Value-based allocation and settlement of reserves in electricity markets

This study proposes a new 'value-based probabilistic optimal allocation' methodology for energy and spinning reserve through cost-benefits analysis approach. The model implements an AC optimal power flow (AC OPF)-based co-optimisation algorithm for dispatching energy and operating reserve. A state sampling Monte Carlo simulation (MCS) approach is implemented to model random failure of system elements during the AC-OPF solution. The proposed MCS-based technique is simple and efficient, and yet models the system uncertainties and network constraints. Three types of generators for active and reactive energy, fictitious spinning reserves and fictitious negative energy generation are modelled in the AC-OPF algorithm. A comparison of reserve dispatch from the deterministic approach and the proposed value-based approach are presented. The finding suggests that for an optimal dispatch of spinning reserve, an independent system operator (ISO) will have to seek a balance between benefits and cost of providing the services. Sensitivity studies of optimal operating reserve dispatch based on the cost of generation, value of lost load (VOLL) and network size were investigated. This study also proposes a new solution methodology based on the MCS for allocation and settlement of reserves. The 24 bus IEEE-reliability test system (RTS) has been used to demonstrate the performance of the proposed methodologies.