Impacts of direct load control on reinforcement costs in distribution networks

Battegay, Archie; Hadjsaïd, Nourédine; Roupioz, Guillaume; Lhote, Fabrice; Chambris, Emrick; Boëda, Didier; Charge, Lysiane

doi:10.1016/j.epsr.2014.09.012

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…Future research will consider the impact of trading markets for renewable obligation certificates. 24 NM [139] The study created a way to measure the benefits of DLC in reducing distribution network reinforcement costs. It assessed the change in electricity demand due to flexible loads, accounting for energy shift and power rebound.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Direct Load Control Technologies in the Smart Grid

Ozkop

2024

IEEE Access

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The purpose of this paper is to review the scientific literature on direct load control (DLC) technology in the context of a smart grid (SG). This study synthesized and compared various articles from the literature with the aim of conducting a comprehensive analysis. This study aims to provide a detailed understanding of the landscape, recent advancements, and the relationships among various aspects within this field. The evaluation is conducted through a systematic literature review, examining recent advancements in the field. A survey of over 200 papers published between 1983 and 2022 is classified based on appliance information, aim/objective, constraint, management method and tool, implementation, and publication status. In the DLC application, the study identifies the type and population of appliances used in and the aim/objective and constraint considered. It also determines whether forecast, prediction, and estimation structures, optimization and control algorithms, and specific structures are employed. The practical aspect of the study examines various perspectives, including the modeling and presentation of the research. This involves identifying the validation methods, time period for implementation, solver, program, software, toolbox, and platform structures used during validation. Additionally, the characteristics of the database used for implementation and the location of the implementation are detailed. The publication year and the countries of the authors are also provided. Comparative relationships between these elements are presented based on the aforementioned information.

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Section: Discussionmentioning

confidence: 99%

A Survey on Direct Load Control Technologies in the Smart Grid

Ozkop

2024

IEEE Access

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“…The proposed tools rely on significant simplifications and approximations to allow the use of commercial optimisation software (e.g., linear approximations, typical seasonal profiles, limited number of decision nodes, and so forth) [9][10][11], or in a wide variety of complex algorithms based on meta-heuristic approaches [12][13][14]. This is still an emerging area of research, as an approach that can capture all the complexity inherent while optimally (and flexibly) planning the distribution networks under real conditions is yet to emerge.…”

Section: Introductionmentioning

confidence: 99%

Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Ceseña

Mancarella

2016

Sustainable Energy, Grids and Networks

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-Distribution networks are currently undergoing fundamental changes due to the rise of smart solutions such as for instance Demand Response (DR), which increases network complexity and challenges the adequacy of traditional planning practices. This calls for the use of suitable planning methodologies. However, the planning problem may be too cumbersome for most commercial software tools, or may lead to complex bespoke optimisation models that may not be easy to use by network planners. In this light, this work proposes a recursive function that can be used in practical algorithms for planning of smart distribution networks. The recursive function can emulate business-as-usual planning practices and further optimise them, including DR options as potential substitutes for network reinforcement. Several case studies based on real UK networks highlight the robustness and flexibility of the proposed algorithms to address different problems, including uncertainty analysis and risk management. The results clearly show that the proposed tool leads to increased economic and social benefits, particularly when optimising investment strategies considering smart DR solutions. The model is herein distributed open-source as accompanying material to this paper with the aim of encouraging new practices required for smart distribution network planning beyond the traditional academic applications.

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