Reserve zone determination based on statistical clustering methods

Abstract

Due to contingencies, uncertainties, and congestion both the amount and the location of operating reserve (spinning and non-spinning) are essential to ensure system reliability and market efficiency. Today's reserve zone determination methods are mainly based on ad-hoc rules, such as utilities ownership, geographical boundaries, or key transmission lines. Thus, there is a need for more systematic, theoretical ways to determine reserve zones. The mathematical representation of reserves within day-ahead unit commitment models does not account for congestion, which is why reserve zones are important: to ensure the deliverability of reserve. Even with reserve zones, reserves within a zone are assumed to have no deliverability problem, i.e., it is assumed that congestion will not prevent reserve to be delivered where needed within that zone. With the advent of variable renewable resources and other causes of uncertainty (load, electric vehicles), there is a need to improve the determination of reserve zones in order to ensure a reliable system. Statistical clustering techniques are employed to determine the reserve zones based on the power transfer distribution factors (PTDF) and electrical distances (ED).