Probabilistic optimal power flow: An alternative solution for emerging high uncertain power systems

Abstract

This paper discusses the challenge of probabilistic optimal power flow (POPF) for handling of uncertainties in the emerging power system. The frameworks for probabilistic modeling for system loading and renewable power are proposed. The presently practical used optimal power flow (OPF) models are generally formulated as deterministic optimization problems and, therefore, can not represent uncertain factors, such as the variation of load and other forecasting errors. Moreover, in the emerging power system, renewable energy resources are taking bigger share in power generation including the effective demand response and high capacity of energy storage systems are widely implemented and developed. The new smart electricity grid is, therefore, necessarily able to manage and control the increasingly complex uncertain future grid characteristic. Therefore, it is necessary for the system operators to have the effective tool to incorporate those uncertainties in the OPF modeling and analysis. As a result, the OPF problem is transformed into the POPF problems in several recent researches. In this paper, the review of different POPF methods, including its possible developments and applications, are discussed. In addition, the investigations on practical load profile and renewable power, which are photovoltaic and wind powers behaviors, are addressed for illustrations.