A Framework for Phasor Measurement Placement in Hybrid State Estimation via Gauss-Newton

In this paper, we study the placement of Phasor Measurement Units (PMU) for enhancing hybrid state estimation via the traditional Gauss-Newton method, which uses measurements from both PMU devices and Supervisory Control and Data Acquisition (SCADA) systems. To compare the impact of PMU placements, we introduce a useful metric which accounts for three important requirements in power system state estimation: {\it convergence}, {\it observability} and {\it performance} (COP). Our COP metric can be used to evaluate the estimation performance and numerical stability of the state estimator, which is later used to optimize the PMU locations. In particular, we cast the optimal placement problem in a unified formulation as a semi-definite program (SDP) with integer variables and constraints that guarantee observability in case of measurements loss. Last but not least, we propose a relaxation scheme of the original integer-constrained SDP with randomization techniques, which closely approximates the optimum deployment. Simulations of the IEEE-30 and 118 systems corroborate our analysis, showing that the proposed scheme improves the convergence of the state estimator, while maintaining optimal asymptotic performance.Comment: accepted to IEEE Trans. on Power System

Optimal Placement of Phasor Measurement Units for Power Systems Using Genetic Algorithm

Power grids require monitoring to operate with high efficiency while minimizing the chances of having a failure. However, current monitoring scheme which consists of SCADA (Supervisory Control and Data Acquisition), accompanied with conventional meters distributed throughout the grid, is no longer sufficient to maintain an acceptable operation of the grid. This is evident from the multiple failures and blackouts that happened and are still happening in grids worldwide. This issue became more severe due to systems being operated near their limits (to reduce costs and due to the increase in electricity demands), as well as, the addition of renewable energy sources, which usually have abrupt changes. Smart grids were introduced as a solution to this issue by the inclusion of Wide Area Monitoring System (WAMS), which is mainly based on Phasor Measurement Units (PMU), which are measurement devices that provides synchronized time stamped measurements with high sending rate which significantly improves the monitoring of the grid. However, PMUs are relatively expensive (considering both direct and indirect costs incurred). Thus, it is desired to know the minimum number of PMUs required for achieving certain monitoring criteria. Thus, Optimal PMU Placement (OPP) formulates an optimization problem to solve this issue. In the literature of OPP, multiple objectives and constraints are considered, based on desired criteria. In this thesis, a review of OPP is made, followed by the application of selected algorithms (Integer Linear Programming and Genetic Algorithm) on various test systems as a verification and then applying it to Qatar Grid, to compare between different considerations as well as gain insight about the possible PMU placements for Qatar Grid. The contribution of this thesis is introducing a modified fitness function for the Genetic Algorithm that provides more diverse results than previous papers, while incorporating for various considerations like Zero Injection Buses, Conventional Measurements and current branch limit. It also analyzes the results of current branch limit and provides new plots describing their effects

Generalized optimal placement of PMUs considering power system observability, communication infrastructure, and quality of service requirements

This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems

Power System State Estimation and Renewable Energy Optimization in Smart Grids

The future smart grid will benefit from real-time monitoring, automated outage management, increased renewable energy penetration, and enhanced consumer involvement. Among the many research areas related to smart grids, this dissertation will focus on two important topics: power system state estimation using phasor measurement units (PMUs), and optimization for renewable energy integration. In the first topic, we consider power system state estimation using PMUs, when phase angle mismatch exists in the measurements. In particular, we build a measurement model that takes into account the measurement phase angle mismatch. We then propose algorithms to increase state estimation accuracy by taking into account the phase angle mismatch. Based on the proposed measurement model, we derive the posterior Cramér-Rao bound on the estimation error, and propose a method for PMU placement in the grid. Using numerical examples, we show that by considering the phase angle mismatch in the measurements, the estimation accuracy can be significantly improved compared with the traditional weighted least-squares estimator or Kalman filtering. We also show that using the proposed PMU placement strategy can increase the estimation accuracy by placing a limited number of PMUs in proper locations. In the second topic, we consider optimization for renewable energy integration in smart grids. We first consider a scenario where individual energy users own on-site renewable generators, and can both purchase and sell electricity to the main grid. Under this setup, we develop a method for parallel load scheduling of different energy users, with the goal of reducing the overall cost to energy users as well as to energy providers. The goal is achieved by finding the optimal load schedule of each individual energy user in a parallel distributed manner, to flatten the overall load of all the energy users. We then consider the case of a micro-grid, or an isolated grid, with a large penetration of renewable energy. In this case, we jointly optimize the energy storage and renewable generator capacity, in order to ensure an uninterrupted power supply with minimum costs. To handle the large dimensionality of the problem due to large historical datasets used, we reformulate the original optimization problem as a consensus problem, and use the alternating direction method of multipliers to solve for the optimal solution in a distributed manner