Scheduling distributed energy resources and smart buildings of a microgrid via multi-time scale and model predictive control method

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid