Demand Response Optimization Scheduling Strategy for Data Center Aggregators Oriented to Multi-Period Power Balance Analysis
With the increasing penetration of renewable energy and the diversification of load patterns, power and energy balance analysis is facing coupled challenges involving multiple periods, multiple scenarios, and uncertainties on both the supply and demand sides. Data centers possess the potential for spatial and temporal load shifting as well as aggregated dispatch, enabling them to participate in system balancing and dispatch optimization as demand response resources. This paper constructs a data center aggregator framework, develops a load demand response model that accounts for spatial-temporal shifting, and formulates an optimization scheduling model applicable to multi-period power balance analysis, with the objectives of minimizing data center operating costs and maximizing aggregator benefits. The Karush-Kuhn-Tucker conditions and the big-M method are employed to transform the bilevel model into a single-level mixed-integer linear programming model for solution. The results show that the proposed strategy can enhance the workload flexibility of data centers, reduce operating costs, and support power balance analysis and dispatch strategy optimization over annual, seasonal, monthly, and weekly time scales.