Energy Management and Smart Charging of PEVs in Isolated Microgrids

Microgrids are defined as a cluster of loads and micro-resources operating as a single controllable entity that provides both power and heat to its local area. Typically, these rely on conventional diesel generators, but with recent developments are expected to include more renewable energy sources (RESs), battery energy storage systems (BESSs), and plug-in electric vehicles (PEVs). Both RESs, such as wind and solar, and PEVs can reduce greenhouse gas (GHG) emissions significantly such as carbon dioxide (CO_2) which are released from burning fuel by generators or conventional vehicles. Energy management in isolated microgrids is an important task since these have limited generation capacity and are expected to rely on various uncontrollable resources to match and balance the demand-supply gap. Moreover, PEVs present a promising solution to GHG emissions but on the other hand, their increased penetration can impact power system operation, particularly so in isolated microgrids. Therefore, PEV load management is considered to be a crucial issue. Similarly, demand response (DR) has the potential to provide significant flexibility in operation of isolated microgrids with limited generation capacity, by altering the demand and introducing an elasticity effect. The present research work examines the impact of uncontrolled and controlled (smart) charging of PEVs using a comprehensive mathematical optimization model for short-term operation of the isolated microgrid. This model determines optimal energy management solutions combining generation from different resources such as diesel generators, wind turbines, solar panels, and BESSs, and incorporates the DR options as well. Furthermore, the thesis presents a stochastic optimization model after creating several probabilistic operational scenarios for energy management and smart charging of PEVs in short-term operation of the isolated microgrid considering fixed and optimal DR options. The proposed stochastic optimization model studies the impact of wind and solar generation output variability as well as the effect of uncertain energy consumption patterns of customers; and also the stochastic nature of the state of charge (SOC) of the PEV battery at the start of charging. Several case and scenario studies considering a modified CIGRE isolated microgrid benchmark test system, and using the proposed models are presented and evaluated, to obtain insights into the effect of smart charging vis-`a-vis uncoordinated charging accompanied by DR options in overall energy management of the isolated microgrid.4 month

Hourly Dispatching Wind-Solar Hybrid Power System with Battery-Supercapacitor Hybrid Energy Storage

This dissertation demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a specific dispatching horizon for an entire day utilizing a hybrid energy storage system (HESS) configured by batteries and supercapacitors. Here, wind speed and solar irradiance are predicted one hour ahead of time using a multilayer perceptron Artificial Neural Network (ANN), which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, multiple state of charge (SOC) controllers as a function of energy storage system (ESS) SOC are developed to accurately estimate the grid reference power (PGrid,ref) for each dispatching period. A low pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC), and the cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations. Besides, the optimum value of depth of discharge for ESS considering both cycling and calendar expenses has been investigated to optimize the life cycle cost of the ESS, which is vital for minimizing the cost of a dispatchable wind-solar power scheme. Finally, the proposed ESS control algorithm is verified by conducting control hardware-in-the loop (CHIL) experiments in a real-time digital simulator (RTDS) platform

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles

Energy management strategies based on fuzzy logic control for grid-tied domestic electro-thermal microgrid

The environmental and economic benefits related to the reduction of both carbon dioxide emission and transmission losses have made distributed renewable generation systems became a competitive solution for future power systems. In this context, Microgrids (MG) are considered as the key building blocks of smart grids and have aroused great attention in the last decade for their potential and the impact they may have in the coming future. The MG concept has captured great attention in the last years since it can be considered one of the most suitable alternatives for integration of distributed generation units in the utility grid. However, this integration involves some challenges to deal with especially when penetration of Renewable Energy Sources (RES) into the distribution network is increased. Therefore, an effective Energy Management System (EMS) is required to ensure optimal energy utilization within the MG, consequently, facilitating both the grid integration and operator control. In this regard, the EMS strategy design depends on the application, MG power architecture, and the power management capability of the MG elements. This dissertation research focuses on the design of different EMS strategies based on Fuzzy Logic Control (FLC) for a residential grid-connected electro-thermal MG including renewable power generation (i.e. photovoltaic and wind turbine generators) and storage capability (i.e. battery bank and water storage tank). The main goal of the FLC-based EMS strategies is to minimize the grid power fluctuations while keeping the battery State-of-Charge (SOC) within secure limits. In order to accomplish this goal, the controller design parameters, such as membership functions and rule-base, of the FLC-based EMS strategies, are adjusted to optimize a pre-defined set of quality criteria of the MG behavior. The analysis and design of the FLC-based EMS strategies for electrical and electro-thermal MG power architectures are developed considering two different scenarios. A first scenario where the MG power forecasting is not provided and a second scenario where the forecast of generation power and load demand are considered. A comparison with the different EMS strategies is presented in simulation level, whereas the features of the enhanced FLC-based EMS strategies are experimentally tested on a real residential microgrid implemented at the Public University of Navarre (UPNa)Este estudio presenta el diseño de diferentes estrategias de gestión energética basadas en un controlador difuso para una microrred electro-térmica residencial conectada a la red eléctrica compuesta por generadores de energía renovable (solar y eólico) y elementos de almacenamiento de energía (banco de baterías y tanque de almacenamiento de agua). El objetivo principal de las estrategias de gestión es reducir los picos y fluctuaciones de potencia en el perfil de potencia intercambiado con la red eléctrica y preservar la vida útil del sistema de almacenamiento. Se presenta una revisión del estado del arte de estudios anteriores que buscan este objetivo. Se muestra el análisis de dos arquitecturas de microrred. La primera arquitectura consiste en una microrred eléctrica compuesta fuentes de energía renovables, sistema de almacenamiento de energía y el consumo eléctrico de una vivienda. La segunda arquitectura consiste en una microrred electro-térmica que contiene los elementos de la microrred eléctrica e incluye adicionalmente generadores térmicos y el consumo térmico de la vivienda. Con el objetivo de medir la eficiencia de las diferentes estrategias de gestión, se presenta un conjunto de criterios de evaluación que analizan la calidad del perfil de potencia intercambiado con la red eléctrica obtenido mediante las diferentes estrategias de gestión energética. Estos criterios de calidad son utilizados adicionalmente para la optimización de parámetros de los controladores difusos, lo cual se realiza mediante un proceso de aprendizaje fuera de línea que considera los datos históricos del comportamiento de la microrred. La comparación entre las diferentes estrategias de gestión energética se realiza mediante simulación, utilizando los datos reales de generación y consumo adquiridos en la Universidad Pública de Navarra durante el período comprendido entre Julio 2013 y Julio 2014. El diseño de las estrategias de gestión energética para la arquitectura de microrred eléctrica supone dos posibles escenarios, el primer escenario no considera la previsión de consumo y generación de la microrred, y el segundo escenario si considera esta previsión. Las prestaciones de las estrategias basadas en control difuso para cada uno de estos escenarios son validadas experimentalmente en condiciones reales en la microrred de la Universidad Pública de Navarra. Finalmente, se presenta el análisis de las estrategias de gestión basadas en control difuso empleadas a la arquitectura de microrred electro-térmica. La comparación, mediante simulación, con otras estrategias de gestión aplicadas a la misma arquitectura ha demostrado el correcto desempeño de las estrategias desarrolladas basadas en control difuso.Postprint (published version

The Modeling and Advanced Controller Design of Wind, PV and Battery Inverters

Renewable energies such as wind power and solar energy have become alternatives to fossil energy due to the improved energy security and sustainability. This trend leads to the rapid growth of wind and Photovoltaic (PV) farm installations worldwide. Power electronic equipments are commonly employed to interface the renewable energy generation with the grid. The intermittent nature of renewable and the large scale utilization of power electronic devices bring forth numerous challenges to system operation and design. Methods for studying and improving the operation of the interconnection of renewable energy such as wind and PV are proposed in this Ph.D. dissertation.;A multi-objective controller including is proposed for PV inverter to perform voltage flicker suppression, harmonic reduction and unbalance compensation. A novel supervisory control scheme is designed to coordinate PV and battery inverters to provide high quality power to the grid. This proposed control scheme provides a comprehensive solution to both active and reactive power issues caused by the intermittency of PV energy. A novel real-time experimental method for connecting physical PV panel and battery storage is proposed, and the proposed coordinated controller is tested in a Hardware in the Loop (HIL) experimental platform based on Real Time Digital Simulator (RTDS).;This work also explores the operation and controller design of a microgrid consisting of a direct drive wind generator and a battery storage system. A Model Predictive Control (MPC) strategy for the AC-DC-AC converter of wind system is derived and implemented to capture the maximum wind energy as well as provide desired reactive power. The MPC increases the accuracy of maximum wind energy capture as well as minimizes the power oscillations caused by varying wind speed. An advanced supervisory controller is presented and employed to ensure the power balance while regulating the PCC bus voltage within acceptable range in both grid-connected and islanded operation.;The high variability and uncertainty of renewable energies introduces unexpected fast power variation and hence the operation conditions continuously change in distribution networks. A three-layers advanced optimization and intelligent control algorithm for a microgrid with multiple renewable resources is proposed. A Dual Heuristic Programming (DHP) based system control layer is used to ensure the dynamic reliability and voltage stability of the entire microgrid as the system operation condition changes. A local layer maximizes the capability of the Photovoltaic (PV), wind power generators and battery systems, and a Model Predictive Control (MPC) based device layer increases the tracking accuracy of the converter control. The detail design of the proposed SWAPSC scheme are presented and tested on an IEEE 13 node feeder with a PV farm, a wind farm and two battery-based energy storage systems

Optimization of Islanded Microgrid Operation

Presently a lot of effort is being deployed in the area of microgrid development. In this aspect, the work presented here is in the direction of developing and coordinating various operational modules in an isolated microgrid system. The work presented in this report looks at the prospects of incorporating a consumer side load-scheduling algorithm that works in conjunction with the unit commitment and economic load dispatch. The unit commitment and economic load dispatch are run a day in advance to determine generator outputs for the following day. From the microgrid operator point of view, the load side scheduling helps reduce the stress on the system especially during peak hours thereby ensuring system stability and security. From the consumers’ point of view, the dynamic electricity prices within a day, which are a reflection of this time varying stress on the system, encourage them to endorse such a scheme and reduce their bills incurred. Owing to unpredictable weather conditions, running unit commitment and economic load dispatch in advance does not guarantee planned real-time generation in the microgrid scenario. Such variability in forecasted generation must be handled in any microgrid, while accounting for load demand uncertainties. To address this issue a load side energy management system and power balance scheme is proposed in this paper. The objective is to ascertain uninterrupted power to critical loads while managing other non-critical loads based on their priorities