Dynamic reserve zones for day-ahead unit commitment with renewable resources

As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With today's deterministic reserve requirements unable to ensure optimal reserve locations, improvements to reserve policies are needed to ensure reserve deliverability while maintaining a reliable system at least cost. This paper proposes a daily reserve zone determination procedure, which is able to reflect system operating conditions by utilizing probabilistic power flows. A statistical clustering algorithm is used to cluster buses together to produce the zones; the algorithm uses a centrality measurement, which is based on weighted power transfer distribution factors. The proposed method is validated by testing it on a modified IEEE 118-bus system for multiple days; the proposed method is compared against existing reserve zone partitioning procedures. While the proposed reserve zone determination method is a heuristic, it is shown to be effective and it is a computationally tractable method. The proposed method can be used on its own and can be used along with stochastic programming techniques that implicitly determine reserves.