In recent years, owing to the effect of fossil fuels on global warming, the exhaustion of oil fields, and the lucrative impacts of renewable energy resources (RESs), the penetration of RESs has been increasing significantly in power systems. An effective way to benefit from all RESs advantages is by applying them in microgrid systems (MGS). Furthermore, MGS can ease the way for utilizing a large amount of RESs, if its economic-environmental-technical aspects of it are taken into account. In this regard, this paper proposes an optimal solution for the energy management of a microgrid by considering a comprehensive study. In the proposed methodology, different distributed energy resources such as wind turbines generator (WTG), energy storage (ES), combined heat and power (CHP), rubbish burning agent (RBA), and diesel generators (DG) are modeled. In addition, electric vehicles (EVs) are considered a load with uncertainty. The objective function of the proposed method is to minimize the microgrid’s total cost by considering the microgrid’s emission cost and technical constraints. In this study, the microgrid’s technical, environmental, and economic aspects are investigated. In addition, the optimization problem is converted into a mixed-integer linear programming method by using the proper linearization method. In this paper, the increasing effect of wind energy penetration rate on the total price also has been studied. The simulation results show that by increasing the wind energy penetration rate by up to 30% of total power, the total cost will decrease by up to 30.9%.

We consider a drift-flux model (DFM) describing multiphase (gas-liquid) flow during drilling. The DFM uses a specific slip law, which allows for transition between single and two phase flows. With this model, we design unscented Kalman filter (UKF) and extended Kalman filter (EKF) for the estimation of unmeasured state, production, and slip parameters using real-time measurements of the bottom-hole pressure, outlet pressure, and outlet flow rate. The OLGA high-fidelity simulator is used to create two scenarios from underbalanced drilling on which the estimators are tested: a pipe connection scenario and a scenario with a changing production index (PI). A performance comparison reveals that both UKF and EKF are capable of identifying the PIs of gas and oil from the reservoir into the well with acceptable accuracy, while the UKF is more accurate than the EKF. Robustness of the UKF and EKF for the pipe connection scenario is studied in case of uncertainties and errors in the reservoir and well parameters of the model. It is found that these methods are very sensitive to errors in the reservoir pore pressure value. However, they are robust in the presence of error in the liquid density value of the model.