Autonomous Multi-Stage Flexible OPF for Active Distribution Systems with DERs

The variability of renewable resources creates challenges in the operation and control of power systems. One way to cope with this issue is to use the flexibility of customer resources in addition to utility resources to mitigate this variability. We present an approach that autonomously optimizes the available distributed energy resources (DERs) of the system to optimally balance generation and load and/or levelize the voltage profile. The method uses a dynamic state estimator which is continuously running on the system providing the real-time dynamic model of the system and operating condition. At user selected time intervals, the real-time model and operating condition is used to autonomously assemble a multi-stage optimal power flow in which customer energy resources are represented with their controls, allowing the use of customer flexibility to be part of the solution. Customer DERs may include photovoltaic rooftops with controllable inverters, batteries, thermostatically controlled loads, smart appliances, etc. The paper describes the autonomous formation of the Multi-Stage Flexible Optimal Power Flow and the solution of the problem, and presents sample results

Stability design criteria and volt var control for distribution system with single phase solid state transformers

Due to recent advancements in semiconductor technology, power electronic converters for high voltage, high power, and high frequency applications will soon be commercially available. Conventional single phase distribution transformers are replaced by solid state transformers (SST) in a distribution test system to investigate their interactive dynamics. Under certain circumstances, instabilities due to harmonic resonance are observed. A design criterion for solid state transformer during no load conditions has been proposed in order to avoid instability using an impedance-based analysis. Stability assessment is also extended to include the impact of distribution system voltages and system wide impedance analysis. It is shown that if the SST filters throughout the system are designed with regards to the proposed stability criterion, then system stability is guaranteed regardless of configuration. This leads to two resulting applications: (1) the order in which the SSTs are connected to the system will not generate instability if the criterion is satisfied, and (2) a system configuration change due to a fault will not produce instability. In distribution power systems, feeder voltages can be very sensitive to changes in load and/or distributed generation. A solid-state-transformer-based local voltage control strategy is introduced to reduce variability distribution system bus voltages. An on-line dynamic volt-var control (VVC) algorithm is proposed to regulate bus voltages by injecting or absorbing reactive power through a solid state transformer. The main goal of the algorithm is to enforce strict voltage constraints on the system voltages. The proposed control algorithm is validated in both a radial and meshed distribution system. --Abstract, page iii

Solid state transformer technologies and applications: a bibliographical survey

This paper presents a bibliographical survey of the work carried out to date on the solid state transformer (SST). The paper provides a list of references that cover most work related to this device and a short discussion about several aspects. The sections of the paper are respectively dedicated to summarize configurations and control strategies for each SST stage, the work carried out for optimizing the design of high-frequency transformers that could adequately work in the isolation stage of a SST, the efficiency of this device, the various modelling approaches and simulation tools used to analyze the performance of a SST (working a component of a microgrid, a distribution system or just in a standalone scenario), and the potential applications that this device is offering as a component of a power grid, a smart house, or a traction system.Peer ReviewedPostprint (published version

Photovoltaic Inverter Control to Sustain High Quality of Service

The increasing penetration of distributed solar photovoltaic (PV) generation presents both challenges and opportunities for distribution systems. The intermittent nature of solar irradiance may lead to power quality degradation. At the same time, PV inverters –if properly designed and operated– can be used to improve power quality. The goal of this dissertation is to develop power flow optimization methods for power distribution networks with high penetration of PV generation. The approaches proposed in this dissertation have been tested using the modified version of the IEEE 34-node distribution system and the IEEE 123-node distribution system, as well as the validated model of a section of the distribution grid in Walterboro, SC. We first focus on the probabilistic assessment of PV penetration in distribution networks. A stochastic approach based on kernel density estimation is proposed to identify the optimal location for the PV plant installation so that the voltage deviations and network losses are minimized. In the second part, we develop a two-stage hierarchical structure to seek the optimal solutions of a fully centralized optimal power flow (OPF) problem. In the first stage, the OPF problem is formulated as a day-ahead optimal scheduling problem with both continuous and discrete design variables. The direct search algorithms are applied to solve this mixed-integer nonlinear programming (MINLP) problem. In the second stage, to compensate for the uncertainties of the PV output and load demand, a real-time PV inverter reactive power control scheme is proposed and tested using a Hardware-In-theLoop (HIL) approach. Due to the limited availability of real-time measurement devices in distribution systems, an artificial neural network (ANN) approach is used to estimate the operating states of distribution systems. In the final part, we present a decentralized state estimation approach to support real-time decentralized Volt/Var optimization. The network is divided into sub-areas according to the location of measurement devices and the mutual information (MI) between the states of interest and the available measurements. In each sub-area, an artificial neural network (ANN) is used to estimate the loads consumption, and each estimator only relies on local information and on a limited amount of information from neighboring areas. A minimum redundancy-maximum relevance (mRMR) feature selection method is utilized for choosing the optimal subset of the input variables. The presented approach also has been validated using an HIL approach