Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties

Grid scale battery integration plays an important role in renewable energy integration and the formation of smart grid. To mitigate the problems of insufficient frequency response and peak regulation capacities faced by modern power grids with high wind energy uptake, a day-ahead optimization dispatch strategy considering operational risks is proposed in this study. In the day-ahead dispatch model, generation units and a large-scale battery energy storage station (LS-BESS) are coordinated to participate in multi-type frequency control ancillary services (FCASs). For optimal performance, scheduling in different timescales and the complementarity between power and energy types of requirements are coordinated, with various service uncertainties considered. Then the conditional value-at-risk theory is utilized to achieve the optimal mix of multiple resources for day-ahead regulation reserves, to realize the minimum operation cost. To tackle the uncertainty of wind power over a large timescale, a robust optimization (RO) approach based on the budget uncertainty set is employed, which considers robustness and economy. This can help avoid over-conservation of the standard RO and enhance the applicability of the decisions made. Simulation studies and comparison analysis of multiple schemes verify the effectiveness of the proposed optimal day-ahead dispatch strategy, which also demonstrate that a LS-BESS participating in multiple FCASs for day-ahead dispatch can help realize secure, reliable, and economic power grid.

Automated summary

Grid scale battery integration plays a crucial role in integrating renewable energy and forming smart grids. This study proposes a day-ahead optimization dispatch strategy that coordinates generation units and a large-scale battery energy storage station to participate in multi-type frequency control ancillary services, aiming to minimize operation costs and address wind power uncertainty.