Data-Driven Distributionally Robust Optimization for Real-Time Economic Dispatch Considering Secondary Frequency Regulation Cost

Battery Energy Storage Systems (BESSs) and the Economy-Dynamics of Microgrids: Review, Analysis, and Classification for Standardization of BESSs Applications

“…Because a dynamic reaction is necessary to cover a mismatch between the load prediction and renewable forecast. In this sense, the development of real-time dynamics-aware energy management programs is essential which has been considered in a few works [92] and demands more studies and effective models.…”

“…In this light, a data-driven framework has been developed in [92], by which the probability distribution of frequency regulation (AGC) signals, in response to load/RESs variations, are used by a real-time economic dispatch program. However, the platform is developed in the context of conventional power systems where the frequency regulation mechanism is wellknown, and without clarifying the control system of the BESS.…”

“…Uncertainties disrupt the optimal OS and optimal power flow results given by day-ahead and real-time scheduling programs. Also, the SoC of BESSs might not be aligned with the prescheduled model due to the inertia and frequency support events [92]. To this end, with the aid of a communication link, which provides the information of all other nodes, i.e., generation of RESs, SoC of BESSs, etc., the online optimal scheduling program may be implemented [159].…”

“…6) The distributed consensus-based control technique has been adopted by the real-time/offline economic programs and also it is used as the secondary controller in the grid-forming BESSs (in the slack bus realization application). These two applications can be combined to develop real-time distributed dynamics-aware economic programs [92]. 7) The power/energy market mechanism must be upgraded by incorporating the virtual inertia/frequency response to facilitate pricing the inertia synthesis [188]- [189].…”

“…As an example, a typical day optimal scheduling is achieved by defining some indices from historic data such as load extreme deviation per time period and resource extreme deviation per time period and are used as constraints for the optimization problem [128]. Also, recently a data-driven frequency response identification/cost modeling has been proposed in [46], [92].…”