Economic scheduling of a smart microgrid utilizing the benefits of plug-in electric vehicles contracts with a comprehensive model of information-gap decision theory

In this paper, a hybrid optimization approach is proposed to smart microgrid (SMG) operators to schedule the existing energy resources optimally to meet the load demand of the system. The proposed hybrid optimization is structured based on information-gap decision theory (IGDT) and two-stage stochastic programming to minimize the operational cost of the system in day-ahead (DA) and real-time (RT) power markets. The uncertainty of the day-ahead market price is tackled with the IGDT. The IGDT is furnished with robustness and opportunity functions leading to various risk-averse and risk-taker decision-making strategies. The problem is structured as bi-level programming, while by implementing the concept of envelop bounds, it is transferred into a single-level optimization problem. On the other hand, the uncertainties of the operation of the real-time market are modeled using scenarios with stochastic programming. The studied SMG is equipped with a photovoltaic system, a wind turbine, two microturbines (MTs), and battery storage. The results determine the scheduling of day-ahead exchange power and scheduled PEV contracts as here-and-now decisions of the optimization. However, the traded energy in the real-time market, MTs' dispatch, and the charging/discharging plan of battery storage are wait-and-see decisions made by the second stage of the programming. The results are obtained and discussed from the perspective of risk-neutral, risk-averse, and risk-taker decision-makers. As an example of performance, when the uncertainty horizon is increased by 10% in the robustness mode, the operation cost is increased by about 27.5%. In comparison, the same amount of increment of uncertainty horizon leads to cost-saving about 33.3% addressing the positive aspect of uncertainty using opportunity function.