An integrated optimization framework for combined heat and power units, distributed generation and plug-in electric vehicles. (1st July 2020)

• Record Type: Journal Article
• Authors: Bostan, Alireza; Nazar, Mehrdad Setayesh; Shafie-khah, Miadreza; Catalão, João P.S.
• Is Part Of: Energy. Volume 202(2020)
• Publication Date: 2020-07-01
• Subjects: Distributed energy resource -- Active distribution system -- Microgrid -- Demand response -- Bidding strategy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117789
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper provides a six-level integrated optimization framework for a distribution system that transacts energy with upward electricity market and downward active microgrids in day-ahead and real-time horizons. The proposed method uses a risk-averse formulation and the distribution system utilizes multiple combined heating and power units, distributed generation, plug-in electric vehicles parking lots, and electric and thermal storage units. Demand response program alternatives are also utilized by the distribution system. A three-stage uncertainty modeling is proposed to model six sources of uncertainties that are consist of energy resource power generations, loads and prices, active microgrids contributions and contingencies. Two case studies evaluate the proposed algorithm for the 123-bus test system that multiple 33-bus microgrid systems are transacting energy and ancillary services with the main grid. Further, different sensitivity analyses are performed to evaluate the effect of energy and ancillary services prices on the simulation results. Highlights: This paper presents an optimization framework for the optimal bidding strategy. The proposed method uses a risk-averse formulation. Each active microgrid optimizes its day-ahead and real-time scheduling problem. The demand response program alternatives are utilized by the distribution system. Two case studies evaluate the proposed algorithm.