Optimising the balance between security and economy on a probabilistic basis

Decision-making methods based on a deterministic criterion, such as optimal power flow (OPF) and security-constrained optimal power flow (SCOPF), have been widely applied to practical power system operation. With these methods, the operating conditions of the power system are classified as secure or insecure based on predefined deterministic criteria. However, such a binary secure/insecure index based on a deterministic analysis does not take into account the relative risks associated with random outages of generation and transmission facilities. Therefore using deterministic decision-making methods such as OPF and SCOPF to balance the security and economy can be unnecessarily expensive. This study proposes an optimal probabilistic security (OPS) approach, which balances security and economy on a probabilistic basis. The objective of the OPS is to minimise the expected social cost, which is the sum of the expected operating cost and the expected interruption cost. An improved particle swarm optimisation technique is used to solve this probabilistic optimisation problem. Unlike other risk-based decision-making algorithms, the OPS is a self-contained decision-making tool, which does not require the definition of an arbitrary risk threshold. The OPS determines operator actions that optimally balance security and economy for given weather and operating conditions. The OPS is compared with the OPF and SCOPF using a six-bus system and the IEEE-RTS.