A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks

Sahu, Abhijeet; Utkarsh, Kumar; Ding, Fei

doi:10.1109/pesgm48719.2022.9916797

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The configuration of these resilient networks must be dynamically optimized [6] by guaranteeing a reliability of service taking into account changes in the capacity of sources or in consumer demands but also breakdowns or degradations due for example to weather conditions [9,11]. This dynamic configuration of the distribution network is essential for the current development of smartgrids or microgrids [20] implementing collective self-consumption grids interconnecting on a local territory a set of prosumers (i.e. consumers able to produce, store and therefore share energy) [2,7,10].…”

Section: Introductionmentioning

confidence: 99%

“…Configuration problems with reliability and resilience objectives are often considered from a graph theory perspective [1,19]. Thus, some solutions seek balanced configurations in terms of load power [21] and other ones configure the network in disjoint balanced subnetworks [12,16,20,22], in particular in the form of spanning trees or sub-DAGS [17].…”

Section: Introductionmentioning

confidence: 99%

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Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Barth,

Mautor,

Watel

et al. 2023

J Glob Optim

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We address the problem of configuring a power distribution network with reliability and resilience objectives by satisfying the demands of the consumers and saturating each production source as little as possible. We consider power distribution networks containing source nodes producing electricity, nodes representing electricity consumers and switches between them. Configuring this network consists in deciding the orientation of the links between the nodes of the network. The electric flow is a direct consequence of the chosen configuration and can be computed in polynomial time. It is valid if it satisfies the demand of each consumer and capacity constraints on the network. In such a case, we study the problem of determining a feasible solution that balances the loads of the sources, that is their production rates. We use three metrics to measure the quality of a solution: minimizing the maximum load, maximizing the minimum load and minimizing the difference of the maximum and the minimum loads. This defines optimization problems called respectively min-M, max-m and min-R.In the case where the graph of the network is a tree, it is known that the problem of building a valid configuration is polynomial. We show the three optimization variants have distinct properties regarding the theoretical complexity and the approximability. Particularly, we show that min-M is polynomial, that max-m is NP-Hard but belongs to the class FPTAS and that min-R is NP-Hard, cannot be approximated to within any exponential relative ratio but, for any ε > 0, there exists an algorithm for which the value of the returned solution equals the value of an optimal solution shifted by at most ε.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Barth,

Mautor,

Watel

et al. 2023

J Glob Optim

View full text Add to dashboard Cite

show abstract

Exploring the Intersection of Artificial Intelligence and Microgrids in Developing Economies: A Review of Practical Applications

Bodewes,

de Hoog,

Ratnam

et al. 2024

Curr Sustainable Renewable Energy Rep

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Purpose of Review This paper reviews practical challenges for microgrid electrification projects in low- and middle-income economies, proposing a Social-Technical-Economic-Political (STEP) framework. With our STEP framework, we review recent Artificial Intelligence (AI) methods capable of accelerating microgrid adoption in developing economies. Recent Findings Many authors have employed novel AI methods in microgrid applications including to support energy management systems, fault detection, generation sizing, and load forecasting. Despite these research initiatives, limited works have investigated the specific challenges for developing economies. That is, high-income countries often have high-quality power, reliable wireless communication infrastructure, and greater access to equipment and technical skills. Accordingly, there are numerous opportunities for the adaptation of AI methods to meet the constraints of developing economies. Summary In this paper, we provide a comprehensive review of the electrification challenges in developing economies alongside an assessment of novel AI approaches for microgrid applications. We also identify emerging opportunities for AI research in the context of developing economies and our proposed STEP framework.

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App Deconfliction: Orchestrating Distributed, Multi-Agent, Multi-Objective Operations for Power Systems

et al. 2023

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Advanced distribution systems need to integrate and orchestrate intelligent subsystems and grid-edge devices that are increasing both in number and sophistication while also serving multiple system-level objectives such as resilience, decarbonization, equity, and profitability. A modular platformbased approach to distribution system operations technology enables operators to deploy a tailored set of best-of-breed algorithms and applications. Combined with the parallel deployment and control of intelligent grid-edge and Internet-of-things devices, this creates a complex environment of distributedcontrol environment with applications that spans ownership boundaries. Conflicts can emerge between applications that want to control overlapping sets of device setpoints. We propose a formalized approach to resolving these conflicts that can be applied when integrating new algorithms or developing customized solutions. A Deconfliction Pipeline is inserted between the device-controlling applications and the device protocol converter, which transmits control setpoints from the operations platform to the devices. The Deconfliction Pipeline executes a process that sets up, solves, and acts on a formally defined deconfliction problem. The deconfliction problem can be solved using a combination of rules and heuristics, application engagement, and optimization. We demonstrate how a few of the most basic solution strategies can be used to orchestrate harmonious behavior between a pair of simple applications with conflicting greedy optimization objectives.

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A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks

Cited by 6 publications

References 14 publications

Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Exploring the Intersection of Artificial Intelligence and Microgrids in Developing Economies: A Review of Practical Applications

App Deconfliction: Orchestrating Distributed, Multi-Agent, Multi-Objective Operations for Power Systems

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