The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

A Longitudinal study of organizational capability development process : rendering project portfolio management capability (PPMC)

This dissertation analyzes the heterogeneous development paths of project portfolio management capability (PPMC). Earlier, modern literature has prioritized its focus on the performance-based classification of organizational capabilities, while their development process remained obscure. Consequently, scholarship advocating high performance organizational capabilities (such as a dynamic capability) are in abundance. However, the evidence of development path-affected performance dissimilarities is rather sparse or otherwise remained implicit due to the increasing conceptual differences among the prominent scholarship. Along with the longitudinal process research design of this research, a critical realism-based retroduction approach has enabled the discovery of the capability investigation framework. This capability dimensions, routines, and performance outcome based framework has been further extended to investigate project portfolio management capability (PPMC). This retroductive framework is operationalized to evidence the nine years of capability development path heterogeneity at three entities of a case company. The research case findings explain the effect of underlying mechanisms, which due to their context dependent outcomes, either positively reinforce the existing development paths or lead to an alternative path selection. The case findings also confirm that higher performance is not universally attributable to any specific organizational capability known in the literature. Instead, the actuation of all three identified learning mechanisms (of a learning organization) can develop high performing organizational capabilities. This research concludes that a capability development process endures through an extemporized mixture of refinement, reconfiguration, and transformation activities. As a result, an organizational capability always remains idiosyncratic in its details and, hence, produce diverse performance outcomes. Finally, this PhD research has created a critical realist model to extend the emergent theory of capability path dependence to the other organizational contexts.Tämä tutkimus analysoi projektiportfolion hallintaa koskevan kyvykkyyden moninaisia kehittämisvaihtoehtoja. Aiempi tutkimus on keskittynyt organisaation toimintaa tukevien kyvykkyyksien luokitteluun, mutta kyvykkyyksien kehittymistä on tutkittu vähemmän. Kyvykkyyden kehittymiseen (kuten dynaamiseen kyvykkyyteen) tähtäävä tutkimus keskittyy enimmäkseen organisaation näkökulmaan. Lisäksi kyvykkyyden kehittymistutkimusta vaikeuttaa se, että alan keskeiset tutkijat käyttävät keskenään erilaista terminologiaa. Tämä tutkimus on pitkittäinen ja siinä rakennettiin kriittisen realismin lähestymistavan avulla kyvykkyyden kehittymisen tutkimista varten viitekehys. Kyvykkyyden osatekijöitä, rutiineja ja toiminnan tuloksia kuvaavaa viitekehystä kehitettiin edelleen niin, että sitä voidaan käyttää organisaation projektisalkun hallinnan kyvyn selvittämiseen. Tämän viitekehyksen avulla osoitettiin tapausyrityksen kolmen yksikön kyvykkyyden kehittymispolku yhdeksän vuoden ajalta. Tapaustutkimuksen tulokset selittävät kyvykkyyden kehittymisen mekanismeja, jotka joko vahvistavat organisaation vallitsevia kehittymispolkuja tai johtavat uuden kehittymispolun valintaan. Tapaustutkimukset myös osoittavat, että tehokas toiminta ei ole kirjallisuudessa mainitun yksittäisen organisaation kyvykkyysosatekijän seurausta. Sen sijaan kaikki tunnistetut oppivan organisaation oppimiskeinot kehittävät tehokkaasti toimivan organisaation kyvykkyyksiä. Tämän tutkimuksen johtopäätös on, että kyvykkyyden kehittymisprosessi muodostuu improvisoiduista hienosäätö-, uudelleenkonfigurointi- ja muokkausvaiheista. Niiden tuloksena organisaation kyvykkyys säilyy aina yksityiskohdissaan omaperäisenä ja siten voi tuottaa vaihtelevia tuloksia. Tämä väitöskirja on luonut kriittiseen realismiin perustuvan mallin, jolla laajennetaan uutta kyvykkyyden kehittymispolkuriippuvuuden teoriaa muihin organisaatiokonteksteihin.fi=vertaisarvioitu|en=peerReviewed

Autonomous Multi-Chemistry Secondary-Use Battery Energy Storage

Battery energy storage is poised to play an increasingly important role in the modern electric grid. Not only does it provide the ability to change the time-of-day and magnitude of energy produced by renewable resources like wind and solar, it can also provide a host of other 3ancillary grid-stabilizing services. Cost remains a limiting factor in deploying energy storage systems large enough to provide these services on the scale required by an electric utility provider. Secondary-use electric vehicle batteries are a source of inexpensive energy storage materials that are not yet ready for the landfill but cannot operate in vehicles any longer. However, the wide range of manufacturers using different battery chemistries and configurations mean that integrating these batteries into a large-format system can be difficult. This work demonstrates methods for the autonomous integration and operation of a wide range of stationary energy storage battery chemistries. A fully autonomous battery characterization is paired with a novel system architecture and transactive optimization to create a system which can provide utility-scale energy services using a multitude of battery chemistries in the same system. These claims are verified using a combination of in-situ testing and a computer modelling testbed. Results are presented which demonstrate the ability of the system to combine a wide range of batteries into an effective single system

μGIM - Microgrid intelligent management system based on a multi-agent approach and the active participation of end-users

[ES] Los sistemas de potencia y energía están cambiando su paradigma tradicional, de sistemas centralizados a sistemas descentralizados. La aparición de redes inteligentes permite la integración de recursos energéticos descentralizados y promueve la gestión inclusiva que involucra a los usuarios finales, impulsada por la gestión del lado de la demanda, la energía transactiva y la respuesta a la demanda. Garantizar la escalabilidad y la estabilidad del servicio proporcionado por la red, en este nuevo paradigma de redes inteligentes, es más difícil porque no hay una única sala de operaciones centralizada donde se tomen todas las decisiones. Para implementar con éxito redes inteligentes, es necesario combinar esfuerzos entre la ingeniería eléctrica y la ingeniería informática. La ingeniería eléctrica debe garantizar el correcto funcionamiento físico de las redes inteligentes y de sus componentes, estableciendo las bases para un adecuado monitoreo, control, gestión, y métodos de operación. La ingeniería informática desempeña un papel importante al proporcionar los modelos y herramientas computacionales adecuados para administrar y operar la red inteligente y sus partes constituyentes, representando adecuadamente a todos los diferentes actores involucrados. Estos modelos deben considerar los objetivos individuales y comunes de los actores que proporcionan las bases para garantizar interacciones competitivas y cooperativas capaces de satisfacer a los actores individuales, así como cumplir con los requisitos comunes con respecto a la sostenibilidad técnica, ambiental y económica del Sistema. La naturaleza distribuida de las redes inteligentes permite, incentiva y beneficia enormemente la participación activa de los usuarios finales, desde actores grandes hasta actores más pequeños, como los consumidores residenciales. Uno de los principales problemas en la planificación y operación de redes eléctricas es la variación de la demanda de energía, que a menudo se duplica más que durante las horas pico en comparación con la demanda fuera de pico. Tradicionalmente, esta variación dio como resultado la construcción de plantas de generación de energía y grandes inversiones en líneas de red y subestaciones. El uso masivo de fuentes de energía renovables implica mayor volatilidad en lo relativo a la generación, lo que hace que sea más difícil equilibrar el consumo y la generación. La participación de los actores de la red inteligente, habilitada por la energía transactiva y la respuesta a la demanda, puede proporcionar flexibilidad en desde el punto de vista de la demanda, facilitando la operación del sistema y haciendo frente a la creciente participación de las energías renovables. En el ámbito de las redes inteligentes, es posible construir y operar redes más pequeñas, llamadas microrredes. Esas son redes geográficamente limitadas con gestión y operación local. Pueden verse como áreas geográficas restringidas para las cuales la red eléctrica generalmente opera físicamente conectada a la red principal, pero también puede operar en modo isla, lo que proporciona independencia de la red principal. Esta investigación de doctorado, realizada bajo el Programa de Doctorado en Ingeniería Informática de la Universidad de Salamanca, aborda el estudio y el análisis de la gestión de microrredes, considerando la participación activa de los usuarios finales y la gestión energética de lascarga eléctrica y los recursos energéticos de los usuarios finales. En este trabajo de investigación se ha analizado el uso de conceptos de ingeniería informática, particularmente del campo de la inteligencia artificial, para apoyar la gestión de las microrredes, proponiendo un sistema de gestión inteligente de microrredes (μGIM) basado en un enfoque de múltiples agentes y en la participación activa de usuarios. Esta solución se compone de tres sistemas que combinan hardware y software: el emulador de virtual a realidad (V2R), el enchufe inteligente de conciencia ambiental de Internet de las cosas (EnAPlug), y la computadora de placa única para energía basada en el agente (S4E) para permitir la gestión del lado de la demanda y la energía transactiva. Estos sistemas fueron concebidos, desarrollados y probados para permitir la validación de metodologías de gestión de microrredes, es decir, para la participación de los usuarios finales y para la optimización inteligente de los recursos. Este documento presenta todos los principales modelos y resultados obtenidos durante esta investigación de doctorado, con respecto a análisis de vanguardia, concepción de sistemas, desarrollo de sistemas, resultados de experimentación y descubrimientos principales. Los sistemas se han evaluado en escenarios reales, desde laboratorios hasta sitios piloto. En total, se han publicado veinte artículos científicos, de los cuales nueve se han hecho en revistas especializadas. Esta investigación de doctorado realizó contribuciones a dos proyectos H2020 (DOMINOES y DREAM-GO), dos proyectos ITEA (M2MGrids y SPEAR), tres proyectos portugueses (SIMOCE, NetEffiCity y AVIGAE) y un proyecto con financiación en cascada H2020 (Eco-Rural -IoT)

Extending Two-Dimensional Knowledge Management System Theory with Organizational Activity Systems\u27 Workflow Dynamics

Between 2005 and 2010 and across 48 countries, including the United States, an increasing positive correlation emerged between national intellectual capital and gross domestic product per capita. The problem remains organizations operating with increasingly complex knowledge networks often lose intellectual capital resulting from ineffective knowledge management practices. The purpose of this study was to provide management opportunities to reduce intellectual capital loss. The first research question addressed how an enhanced intelligent, complex, and adaptive system (ICAS) model could clarify management\u27s understanding of organizational knowledge transfer. The second research question addressed how interdisciplinary theory could become more meaningfully infused to enhance management practices of the organization\u27s knowledge ecosystem. The nature of this study was phenomenological to gain deeper understanding of individual experiences related to knowledge flow phenomena. Data were collected from a single historical research dataset containing 11 subject interviews and analyzed using Moustakas\u27 heuristic framework. Original interviews were collected in 2012 during research within a military unit, included in this study based on theme alignment. Organizational, knowledge management, emergent systems, and cognition theories were synthesized to enhance understandings of emergent ICAS forces. Individuals create unique ICAS flow emergent force dynamics in relation to micro- and macro-meso sensemaking and sensegiving. Findings indicated individual knowledge work significantly shapes emergent ICAS flow dynamics. Collectively enhancing knowledge stewardship over time could foster positive social change by improving national welfare

Impact of peer-to-peer trading and flexibility on local energy systems

To meet the 2050 net zero emission targets, energy systems around the globe are being revisited to achieve multi-vector decarbonisation in terms of electricity, transport, heating and cooling. As energy systems become more decentralised and digitised, local energy systems will have greater potential to self-sustain and hence, decrease reliance on fossil-fuelled central generation. While the uptake of electric vehicles, heat pumps, solar and battery systems offer a solution, the increase in electricity demand poses challenges in terms of higher peak demand, imbalance and overloading. Additionally, the current energy market structure prevents these assets in the distribution network from reaching their true techno-economic potential in flexibility services and energy trading. Peer-to-peer energy trading and community-level control algorithms achieve better matching of local demand and supply through the use of transactive energy markets, load shifting and peak shaving techniques. Existing research addresses the challenges of local energy markets and others investigate the effect of increased distributed assets on the network. However, the combined techno-economic effect requires the co-simulation of both market and network levels, coupled with simultaneous system balance, cost and carbon intensity considerations. Using bottom-up coordination and user-centric optimisation, this project investigated the potential of network-aware peer-to-peer trading and community-level control to increase self-sufficiency and self-consumption in energy communities. The techno-economic effects of these strategies are modelled while maintaining user comfort levels and healthy operation of the network and assets. The proposed strategies are evaluated according to their economic benefit, environmental impact and network stress. A case study in Scotland was employed to demonstrate the benefits of peer-to-peer trading and community self-consumption using future projections of demand, generation and storage. Additionally, the concept of energy smart contracts, embedded in blockchains, are proposed and demonstrated to overcome the major challenges of monitoring and contracting. The results indicate benefits for various energy systems stakeholders. Distribution system end-users benefit from lower energy costs while system operators obtain better visibility of the local-level flexibility along with the associated technical challenges in terms of losses, imbalance and loading. From a commercial perspective, community energy companies may utilise this study to inform investment decisions regarding storage, distributed generation and transactive market solutions. Additionally, the insights about the energy smart contracts allow blockchain and relevant technology sectors to recognise the opportunities and challenges of smart contracts and distributed ledger technologies that are specific to the energy sector. On the broader scale, energy system operators, regulators and high-level decision-makers can compare the simulated impact of community-led energy transition on the net zero goals with large-scale top-down initiatives