3 research outputs found
Effective demand response gathering and deployment in smart grids for intensive renewable integration using aggregation and machine learning
Tesis por compendio de publicaciones.[EN] Distributed generation, namely renewables-based technologies, have
emerged as a crucial component in the transition to mitigate the effects of climate
change, providing a decentralized approach to electricity production. However,
the volatile behavior of distributed generation has created new challenges in
maintaining system balance and reliability. In this context, the demand response
concept and corresponding programs arise giving the local energy communities
prominence.
In demand response concept, it is expected an empowerment of the
consumer in the electricity sector. This has a significant impact on grid operations
and brings complex interactions due to the volatile behavior, privacy concerns,
and lack of consumer knowledge in the energy market context. For this,
aggregators play a crucial role addressing these challenges. It is crucial to develop
tools that allow the aggregators helping consumers to make informed decisions,
maximize the benefits of their flexibility resources, and contribute to the overall
success of grid operations. This thesis, through innovative solutions and
resorting to artificial intelligence models, addresses the integration of
renewables, promoting fair participation among all demand response providers.
The thesis ultimately results in an innovative decision support system -
MAESTRO, the Machine learning Assisted Energy System management Tool for
Renewable integration using demand respOnse. MAESTRO is composed by a set
of diversified models that together contribute for handling the complexity of
managing energy communities with distributed generation resources, demand
response providers, energy storage systems and electric vehicles.
This PhD thesis comprises a comprehensive analysis of state-of-the-art
techniques, system design and development, experimental results, and key
findings. In this research were published twenty-six scientific papers, in both
international journals and conference proceedings. Contributions to international
projects and Portuguese projects was accomplished.
[ES] La generación distribuida, en particular las tecnologías basadas en energías
renovables, se ha convertido en un componente crucial en la transición para
mitigar los efectos del cambio climático, al proporcionar un enfoque
descentralizado para la producción de electricidad. Sin embargo, el
comportamiento volátil de la generación distribuida ha generado nuevos
desafíos para mantener el equilibrio y la confiabilidad del sistema. En este
contexto, surge el concepto de respuesta de la demanda y los programas
correspondientes, otorgando prominencia a las comunidades energéticas locales.
En el concepto de "respuesta a la demanda" (DR por sus siglas en inglés), se
espera un empoderamiento del consumidor en el sector eléctrico. Esto tiene un
impacto significativo en la operación de la red y genera interacciones complejas
debido al comportamiento volátil, las preocupaciones de privacidad y la falta de
conocimiento del consumidor en el contexto del mercado energético. Para esto,
los agregadores desempeñan un papel crucial al abordar estos desafíos. Es
fundamental desarrollar herramientas que permitan a los agregadores ayudar a
los consumidores a tomar decisiones informadas, maximizar los beneficios de sus
recursos de flexibilidad y contribuir al éxito general de las operaciones de la red.
Esta tesis, a través de soluciones innovadoras y utilizando modelos de
inteligencia artificial, aborda la integración de energías renovables, promoviendo
una participación justa entre todos los proveedores de respuesta de la demanda.
La tesis resulta en última instancia en un sistema de apoyo a la toma de decisiones
innovador: MAESTRO, Machine learning Assisted Energy System management Tool
for Renewable integration using demand respOnse. MAESTRO está compuesto por
un conjunto de modelos diversificados que contribuyen juntos para manejar la
complejidad de la gestión de comunidades energéticas con recursos de
generación distribuida, proveedores de respuesta de la demanda, sistemas de
almacenamiento de energía y vehículos eléctricos.
Esta tesis de doctorado comprende un análisis exhaustivo de las técnicas de
vanguardia, el diseño y desarrollo del sistema, los resultados experimentales y
los hallazgos clave. En esta investigación se publicaron veintiséis artículos
científicos, tanto en revistas internacionales como en actas de conferencias. Se
lograron contribuciones a proyectos internacionales y proyectos portugueses.
[POR] A produção distribuída, nomeadamente as tecnologias baseadas em
energias renováveis, emergiram como um componente crucial na transição para
mitigar os efeitos das alterações climáticas, proporcionando uma abordagem
descentralizada à produção de eletricidade. No entanto, o comportamento volátil
da geração distribuída criou desafios na manutenção do equilíbrio e da
fiabilidade do sistema. Nesse contexto, surge o conceito de resposta à procura e
os programas correspondentes, conferindo proeminência às comunidades
energéticas locais.
No conceito de resposta à procura, espera-se um empoderamento do
consumidor no setor elétrico. Isso tem um impacto significativo nas operações da
rede e gera interações complexas devido ao comportamento volátil,
preocupações com a privacidade e falta de conhecimento dos consumidores no
contexto do mercado energético. Para isso, os agregadores desempenham um
papel crucial ao lidar com esses desafios. É fundamental desenvolver ferramentas
que permitam aos agregadores ajudar os consumidores a tomar decisões
informadas, maximizar os benefícios de seus recursos de flexibilidade e
contribuir para o sucesso global das operações da rede.
Esta tese de doutoramento, através de soluções inovadoras e recorrendo a
modelos de inteligência artificial, aborda a integração de energias renováveis,
promovendo uma participação justa entre todos os fornecedores de resposta à
procura. A tese resulta, em última instância, num sistema inovador de apoio à
tomada de decisões - MAESTRO, Machine learning Assisted Energy System
management Tool for Renewable integration using demand respOnse. A ferramenta
MAESTRO é composta por um conjunto de modelos diversificados que, em
conjunto, contribuem para lidar com a complexidade da gestão de comunidades
energéticas com recursos de geração distribuída, fornecedores de resposta à
procura, sistemas de armazenamento de energia e veículos elétricos.
Esta tese de doutoramento abrange uma análise abrangente de técnicas de
ponta, design e desenvolvimento do sistema, resultados experimentais e
descobertas-chave. Nesta pesquisa, foram publicados vinte e seis artigos
científicos, tanto em revistas internacionais como em atas de conferências. Foram
realizadas contribuições para projetos internacionais e projetos portugueses
Model based forecasting for demand response strategies
The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed energy resources, such as solar and wind, calls for a more active management of the distribution grid. Conventional distribution grids were passive systems, in which the power was flowing unidirectionally from upstream to downstream. Nowadays, and increasingly in the future, the penetration of distributed generation (DG), with its stochastic nature and lack of controllability, represents a major challenge for the stability of the network, especially at the distribution level. In particular, the power flow reversals produced by DG cause voltage excursions, which must be compensated. This poses an obstacle to the energy transition towards a more sustainable energy mix, which can however be mitigated by using a more active approach towards the control of the distribution networks.
Demand side management (DSM) offers a possible solution to the problem, allowing to actively control the balance between generation, consumption and storage, close to the point of generation. An active energy management implies not only the capability to react promptly in case of disturbances, but also to ability to anticipate future events and take control actions accordingly. This is usually achieved through model predictive control (MPC), which requires a prediction of the future disturbances acting on the system.
This thesis treat challenges of distributed DSM, with a particular focus on the case of a high penetration of PV power plants. The first subject of the thesis is the evaluation of the performance of models for forecasting and control with low computational requirements, of distributed electrical batteries. The proposed methods are compared by means of closed loop deterministic and stochastic MPC performance.
The second subject of the thesis is the development of model based forecasting for PV power plants, and methods to estimate these models without the use of dedicated sensors. The third subject of the thesis concerns strategies for increasing forecasting accuracy when dealing with multiple signals linked by hierarchical relations. Hierarchical forecasting methods are introduced and a distributed algorithm for reconciling base forecasters is presented. At the same time, a new methodology for generating aggregate consistent probabilistic forecasts is proposed. This method can be applied to distributed stochastic DSM, in the presence of high penetration of rooftop installed PV systems. In this case, the forecasts' errors become mutually dependent, raising difficulties in the control problem due to the nontrivial summation of dependent random variables. The benefits of considering dependent forecasting errors over considering them as independent and uncorrelated, are investigated.
The last part of the thesis concerns models for distributed energy markets, relying on hierarchical aggregators. To be effective, DSM requires a considerable amount of flexible load and storage to be controllable. This generates the need to be able to pool and coordinate several units, in order to reach a critical mass. In a real case scenario, flexible units will have different owners, who will have different and possibly conflicting interests. In order to recruit as much flexibility as possible, it is therefore importan