A Chance-Constraints-Based Control Strategy for Microgrids With Energy Storage and Integrated Electric Vehicles

作者：Ravichandran Adhithya; Sirouspour Shahin^*; Malysz Pawel; Emadi Ali

来源：IEEE Transactions on Smart Grid, 2018, 9(1): 346-359.

DOI：10.1109/TSG.2016.2552173

• Microgrid
• energy management
• vehicle to grid (V2G)
• rolling horizon control
• model predictive control
• optimization
• mixed-integer-linear program (MILP)
• chance constraints
• stochastic optimization
• Monte Carlo simulations

An online optimal control strategy for power flow management in microgrids with on-site battery, renewable energy sources, and integrated electric vehicles (EVs) is presented in this paper. An optimization problem in the form of a mixed integer linear program is formulated. It is executed over a rolling time horizon using predicted values of the microgrid electricity demand, renewable energy generation, EV connection and disconnection times, and the EV state of charge at time of connection. The solution to this optimization problem provides the on-site storage and EV charge/discharge powers. Both bidirectional and unidirectional charging scenarios are considered for EVs. The proposed optimal controller maximizes economic benefits and ensures user-specified charge levels are reached at the time of EV disconnection from the microgrid. By formulating the problem as a stochastic chance constraints optimization, significant improvement is shown in the system robustness over conventional rolling horizon controller, while dealing with uncertainties in the predictions of demand/generation, and EV state of charge and connection/disconnection times. Results of Monte Carlo simulations show that the proposed chance constraints-based controller is highly effective in reducing cost and meeting the user desired EV charge level at time of disconnection from the microgrid, even in the presence of uncertainty.

• 出版日期2018-1