A Novel Probabilistic Method for Generating Scheduling of Multi-Zone Virtual Power Plants

Microgrids are practical samples of the concept of decentralized power production. In this regard, after determination of appropriate technologies and the capacity of power generation and storage units for defensive objectives, the optimal scheduling of units in microgrids is of the crical importance. Thus, scheduling of distributed generations and energy storages in form of virtual power plant have gained significant attentions of both owner of these units and the operators of distributions aiming at increasing the system efficiency. This paper proposes a model for the optimal day-ahead scheduling of electrical and thermal units in a large scale virtual power plant. This plant includes a number of combined heat and power units, distribution consumers, a parking for plug-in hybrid electric vehicles with the ability to follow a smart charging pattern, boilers, renewable energy based generators and storage units. The presence of large number of storages, especially electrical vehicles, in these types of microgrids enhances the resiliency of the grids when encountering malicious attacks. Uncertaintie associated with electrical and thermal loads is modeled using a probabilistic programing while Monte-Carlo simulation has been utilized to model uncertainty in the behavior of electrical vehicles. Simulations have accomplished in two scenarios: 1) normal and 2) islanding operation after a deliberate attack. MATLAB software and cuckoo optimization algorithm are used for optimization task in this paper.