Stochastic load and renewable resource control for smart distribution system
Electric vehicles (EVs) have been introduced as the demand for green transportation has increased. EVs require charging. Studies have shown that uncontrolled EV charging in certain areas of the community reduces the life of the distribution components, such as the secondary distribution transformer. One approach to remedying this is to increase the rating of the secondary distribution transformer, but by doing so, the no-load losses also increase. Minimizing no-load losses while deciding an optimal rating for the transformer during controlled and uncontrolled EV charging for an average day is studied in this work. The study is extended to include an hourly EV charging load. Along with no-load losses, the cost of the no-load losses and the cost of the transformer are minimized while deciding the optimal transformer rating. Controlling EV charging is another approach to controlling distribution losses and the transformer loss of life. Customer participation in delaying charging until getting the desired price is the approach taken in the next part of this study. Customer anxiety is defined and used as a measure to control EV charging behavior. The impact of customer anxiety on EV charging behavior is analyzed. An algorithm to control EV charging using customer anxiety as a measure is suggested in this work. The use of renewable resources along with controlled and uncontrolled EV charging and its impacts on the distribution transformer is studied here. Controlled EV charging deteriorates transformer life much less than uncontrolled EV charging does. The optimal transformer rating increases for uncontrolled EV charging versus controlled EV charging. When residential controlled EV charging along with energy from rooftop solar panels is used, the transformer loss of life is considerably reduced. With customer participation, EV charging can be controlled; hence, customer anxiety level can be a good measure for the utility to use in order to provide a plan-ahead system for EV charging.