Review of Methodologies for the Assessment of Feasible Operating Regions at the TSO–DSO Interface

Abstract

The Feasible Operating Region (FOR) is defined as a set of points in the PQ plane that includes all the feasible active and reactive power flows at the Transmission System Operator (TSO)–Distribution System Operator (DSO) interconnection. Recent trends in power systems worldwide increase the need of cooperation between the TSO and the DSO for flexibility provision. In the current landscape, the efficient and accurate estimation of the FOR could unlock the potential of the DSO to provide flexibility to the TSO. To that end, much existing research has tackled the problem of FOR estimation, which is a challenging problem. However, no research that adequately organizes the literature exists. This work aims to fill this gap. Three categories of FOR estimation methods were identified: Geometric, Random Sampling, and Optimization-Based methods. The basic principles behind each method are analyzed and the most significant works involving each method are presented. For the reviewed works, we focus on the types of flexibility providing units included in the FOR estimation, the examination of time dependence, and the monetization of the FOR. Finally, the strengths and weaknesses of each category of methods are compared, providing a holistic review of the available FOR estimation methods.

Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem

“…Firstly, GA or PSO OPF algorithms can be easily created for free by practitioners using open-source tools that are available online, while most mixed-integer non-linear solvers are commercial and come with a hefty cost. Secondly, as suggested by [80], deterministic optimization techniques require the full topology of the network to be available. With the proliferation of distributed generation and the need to run OPF analyses in the distribution network, where the observability of the network is more often than not limited, practitioners may need to rely on measurements to infer the topologies (e.g., use "black-box" neural network approaches).…”