Data-Driven Reserve Allocation With Frequency Security Constraint Considering Inverter Air Conditioners

Abstract

Inverter air conditioners (IACs) with considerable total capacity and fast response speed are ideal demand response resources, which are of significant potential to provide reserve capacity for the power system frequency regulation. However, due to the complexity and implicitness of the frequency response models, it is difficult to formulate the optimization problem considering frequency dynamics to allocate reserve capacity precisely. In this paper, a data-driven method is proposed for reserve allocation with the frequency security constraint considering IACs. Firstly, the equivalent frequency response model of aggregated IACs is developed considering electrical-thermal characteristics and then incorporated into the frequency regulation framework of power systems along with conventional generators. Then, simulations are implemented to generate massive reserve samples with deterministic frequency security labels. Later, a support vector machine (SVM) based frequency security classifier is trained to convert the implicit frequency security constraint into polynomials and reshape the reserve allocation problem into a solvable general quadratically constrained quadratic program (QCQP). Finally, a heuristic Suggest-and-Improve (SI) method is adopted to deal with the nonconvex QCQP of interest. It is demonstrated by numerical studies that the proposed data-driven method enables power systems to operate closer to the frequency security boundaries and thus achieve lower costs.

Keywords

• Reserve allocation
• frequency security constraint
• inverter air conditioner
• classifier
• data-driven