Kanakri, Haitham M.2019-08-142019-08-142019-08-01http://hdl.handle.net/2097/40045This thesis proposes a Mixed Integer Non-Linear Programming (MINLP) stochastic energy model for an energy aggregator operating in the US distribution systems energy markets. Day- ahead, real-time and spot markets are considered as trading market options for the aggregator. When trading in real-time and spot markets; the aggregator faces multiple risks coming from load variability and uncertain market price. Deciding the selling price to be offered to the aggregator’s customers is a challenge for the aggregator. Uncertainties are modeled via stochastic programming and quantified via Conditional Value at Risk (CVaR). The aggregator’s optimal day-ahead selling prices to be offered to customers under real-time and spot prices uncertainty are determined by solving the proposed stochastic model. Changing the hourly prices offered to customers will change their hourly consumption resulting in a load redistribution during the day. Savings for the aggregator and customers will be gained by shifting the customers load to a lower price periods during the day. A case study is implemented to show the validity of the proposed model and influence of the aggregator in the distribution systems energy market.en-US© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).http://rightsstatements.org/vocab/InC/1.0/AggregatorDay-ahead marketReal-time marketConditional Value at Risk (CVaR)Load shiftingDemand Response (DR)Thesis