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Abstract This paper proposes a stochastic framework to augment the integration of variable renewable energy sources (VRESs) in power system scheduling. In this way, the fast-response capability of gas-fired generator units (GFGUs) and vehicle-to-grid (V2G) capability of electric vehicles (EVs) can play important roles in large-scale integration of VRESs. However, the growth of GFGUs utilization can increase the grade of interdependency between power and natural gas systems. In this condition, the power system tends to demand more reliability and flexibility from the natural gas system, which creates new challenges in power system scheduling. The likely significant growth of EVs can solve this challenge and reduce the correlation between power and natural gas systems, bringing new opportunities for power system scheduling. However, a considerable literature in the field of operation of GFGUs and EVs has only focused on using the hourly discrete time model (HDTM). Undoubtedly, the major limitation of HDTM is its inability to handle the fast sub-hourly dispatch of GFGUs and energy storage capability of EVs. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). The reliability test system with a ten-node gas transmission system has been analysed to show the effectiveness of the proposed problem.

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.