8 research outputs found
Transaction-Oriented Dynamic Power Flow Tracing for Distribution Networks – Definition and Implementation in GIS Environment
There is a growing interest from owners of distributed energy resources (DERs) to actively participate in the energy market through peer-to-peer (P2P) energy trading. Many strategies have been proposed to base P2P energy trading on. However, in those schemes neither the costs of assets usage nor the losses incurred are so far taken into account. This article presents a transaction-oriented dynamic power flow tracing (PFT) platform for distribution networks (DNs) implemented in a geographic information system (GIS) environment. It introduces a new transaction model that quantifies the use of the DN, apportions the losses and unlocks a flexible use of the surplus generation enabling that prosumers can adopt simultaneously different mechanisms for participation in energy trading, maximizing renewable energy usage. The platform is also helpful for future distribution system operators (DSOs) to overcome the status invisibility of low voltage (LV) DNs, determine who makes use of the assets, debit the losses on them and explore the effects from new connections. A case study is conducted over the IEEE European LV Test Feeder. The tool provides a clear, intuitive, temporal and spatial assessment of the network operation and the resulting power transactions, including losses share and efficiency of DERs
Cournot oligopoly game-based local energy trading considering renewable energy uncertainty costs
Facilitated by advanced information and communication technologies (ICTs), local energy trading develops rapidly, playing an important role in the energy supply chain. Thus, it is essential to develop local trading models and strategies that can benefit participants, not only stimulating local balancing but also promoting renewable penetration. This paper proposes a new local energy trading decision-making model for suppliers by using the Cournot Oligopoly game, considering the uncertainty costs of renewable energy. Four types of representative energy providers are modelled, traditional thermal generation, wind power, photovoltaic (PV) power, and electricity storage. The revenue of these technologies is extensively formulated according to their operation cost, investment cost, and income from selling energy. The uncertainty cost of renewable generation is integrated into the trading, modelled as a penalty for potential energy shortage that is derived from output probability distribution function (PDF). This trading model is formulated as a non-cooperative Cournot oligopoly game to enable energy suppliers to maximize their profits through local trading considering price. The response of the customer to energy price variations, i.e. demand elasticity, is also included in the model. A unique Nash equilibrium (NE) and optimum strategies are derived by the proposed Optimal-Generation-Plan (OGP) Algorithm. As demonstrated in a typical local market, the proposed approach can effectively model and resolve multiple suppliers’ competition in local energy trading. It can work as a vehicle to facilitate the trading between various generation technologies and customers, realising local balancing and benefiting all market participants with enhanced revenue and reduced energy bills.</p
An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain
In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.publishedVersio
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
In this paper, we propose a novel energy-efficient framework for an electric
vehicle (EV) network using a contract theoretic-based economic model to
maximize the profits of charging stations (CSs) and improve the social welfare
of the network. Specifically, we first introduce CS-based and CS
clustering-based decentralized federated energy learning (DFEL) approaches
which enable the CSs to train their own energy transactions locally to predict
energy demands. In this way, each CS can exchange its learned model with other
CSs to improve prediction accuracy without revealing actual datasets and reduce
communication overhead among the CSs. Based on the energy demand prediction, we
then design a multi-principal one-agent (MPOA) contract-based method. In
particular, we formulate the CSs' utility maximization as a non-collaborative
energy contract problem in which each CS maximizes its utility under common
constraints from the smart grid provider (SGP) and other CSs' contracts. Then,
we prove the existence of an equilibrium contract solution for all the CSs and
develop an iterative algorithm at the SGP to find the equilibrium. Through
simulation results using the dataset of CSs' transactions in Dundee city, the
United Kingdom between 2017 and 2018, we demonstrate that our proposed method
can achieve the energy demand prediction accuracy improvement up to 24.63% and
lessen communication overhead by 96.3% compared with other machine learning
algorithms. Furthermore, our proposed method can outperform non-contract-based
economic models by 35% and 36% in terms of the CSs' utilities and social
welfare of the network, respectively.Comment: 16 pages, submitted to TM
Demand response performance and uncertainty: A systematic literature review
The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio
Data-Intensive Computing in Smart Microgrids
Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area
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
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts
The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above