Autonomous multi-stage flexible optimal power flow
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In modern power systems, an increasing number of renewable resources and controllable devices are implemented every year. The conventional OPF that mainly models the generators, lines and loads, as well as some other devices considered due to specific reasons, is not suited for the modern networks. To deal with these new challenges, this PhD thesis develops a systematic way to formulate and solve the OPF problem autonomously. Two specific problems facing modern power systems are introduced, the multi-stage quadratic flexible OPF (MQFOPF) and the security constrained quadratic OPF (SCQOPF). The MQFOPF optimizes the operation of the system over multiple stages into the future, while the SCQOPF optimizes the operation of the system considering a number of contingencies to drastically improve operational security. To accommodate a huge number of devices, both old and new, in power systems, a physically based object-oriented modeling approach is utilized. A unified general expression is introduced for the device models, based on which the network model is constructed. Together with the objective function, an OPF problem is formed and a tailored sequential linear programming algorithm is used to compute the optimal solution. During the solution process, the constraints are included gradually and the efficient costate method is applied to linearizing the OPF model with respect to the control variables only. Due to object orientation, the whole formulation and solution process of the selected OPF problem is fully autonomous.