Energy storage systems and grid code requirements for large-scale renewables integration in insular grids

This thesis addresses the topic of energy storage systems supporting increased penetration of renewables in insular systems. An overview of energy storage management, forecasting tools and demand side solutions is carried out, comparing the strategic utilization of storage and other competing strategies. Particular emphasis is given to energy storage systems on islands, as a new contribution to earlier studies, addressing their particular requirements, the most appropriate technologies and existing operating projects throughout the world. Several real-world case studies are presented and discussed in detail. Lead-acid battery design parameters are assessed for energy storage applications on insular grids, comparing different battery models. The wind curtailment mitigation effect by means of energy storage resources is also explored. Grid code requirements for large-scale integration of renewables are discussed in an island context, as another new contribution to earlier studies. The current trends on grid code formulation, towards an improved integration of distributed renewable resources in island systems, are addressed. Finally, modeling and control strategies with energy storage systems are addressed. An innovative energy management technique to be used in the day-ahead scheduling of insular systems with Vanadium Redox Flow battery is presented.Esta tese aborda a temática dos sistemas de armazenamento de energia visando o aumento da penetração de energias renováveis em sistemas insulares. Uma visão geral é apresentada acerca da gestão do armazenamento de energia, ferramentas de previsão e soluções do lado da procura de energia, comparando a utilização estratégica do armazenamento e outras estratégias concorrentes. É dada ênfase aos sistemas de armazenamento de energia em ilhas, como uma nova contribuição no estado da arte, abordando as suas necessidades específicas, as tecnologias mais adequadas e os projetos existentes e em funcionamento a nível mundial. Vários casos de estudos reais são apresentados e discutidos em detalhe. Parâmetros de projeto de baterias de chumbo-ácido são avaliados para aplicações de armazenamento de energia em redes insulares, comparando diferentes modelos de baterias. O efeito de redução do potencial de desperdício de energia do vento, recorrendo ao armazenamento de energia, também é perscrutado. As especificidades subjacentes aos códigos de rede para a integração em larga escala de energias renováveis são discutidas em contexto insular, sendo outra nova contribuição no estado da arte. As tendências atuais na elaboração de códigos de rede, no sentido de uma melhor integração da geração distribuída renovável em sistemas insulares, são abordadas. Finalmente, é estudada a modelação e as estratégias de controlo com sistemas de armazenamento de energia. Uma metodologia de gestão de energia inovadora é apresentada para a exploração de curto prazo de sistemas insulares com baterias de fluxo Vanádio Redox

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response

[EN] A Model Predictive Control (MPC) strategy based on the Evolutionary Algorithms (EA) is proposed for the optimal dispatch of renewable generation units and demand response in a grid-tied hybrid system. The generating system is based on the experimental setup installed in a Distributed Energy Resources Laboratory (LabDER), which includes an AC micro-grid with small scale PV/Wind/Biomass systems. Energy storage is by lead-acid batteries and an H2 system (electrolyzer, H2 cylinders and Fuel Cell). The energy demand is residential in nature, consisting of a base load plus others that can be disconnected or moved to other times of the day within a demand response program. Based on the experimental data from each of the LabDER renewable generation and storage systems, a micro-grid operating model was developed in MATLAB(C) to simulate energy flows and their interaction with the grid. The proposed optimization algorithm seeks the minimum hourly cost of the energy consumed by the demand and the maximum use of renewable resources, using the minimum computational resources. The simulation results of the experimental micro-grid are given with seasonal data and the benefits of using the algorithm are pointed out.Acevedo-Arenas, CY.; Correcher Salvador, A.; Sánchez-Diaz, C.; Ariza-Chacón, HE.; Alfonso-Solar, D.; Vargas-Salgado, C.; Petit-Suarez, JF. (2019). MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response. Energy Conversion and Management. 186:241-257. https://doi.org/10.1016/j.enconman.2019.02.044S24125718

Adaptive model predictive control of renewable energy-based micro-grid.

Doctoral Degree. University of KwaZulu-Natal, Durban.Energy sector is facing a shift from a fossil-fuel energy system to a modern energy system focused on renewable energy and electric transport systems. New control algorithms are required to deal with the intermittent, stochastic, and distributed nature of the generation and with the new patterns of consumption. Firstly, this study proposes an adaptive model-based receding horizon control technique to address the issues associated with the energy management system (EMS) in micro-grid operations. The essential objective of the EMS is to balance power generation and demand through energy storage for optimal operation of the renewable energy-based micro-grid. At each sampling point, the proposed control system compares the expected power produced by the renewable generators with the expected load demand and determines the scheduling of the different energy storage devices and generators for the next few hours. The control technique solves the optimization problem in order to minimize or determines the minimum running cost of the overall micro-grid operations, while satisfying the demand and taking into account technical and physical constraints. Micro-grid, as any other systems are subject to disturbances during their normal operation. Hence, the power generated by the renewable energy sources (RESs) and the demanded power are the main disturbances acting on the micro-grid. As renewable sources are used for the generation, their time-varying nature, their difficulty in predicting, and their lack of ability to manipulate make them a problem for the control system to solve. In view of this, the study investigates the impacts of considering the prediction of disturbances on the performance of the energy management system (EMS) based on the adaptive model predictive control (AMPC) algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. Furthermore, adequate management of loads and electric vehicle (EV) charging can help enhance the micro-grid operation. This study also introduced the concept of demand-side management (DSM), which allows the customers to make decisions regarding their energy consumption and also help to reduce the peak load demand and to reshape the load profile so as to improve the efficiency of the system, environmental impacts, and reduction in the overall operational costs. More so, the intermittent nature of renewable energy and consumer random behavior introduces a stochastic component to the problem of control. Therefore, in order to solve this problem, this study utilizes an AMPC control technique, which provides some robustness to the control of systems with uncertainties. Lastly, the performances of the micro-grids used as a case study are evaluated through simulation modeling, implemented in MATLAB/Simulink environment, and the simulation results show the accuracy and efficiency of the proposed control technique. More so, the results also show how the AMPC can adapt to various generation scenarios, providing an optimal solution to power-sharing among the distributed energy resources (DERs) and taking into consideration both the physical and operational constraints and similarly, the optimization of the imposed operational criteria

Optimal operation of an energy hub considering the uncertainty associated with the power consumption of plug-in hybrid electric vehicles using information gap decision theory

© 2019 Elsevier Ltd An energy hub is a multi-carrier energy system that is capable of coupling various energy networks. It increases the flexibility of energy management and creates opportunities to increase the efficiency and reliability of energy systems. When plug-in hybrid electric vehicles (PHEVs)are incorporated into the energy hub, batteries can act as an aggregated storage system, increasing the potential integration of variable renewable energy sources (RES)into power system networks. This paper presents a new model for the optimal operation of an energy hub that includes RES, PHEVs, fuel cell vehicles, a fuel cell, an electrolyzer, a hydrogen tank, a boiler, an inverter, a rectifier, and a heat storage system. A novel model is developed to estimate the uncertainty associated with the power consumption of PHEVs during trips using information gap decision theory (IGDT)under risk-averse and risk-seeking strategies. Simulation results demonstrate that the proposed method maximizes the objective function under the risk-neutral and risk-averse strategies, while minimizing the objective function under the risk-seeking strategy. Results from the modeling show that considering the uncertainty associated with the power consumption of PHEVs using IGDT enables the energy hub operator to make appropriate decisions when optimizing the operation of the energy hub against possible changes in power consumption of PHEVs

Optimal energy management of a microgrid system

Mestrado de dupla diplomação com École Superieure en Sciences AppliquéesA smart management strategy for the energy ows circulating in microgrids is necessary to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the optimum set-points of the various generators and the best scheduling of the microgrid generators can lead to moderate and judicious use of the powers available in the microgrid. This thesis aims to apply an energy management system based on optimization algorithms to ensure the optimal control of microgrids by taking as main purpose the minimization of the energy costs and reduction of the gas emissions rate responsible for greenhouse gases. Two approaches have been proposed to nd the optimal operating setpoints. The rst one is based on a uni-objective optimization approach in which several energy management systems are implemented for three case studies. This rst approach treats the optimization problem in a uni-objective way where the two functions price and gas emission are treated separately through optimization algorithms. In this approach the used methods are simplex method, particle swarm optimization, genetic algorithm and a hybrid method (LPPSO). The second situation is based on a multiobjective optimization approach that deals with the optimization of the two functions: cost and gas emission simultaneously, the optimization algorithm used for this purpose is Pareto-search. The resulting Pareto optimal points represent di erent scheduling scenarios of the microgrid system.Uma estrat egia de gest~ao inteligente dos uxos de energia que circulam numa microrrede e necess aria para gerir economicamente a produ c~ao e o consumo local, mantendo o equil brio entre a oferta e a procura. Encontrar a melhor programa c~ao dos geradores de microrrede pode levar a uma utiliza c~ao moderada e criteriosa das pot^encias dispon veis na microrrede. Esta tese visa desenvolver um sistema de gest~ao de energia baseado em algoritmos de otimiza c~ao para assegurar o controlo otimo das microrredes, tendo como objetivo principal a minimiza c~ao dos custos energ eticos e a redu c~ao da taxa de emiss~ao de gases respons aveis pelo com efeito de estufa. Foram propostas duas estrat egias para encontrar o escalonamento otimo para funcionamento. A primeira baseia-se numa abordagem de otimiza c~ao uni-objetivo no qual v arios sistemas de gest~ao de energia s~ao implementados para tr^es casos de estudo. Neste caso o problema de otimiza c~ao e baseado na fun c~ao pre co e na fun c~ao emiss~ao de gases. Os m etodos de otimiza c~ao utilizados foram: algoritmo simplex, algoritmos gen eticos, particle swarm optimization e m etodo h brido (LP-PSO). A segunda situa c~ao baseia-se numa abordagem de otimiza c~ao multi-objetivo que trata a otimiza c~ao das duas fun c~oes: custo e emiss~ao de gases em simult^aneo. O algoritmo de otimiza c~ao utilizado para este m foi a Procura de Pareto. Os pontos otimos de Pareto resultantes representam diferentes cen arios de programa c~ao do sistema de microrrede

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world