چكيده به لاتين
In recent years, the use of residential demand response to address environmental problems and reduce the cost of electricity generation and consumption and expand the penetration of renewable resources such as wind and solar have been considered. This thesis, presents demand response scheduling for a residential community considering uncertainty of renewable energy resources and initial electric vehicles (EVs) charge.
Different types of uncertainty modeling for residential load scheduling such as Monte Carlo simulation, defuzzification, robust optimization and etc. is investigated so far, which they need probability distribution function and a large amount of calculations because of their complexity and high number of scenarios.
In this thesis by using information gap decision theory variation of uncertainty radius on home community electricity costs is studied. The loads of this residential community is categorized into interruptible, illumination, air conditioning and shiftable loads besides EVs. According to their contracts with system operator, EVs are classified into two groups: only charging EVs and, charging and discharging EVs.
Scheduled loads of residential community and effects of RTP program for different amounts of uncertainty is presented which shows the effectiveness of proposed model. Furthermore, efficacy of categorizing EVs is demonstrated. Besides, this model provides an explicit guide for system operator in confrontation of uncertainties.
