Topology Error and Bad Data Processing in Generalized State Estimation

Abstract

Abstract-This paper discusses the need for the integration of storage systems on transmission networks having renewable sources, and presents a tool for energy storage planning. The tool employs robust optimization to minimize the investment in storage units that guarantee a feasible system operation, without load or renewable power curtailment, for all scenarios in the convex hull of a discrete uncertainty set; it is termed ROSION -Robust Optimization of Storage Investment On Networks. The computational engine in ROSION is a specific tailored implementation of a column-and-constraint generation algorithm for two-stage robust optimization problems, where a lower and an upper bound on the optimal objective function value are successively calculated until convergence. The lower bound is computed using mixed-integer linear programming and the upper bound via linear programming applied to a sequence of similar problems. ROSION is demonstrated for storage planning on the IEEE 14-bus and 118-bus networks, and the robustness of the designs is validated via Monte-Carlo simulation.Index Terms-Design optimization, energy storage, integer linear programming, optimization methods, power system planning. NOMENCLATURE A. Storage Planning Problem BijLine susceptance between nodes i and j, defined as a positive number. Ei0Initial stored energy in the storage unit at node i. E ifFinal stored energy in the storage unit at node i.Maximum storage capacity of the unit at node i.Minimum storage capacity of the unit at node i. fi(.)Convex generation cost function at node i. KDPenalty coefficient for demand curtailment. KRPenalty coefficient for renewable energy curtailment. KSCapital recovery factor of storage investment.n Number of nodes in the system. NiNumber of linear segments in the cost curve of the generator at node i. PCDi (t) Demand power curtailment at node i and in period t. PCRi (t) Renewable power curtailment at node i and in period t. PDi (t) Power demand at node i and in period t. P Gik (t) Power generation at node i, over segment k, and in period t. PGi (t)Power generation at node i and in period t.

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

“…Hence, the objective is to use the system-wide measurements to update our knowledge of system configuration to avoid topology errors (i.e., state of switches, fuses, lines, DG/customer connection status.) Conventionally, generalized SE models have been used at the transmission level (with switch-related variables added to the SE formulation) to detect and correct topological errors [1] [54]. Similar classic methods have been applied to DSSE as well [55] [56].…”

A Mixed WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology

“…It can be observed from Table 5 that as the number of PMU configurations increases, we can get better results, which include voltage accuracy (V a ), angle accuracy (A a ), the condition number (K a ) and quality (Q t ). In statistics, bias of an estimator is the difference between estimator expected value and the true value of the parameter being estimated [40]. Based on mathematical theory, one has:…”

Roots, achievements, and prospects of power system state estimation: A review on handling corrupted measurements

“…Most of the studies involving topological errors are concentrated on analyzing measurement residuals, establishing conditions in which their occurrence can be detected . In critical cases, topology errors can be processed by the generalized SE approach . More recently, with the arrival of phasor measurement units (PMUs), the strategic placement of phasor and conventional measurements to enhance the topology error processing capability has been studied…”

“…[34][35][36][37][38] In critical cases, topology errors can be processed by the generalized SE approach. 39 More recently, with the arrival of phasor measurement units 40 (PMUs), the strategic placement of phasor and conventional measurements to enhance the topology error processing capability has been studied. 41…”