Ensuring Reserve Deployment In Network-Constrained Generation Scheduling Under Uncertain Nodal Net Power Injections

Over the last years, power systems have experienced a steady increase in the use of renewable-based power generation technologies such as wind power and photovoltaic generators. In order to cope with the uncertainty associated with those energy sources, increased levels of reserves are required. Within this new context, the determination of adequate reserve levels has become an issue of major concern. However, the effect of the transmission network on the deployment of reserves is typically disregarded in current unit-commitment-based generation scheduling models. Therefore, network congestion may lead to the undeliverability of reserves when eventually called by the system operator. This paper proposes a new approach relying on adjustable robust optimization to guarantee reserve deliverability in network-constrained generation scheduling under uncertainty. The robust counterpart is formulated as a mixed-integer trilevel program that is solved by Benders decomposition. Numerical results illustrate the operational advantages and the effectiveness of the proposed approach.