A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid. (December 2016)

• Record Type: Journal Article
• Authors: Moghaddas Tafreshi, Seyed Masoud; Ranjbarzadeh, Hassan; Jafari, Mehdi; Khayyam, Hamid
• Is Part Of: Renewable & sustainable energy reviews. Volume 66(2017)
• Page Start: 934
• Page End: 947
• Publication Date: 2016-12
• Subjects: Microgrid -- Uncertainty Modelling -- Unit commitment -- Plug-in electric vehicles -- Vehicle to grid -- Probabilistic modelling -- Stochastic modelling -- Particle swarm optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.08.013
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: This paper presents a probabilistic Unit Commitment (UC) model for optimal scheduling of wind power, load forecasts and controllability of vehicles in a microgrid using a stochastic programming framework. The microgrid is made up of microturbines, wind turbine, boiler, Plug-in Electric Vehicles (PEVs), thermal storage and battery storage. The proposed model will help the power grid operators with optimal day- ahead planning even with variable operating conditions in respect of load forecasts, controllability of vehicles and wind generation. A set of valid scenarios is assigned for the uncertainties of wind sources, load and PEVs and objective function in the form of expected value. The objective function is to maximize the expected total profit of the UC schedule for the set of scenarios from the viewpoint of microgrid management. The probabilistic unit commitment optimizes the objective function using Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the stochastic modelling and make a comparison with a simple deterministic one, a typical microgrid is used as a case study. The results can be used to evaluate the effect of integration of PEVs on the economic operation of the microgrid. The results also confirm the necessity to consider the key uncertainties of the microgrid; otherwise the results could overly misrepresent the real world operation of the system.