Automated Demand Response for Smart Buildings and Microgrids: The State of the Practice and Research Challenges

Samad, Tariq; Koch, Ed; Stluka, Petr

doi:10.1109/jproc.2016.2520639

Cited by 195 publications

(104 citation statements)

References 19 publications

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“…transactive control). Power system application areas relevant to distributed control of DER include EV charging [17], demand response (DR) [18] or microgrids [16], [27]. Most of these surveys provide a comprehensive perspective on grouping control solutions by their control objectives, such as [16], [17], [27].…”

Section: B Categorization Of Distributed Control In Surveysmentioning

confidence: 99%

“…Distributed control of DER units is a very active research topic within the context of Smart Grids, and a diverse range of applications and approaches have been proposed in a growing body of academic literature. Several attempts at analyzing and categorizing such control strategies have been undertaken for specific system types or application areas, such as applications in microgrids [16], electric vehicle (EV) charging [17] or demand response (DR) [18], for agent based systems [15], or for transactive control applications [19]. These works provide domain-specific categorizations from the perspective of a particular application, and adopt existing categorizations from one particular field such as computer science, control engineering or distributed systems.…”

Section: Introductionmentioning

confidence: 99%

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Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

Han

Heussen

Gehrke

et al. 2018

IEEE Trans. Smart Grid

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Abstract-Distributed control strategies applied to power distribution control problems are meant to offer robust and scalable integration of distributed energy resources (DER). However, the term "distributed control" is often loosely applied to a variety of very different control strategies. In particular there is a lack of discrimination between aspects related to communication topology, physical distribution of components and associated control objectives. This has lead to a lack of objective criteria for performance comparison and general quality assessment of state of the art distributed control solutions. For such comparison, a classification is required that is consistent across the different aspects mentioned above. This paper develops systematic categories of control strategies that accounts for communication, control and physical distribution aspects of the problem, and provides a set of criteria that can be assessed for these categories. The proposed taxonomy is applied to the state of the art as part of a review of existing work on distributed control of DER. Finally, we demonstrate the applicability and usefulness of the proposed classification to researchers and system designers.

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Section: B Categorization Of Distributed Control In Surveysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

Han

Heussen

Gehrke

et al. 2018

IEEE Trans. Smart Grid

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“…As one of the most important DSM applications, peak shaving can help to mitigate such issue through demand response [2]- [5] and BESS [7]- [9]. This paper focuses on the BESS-based peak shaving methodology.…”

Section: Introductionmentioning

confidence: 99%

A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving

Wang

Shi

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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For the last ten years, utilities have observed ongoing transitions on consumers' load curves. The previously flat load curves have more frequently turned into duck-shape. This is jointly caused by the increasing household loads as well as the popularity of rooftop solar photovoltaic. Such load curve transition challenges the operation flexibility of the existing systems and greatly increases the per-MWh energy costs. Peak shaving, in this context, becomes a critical task to demand-side management. Owing to the development of Battery Energy Storage Systems (BESS), numerous peak shaving strategies have been developed and implemented. In this paper, by applying a stacked autoencoder (SAE)-based residential peak load curve forecasting technology, we further lift the peaking shaving capabilities of BESSs to a new level. The proposed strategy takes into account the welfares of both generation-side and demandside and reaches an optimal balance. A comprehensive case study using smart meter data demonstrates the effectiveness of the proposed method in peak shaving application.

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“…Interactive participation of the demand side, such as residential consumers, has a key role in this context [2]. DR is one of the concepts present in these technologies that begin to be widely used nowadays [3]. A DR event can be transmitted from the grid or market operator to the demand side, due to economic or technical problems [4].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Demand Response Programs Based on Open ADR Technology

Abrishambaf

Faria

Vale

2017

Advances in Intelligent Systems and Computing

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Automated Demand Response for Smart Buildings and Microgrids: The State of the Practice and Research Challenges

Cited by 195 publications

References 19 publications

Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving

Real-Time Implementation of Demand Response Programs Based on Open ADR Technology

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