions are made given updated wind forecast information and the probability of different scenarios occurring This approach comes closer to capturing the uncertainty faced by system operators. A cooperative co-evolution algorithm based on MAS is proposed for the smart grid environment powered by TUs, solar and wind power, and PHEVs. The PHEVs’ charging and discharging control, the coordination of PHEVs, and wind power are.
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