Abstract
Battery energy storage (BES) and demand response (DR) are considered to be promising technologies to cope with the uncertainty of renewable energy sources (RES) and the load in the microgrid (MG). Considering the distinct prediction accuracies of the RES and load at different timescales, it is essential to incorporate the multi-timescale characteristics of BES and DR in MG energy management. Under this background, a hierarchical energy management framework is put forward for an MG including multi-timescale BES and DR to optimize operation with the uncertainty of RES as well as load. This framework comprises three stages of scheduling: day-ahead scheduling (DAS), hour-ahead scheduling (HAS), and real-time scheduling (RTS). In DAS, a scenario-based stochastic optimization model is established to minimize the expected operating cost of MG, while ensuring its safe operation. The HAS is utilized to bridge DAS and RTS. In RTS, a control strategy is proposed to eliminate the imbalanced power owing to the fluctuations of RES and load. Then, a decomposition-based algorithm is adopted to settle the models in DAS and HAS. Simulation results on a seven-bus MG validate the effectiveness of the proposed methodology.
Highlights
For the energy crisis and environmental degradation, the evolution of the energy system is accelerating in the direction of a higher proportion of renewable energy sources (RES)
A hierarchical energy management framework incorporating the multi-timescale characteristics of the Battery energy storage (BES) and demand response (DR) along with the security constraints is proposed for an MG in grid-connected mode, which consists of day-ahead scheduling (DAS), hour-ahead scheduling (HAS), as well as real-time scheduling (RTS)
Given that the operation states of controllable distributed generation (CDG) have been yielded in DAS, the CDG constraints only comprise the power limit constraint, the ramp constraint, and the reserve constraint, which are similar to the Constraints (7), (8), and (12), respectively
Summary
For the energy crisis and environmental degradation, the evolution of the energy system is accelerating in the direction of a higher proportion of renewable energy sources (RES). The above studies adopted multiple optimizing methods, such as stochastic programming [2,3,4,5], fuzzy programming [6], robust programming [7], and interval programming [8], to handle the uncertainty, and reduce the operation risks of MG These researches did not employ the intra-day forecast data of RES with higher precision in MG energy management, which affect the control effects of such energy management methods. This may discourage DR resources to facilitate system balance and RES integration on different timescales In this regard, Pourmousavi et al [24] proposed a multi-timescale cost-effective energy scheduling framework for MG in isolated mode. A hierarchical energy management framework incorporating the multi-timescale characteristics of the BES and DR along with the security constraints is proposed for an MG in grid-connected mode, which consists of DAS, hour-ahead scheduling (HAS), as well as real-time scheduling (RTS).
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