1,444 research outputs found
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
Robust and automatic data cleansing method for short-term load forecasting of distribution feeders
Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting
Power quality and electromagnetic compatibility: special report, session 2
The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems.
Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages).
The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks:
Block 1: Electric and Magnetic Fields, EMC, Earthing systems
Block 2: Harmonics
Block 3: Voltage Variation
Block 4: Power Quality Monitoring
Two Round Tables will be organised:
- Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13)
- Reliability Benchmarking - why we should do it? What should be done in future? (RT 15
Global capacity announcement of electrical distribution systems: A pragmatic approach
We propose a pragmatic procedure to facilitate the connection process of Distributed Generation (DG) with reference to the European regulatory framework where Distribution System Operators (DSOs) are, except in specific cases, not allowed to own their generation. The procedure is termed Global Capacity ANnouncement (GCAN) and is intended to compute the estimates of maximum generation connection amount at appropriate substations in a distribution system, to help generation connection decisions. The pragmatism of the proposed procedure stems from its reliance on the tools that are routinely used in distribution systems planning and operation, and their use such that the possibilities of network sterilization are avoided. The tools involved include: long-term load forecasting, long-term planning of network extension/reinforcement, network reconfiguration, and power flow. Network sterilizing substations are identified through repeated power flow computations. The proposed procedure is supported by results using an artificially created 5-bus test system, the IEEE 33-bus test system, and a part of real-life distribution system of ORES (a Belgian DSO serving a large portion of the Walloon region in Belgium).GREDO
Innovative Smart Grid Solutions for Network Planning and Access
Smart Grids are the cornerstone for Distribution System Operators transformation. Having new solutions to deal with historical and future problems is key to ensure a smooth transition to an advanced power system that not only integrate a large share of renewables and distributed energy resources (e.g. storage, electrical vehicles), but also requires efficient operation, better planning and exceptional customer service. EDP Distribuição is at the forefront of this transformation, as it is developing Inovgrid, a smart grid project in Évora city (Portugal), where a smart grid infrastructure was deployed, and new data is now available to incorporate in planning and access tools and procedures, hence contributing to a Smarter Grid. This paper discusses the results that EDP Distribuição has attained so far in these areas of the smart grid development, as well as the projected evolution of these innovative approaches to the future of the distribution grid, which are being developed in European projects like SuSTAINABLE (www.sustainableproject.eu)
Stochastic management framework of distribution network systems featuring large-scale variable renewable energy sources and flexibility options
The concerns surrounding climate change, energy supply security and the growing demand are
forcing changes in the way distribution network systems are planned and operated, especially
considering the need to accommodate large-scale integration of variable renewable energy
sources (vRESs). An increased level of vRESs creates technical challenges in the system, bringing
a huge concern for distribution system operators who are given the mandate to keep the integrity
and stability of the system, as well as the quality of power delivered to end-users. Hence,
existing electric energy systems need to go through an eminent transformation process so that
current limitations are significantly alleviated or even avoided, leading to the so-called smart
grids paradigm.
For distribution networks, new and emerging flexibility options pertaining to the generation,
demand and network sides need to be deployed for these systems to accommodate large
quantities of variable energy sources, ensuring an optimal operation. Therefore, the
management of different flexibility options needs to be carefully handled, minimizing the sideeffects
such as increasing costs, worsening voltage profile and overall system performance. From
this perspective, it is necessary to understand how a distribution network can be optimally
operated when featuring large-scale vRESs. Because of the variability and uncertainty pertinent
to these technologies, new methodologies and computational tools need to be developed to deal
with the ensuing challenges. To this end, it is necessary to explore emerging and existing
flexibility options that need to be deployed in distribution networks so that the uncertainty and
variability of vRESs are effectively managed, leading to the real-time balancing of demand and
supply.
This thesis presents an extensive analysis of the main technologies that can provide flexibility
to the electric energy systems. Their individual or collective contributions to the optimal
operation of distribution systems featuring large-scale vRESs are thoroughly investigated. This
is accomplished by taking into account the stochastic nature of intermittent power sources and
other sources of uncertainty. In addition, this work encompasses a detailed operational analysis
of distribution systems from the context of creating a sustainable energy future.
The roles of different flexibility options are analyzed in such a way that a major percentage of
load is met by variable RESs, while maintaining the reliability, stability and efficiency of the
system. Therefore, new methodologies and computational tools are developed in a stochastic
programming framework so as to model the inherent variability and uncertainty of wind and
solar power generation. The developed models are of integer-mixed linear programming type,
ensuring tractability and optimality.As mudanças climáticas, a crescente procura por energia e a segurança de abastecimento estão
a modificar a operação e o planeamento das redes de distribuição, especialmente pela
necessidade de integração em larga escala de fontes de energia renováveis. O aumento desses
recursos energéticos sustentáveis gera enormes desafios a nível técnico no sistema, atendendo
a que o operador do sistema de distribuição tem o dever de manter a integridade e a
estabilidade da rede, bem como a qualidade de energia entregue aos consumidores. Portanto,
os sistemas de energia elétrica existentes devem passar por um eminente processo de
transformação para que as limitações atuais sejam devidamente atenuadas ou mesmo evitadas,
esperando-se assim chegar ao paradigma das redes elétricas inteligentes.
Para as redes de distribuição acomodarem fontes variáveis de energia renovável, novas e
emergentes opções de flexibilidade, que dizem respeito à geração, carga e à própria rede,
precisam de ser desenvolvidas e consideradas na operação ótima da rede de distribuição. Assim,
a gestão das opções de flexibilidade deve ser cuidadosamente efetuada para minimizar os
efeitos secundários como o aumento dos custos, agravamento do perfil de tensão e o
desempenho geral do sistema. Desta perspetiva, é necessário entender como uma rede de
distribuição pode operar de forma ótima quando se expõe a uma integração em larga escala de
fontes variáveis de energia renovável. Devido à variabilidade e incerteza associadas a estas
tecnologias, novas metodologias e ferramentas computacionais devem ser desenvolvidas para
lidar com os desafios subsequentes. Desta forma, as opções de flexibilidade existentes e
emergentes devem ser implantadas para gerir a incerteza e variabilidade das fontes de energia
renovável, mantendo o necessário balanço entre carga e geração.
Nesta tese é feita uma análise extensiva das principais tecnologias que podem providenciar
flexibilidade aos sistemas de energia elétrica, e as suas contribuições para a operação ótima
dos sistemas de distribuição, tendo em consideração a natureza estocástica dos recursos
energéticos intermitentes e outras fontes de incerteza. Adicionalmente, este trabalho contém
investigação detalhada sobre como o sistema pode ser otimamente gerido tendo em conta estas
tecnologias de forma a que a uma maior percentagem de carga seja fornecida por fontes
variáveis de energia renovável, mantendo a fiabilidade, estabilidade e eficiência do sistema.
Por esse motivo, novas metodologias e ferramentas computacionais usando programação
estocástica são desenvolvidas para modelizar a variabilidade e incerteza inerente à geração
eólica e solar. A convergência para uma solução ótima é garantida usando programação linear
inteira-mista para formular o problema
- …