Power System State Estimation and Renewable Energy Optimization in Smart Grids.

摘要

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 Cramer-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.