The Evaluation of Agile Demand Response: An Applied Methodology

Babar, Muhammad; Nguyen, Phuong H.; Ćuk, V.; Kamphuis, I.G.; Bongaerts, Martijn; Hanzelka, Zbigniew

doi:10.1109/tsg.2017.2703643

Cited by 27 publications

(25 citation statements)

References 21 publications

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“…On the other hand, each appliance is modeled separately as an agent, which assesses the amount of flexible load available on a 15 min interval, and then shares its bid (or value proposition) to an upstream agent. The details and specifications of appliance agents are discussed in [13]. Furthermore, the given network is augmented such that up to 60% of the households are connected to PV systems, 80% of the households have electric vehicles (EV), and every household has at least a controllable refrigerator and a washing machine.…”

Section: B Methodologymentioning

confidence: 99%

“…A local market framework incorporating day-ahead and real-time operations is proposed in [9] that aims to minimize the cost of network support through the voluntary and compulsory participation of the prosumers. A spatially distributed hierarchical control architecture is discussed in [13], that incorporates the network reconfiguration possibilities along with market-based flexibility. The proposed DR paradigm determines optimal dispatch of a grid-connected DERs and dispatchable home appliances (referred to as domotics) providing flexibility services to the market and the network.…”

Section: Introductionmentioning

confidence: 99%

“…Reinforcement learning (RL) approaches in DR have attracted significant attention as a possible solution to address the limitations associated with the scheduling [14]- [16], forecasting [17], and dispatch [13], [18] of flexible demand. Reinforcement 978-1-7281-0653-3/19/$31.00 c 2019 IEEE learning (RL) is concerned with how an agent ought to take actions in a dynamic environment to maximize long-term reward [19].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks

Babar

Roos²,

Nguyen

2019

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Syst

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: B Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks

Babar

Roos²,

Nguyen

2019

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Syst

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“…The composite bidding rules, as mentioned in [42], so according to (2) the demand elasticity of the current k th interval versus an interval k : k = k, ∀k ∈ K can be defined as:…”

Section: Energy Elasticity Learnermentioning

confidence: 99%

Energy Flexometer: Transactive Energy-Based Internet of Things Technology

et al. 2018

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Effective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design.

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“…Now the agent does not explore anymore; it chooses the action that it knows to bring the maximum value. This method has been applied in several papers, where they mostly used the Q‐learning method of RL (Babar et al, ; Kladnik et al, ; Wen, O'Neill, & Maei, ; Yanf, Nagy, Goffin, & Schlueter, ).…”

Section: Implementation Of Aql Agentmentioning

confidence: 99%

The use of intelligent aggregator agents for advanced control of demand response

Medved

Artač

Gubina

2018

WIREs Energy & Environment

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With the integration of distributed renewable energy sources (DRES) and active demand (e.g., units providing demand response, DR) in the distribution grid, the importance of monitoring the network conditions, managing the line congestions and observing the voltage levels is increasing. The distribution system operator (DSO) needs a mechanism, such as the traffic light system, to screen and approve the proposed operation schedules of the flexible active resources in the distribution grid. Their aggregated control will require the aggregators to employ advanced scheduling algorithms. The DR scheduling algorithms can be set to pursue various goals, for example, maximization of profit or cost reduction, grid support, or provision of the ancillary services. In the paper, we present a new DR scheduling approach suitable for the aggregation agent using approximate Q‐learning (AQL) algorithm scheduling. We present the AQL algorithm and the associated assumptions used in simulations on a real‐world low‐voltage (LV) grid model, comparing the AQL approach results to those of the economic scheduling and the energy scheduling approaches. Our assumption was that the AQL approach could outperform the energy or economic approaches as the AQL agent would be able to learn to avoid the scheduling penalties. The results of our research show that the aggregator agent using the economic approach shows the best economic performance, but causes the most schedule violations. The energy scheduling approach improves the network voltage profile but lowers the aggregator's profit. The AQL approach results in the agent's economic performance between the former two approaches with minimal schedule violations, confirming our research hypothesis. This article is categorized under: Concentrating Solar Power > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Efficiency > Economics and Policy

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The Evaluation of Agile Demand Response: An Applied Methodology

Cited by 27 publications

References 21 publications

Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks

Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks

Energy Flexometer: Transactive Energy-Based Internet of Things Technology

The use of intelligent aggregator agents for advanced control of demand response

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