Distributed flexibility to maintain security margin through decentralised TSO–DSO coordination

Nikkhah, Saman; Rabiee, Abbas; Soroudi, Alireza; Allahham, Adib; Taylor, Philip; Giaouris, Damian

doi:10.1016/j.ijepes.2022.108735

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…In order to enable more adaptive and clever control decisions, deep learning models with the ability to comprehend intricate patterns and relationships from historical data include Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs) [11]. Hybrid control approaches can potentially improve machine learning systems' efficiency, stability, and resilience by fusing the interpretability of rule-based systems with the learning power of deep neural networks [12,13].…”

Section: Of 23mentioning

confidence: 99%

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Akbulut,

Cavus,

Cengiz

et al. 2024

Preprint

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Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

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Section: Of 23mentioning

confidence: 99%

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Akbulut,

Cavus,

Cengiz

et al. 2024

Preprint

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“…Distribution network reconfiguration is well known in power systems research as a tool for improving system security and optimising its operation, e.g., for loss reduction and voltage control [12], [13]. But, historically speaking, active network reconfiguration and meshed operation have not been economically attractive at the lowest voltage levels of the distribution networks, being typically considered only during emergency conditions [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Distribution Network Reconfiguration on Aggregated DER Flexibility

Churkin¹,

Sanchez-Lopez²,

Alizadeh³

et al. 2023

Preprint

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The ongoing integration of controllable distributed energy resources (DER) makes distribution networks capable of aggregating flexible power and providing flexibility services at both transmission and distribution levels. The aggregated flexibility of an active distribution network (ADN) can be represented as its feasible operating area in the P-Q space. The limits of this area are pivotal for arranging flexibility markets and coordinating transmission and distribution system operators (TSOs and DSOs). However, motivated by the current technical limitations of distribution networks (e.g., protection schemes), existing literature on ADN flexibility and TSO-DSO coordination mostly focuses on radial networks, overlooking the potential benefits of network reconfiguration. This paper, using a realistic meshed distribution system from the UK and the exact ACOPF model for flexibility estimation, demonstrates that network reconfiguration can increase the limits of ADN aggregated flexibility and improve the economic efficiency of flexibility markets.

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“…Distributed System Operators (DSOs) are tasked with not only delivering power but also ensuring that power quality remains within acceptable standards [5,6]. Voltage disturbances, often prompted by overloading from new connections, pose a significant risk; if unaddressed by DSOs, these can trigger unwarranted local blackouts due to the activation of protective devices within the distribution network [7,8].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of a Renewable Energy-Based Smart Grid for Ma'an City, Jordan: A Feasibility Study

Alzgool,

Khalaf,

Nasan

et al. 2023

IJEPM

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The escalating costs, transmission losses, and environmental ramifications associated with fossil fuel utilization have catalyzed a paradigm shift towards Renewable Energy Sources (RES) in electricity generation. Smart Grid (SG) technologies, which are inherently reliant on a RES-exclusive electricity framework, facilitate efficient energy consumption and the distribution of decentralized energy resources. This investigation underscores the integration of RES within SG infrastructure and the potential for Jordan's transition towards an SG-enabled future. Situated in a locale characterized by abundant solar irradiance and significant wind velocities, Ma'an city presents an optimal case study for RES deployment. An amalgamated RES system, comprising wind and photovoltaic (PV) modules with an aggregate capacity of 180 MW, has been meticulously sized and designed to cater to the electrical demand of Ma'an. The load requirements for Ma'an were determined through an analysis of the city's average annual energy consumption, adjusted for population growth projections. To bolster the system's reliability and cater to emergency load demands, a storage solution has been integrated. The performance of the proposed design was substantiated and assessed via mathematical modeling and simulation analysis, utilizing the MATLAB Simulink platform. The simulations were conducted accounting for factors impinging upon each system's production capacity, inclusive of transmission line losses. Moreover, a Proportional-Integral-Derivative (PID) controller was incorporated and evaluated under simulated fault conditions, ensuring system disconnection within a five-second window subsequent to fault detection. The simulation outcomes exhibited congruence with the mathematical model predictions. Economically, the installation of the proposed systems is justifiable, with projected savings of approximately 80 million Jordanian Dinars (JD) annually and a favorable payback period of 14 months. The levelized cost of electricity is competitively priced at 14.41 JD/MWh. The findings advocate for the expansion of RES integration across Jordan, suggesting the feasibility of a nationwide RES-based SG implementation.

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Distributed flexibility to maintain security margin through decentralised TSO–DSO coordination

Cited by 9 publications

References 27 publications

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Impacts of Distribution Network Reconfiguration on Aggregated DER Flexibility

Design and Simulation of a Renewable Energy-Based Smart Grid for Ma'an City, Jordan: A Feasibility Study

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