2,942 research outputs found
Optimal operation of hybrid AC/DC microgrids under uncertainty of renewable energy resources : A comprehensive review
The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources’ generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy resources are reviewed.fi=vertaisarvioitu|en=peerReviewed
Planning of power distribution systems with high penetration of renewable energy sources using stochastic optimization
Driven by techno-economic and environmental factors, there is a global drive to integrate more distributed energy resources in power systems, particularly at the distribution level. These typically include smart-grid enabling technologies, such as distributed generation (DG), energy storage systems and demand-side management.
Especially, the scale of DG sources (mainly renewables) integrated in many distribution networks is steadily increasing. This trend is more likely to continue in the years to come due to the advent of emerging solutions, which are expected to alleviate existing technical limitations and facilitate smooth integration of DGs. The favorable agreements of countries to limit greenhouse gas (GHG) emissions and mitigate climate change are also expected to accelerate the integration of renewable energy sources (RESs).
However, the intermittent and volatile nature of most of these RESs (particularly, wind and solar) makes their integration in distribution networks a more challenging task. This is because such resources introduce significant operational variability and uncertainty to the system. Hence, the development of novel methodologies and innovative computational tools is crucial to realize an optimal and cost-efficient integration of such DGs, minimizing also their side effects.
Novel methodologies and innovative computational tools are developed in this thesis that take into account the operational variability and uncertainty associated with the RES power generation, along with the integration of smart-grid enabling technologies. The developed methodologies and computational tools are tested in real-life power systems, as well as in standard test systems, demonstrating their computational proficiency when compared with the current state-of-the-art. Due to the inherent uncertainty and variability of RESs, stochastic programming is used in this thesis. Moreover, to ensure convergence and to use efficient off-the-shelf solvers, the problems addressed in this thesis are formulated using a mixed integer linear programming (MILP) approach.Atualmente há um esforço global para integrar mais recursos energéticos distribuídos nas redes elétricas, impulsionado por fatores técnico-económicos e ambientais, particularmente ao nível da rede de distribuição. Estes recursos incluem tipicamente tecnologias facilitadoras das redes elétricas inteligentes, tais como geração distribuída, sistemas de armazenamento de energia, e gestão ativa da procura.
A integração de fontes de geração distribuída (energias renováveis, principalmente) está a aumentar progressivamente em muitas redes de distribuição, e é provável que esta tendência continue nos próximos anos devido ao avanço de soluções emergentes, esperando-se assim que as limitações técnicas existentes sejam ultrapassadas e que facilitem a integração progressiva das fontes de geração distribuída. Espera-se também que os acordos feitos pelos países para limitar as emissões de gases de efeito de estufa e para mitigar as alterações climáticas acelerem a integração de fontes de energia renováveis.
No entanto, a natureza intermitente e volátil da maioria das fontes de energia renováveis (em particular, eólica e solar) faz com que a sua integração nas redes de distribuição seja uma tarefa complexa. Isto porque tais recursos introduzem variabilidade operacional e incerteza no sistema. Assim, é essencial o desenvolvimento de novas metodologias e ferramentas computacionais inovadoras para beneficiar uma integração óptima da geração distribuída renovável e minimizar os possíveis efeitos colaterais.
Nesta tese são desenvolvidas novas metodologias e ferramentas computacionais inovadoras que consideram a variabilidade operacional e a incerteza associadas à geração a partir de fontes de energia renováveis, juntamente com a integração de tecnologias facilitadoras das redes elétricas inteligentes. As metodologias e ferramentas computacionais desenvolvidas são testadas em casos de estudo reais, bem como em casos de estudo clássicos, demonstrando a sua proficiência computacional comparativamente ao atual estado-da-arte. Devido à inerente incerteza e variabilidade das fontes de energia renováveis, nesta tese utiliza-se programação estocástica. Ainda, para assegurar a convergência para soluções ótimas, o problema é formulado utilizando programação linear inteira-mista
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
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Distribution Network Operation with High Penetration of Renewable Energy Sources. Joint Active/Reactive Power Procurement: A Market-Based Approach for Operation of Distribution Network
Distributed generators (DGs) are proposed as a possible solution to supply
economic and reliable electricity to customers. It is adapted to overcome the
challenges that are characterized by centralized generation such as
transmission and distribution losses, high cost of fossil fuels and environmental
damage. This work presents the basic principles of integrating renewable DGs
in low voltage distribution networks and particularly focuses on the operation
of DG installations and their impacts on active and reactive power.
In this thesis, a novel technique that applies the stochastic approach for the
operation of distribution networks with considering active network
management (ANM) schemes and demand response (DR) within a joint active
and reactive distribution market environment is proposed. The projected model
is maximized based on social welfare (SW) using market-based joint active
and reactive optimal power flow (OPF). The intermittent behaviour of
renewable sources (such as solar irradiance and wind speed) and the load
demands are modelled through Scenario-Tree technique. The distributed
network frame is recast using mixed-integer linear programming (MILP) that is
solved by using the GAMS software and then the obtained results are being
analysed and discussed. In addition, the impact of wind and solar power
penetration on the active and reactive distribution locational prices (D-LMPs)
within the distribution market environment is explored in terms of the
maximization of SW considering the uncertainty related to solar irradiance,
wind speed and load demands. Finally, a realistic case study (16-bus UK
generic medium voltage distribution system) is used to demonstrate the
effectiveness of the proposed method. Results show that ANM schemes and
DR integration lead to an increase in the social welfare and total dispatched
active and reactive power and consequently decrease in active and reactive
D-LMPs.Ministry of Higher Education and Scientific Research - IraqThe selected author's publications, the published versions of which were attached at the end of the thesis, have been removed due to copyright
New typical power curves generation approach for accurate renewable distributed generation placement in the radial distribution system
This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration
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