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Cited by 23Smart Grid Energy ManagementMicrogrid Control and OptimizationElectric Power System Optimization

Load Shifting Agents for Automated Demand Side Management in Micro Energy Grids

Deindl, Matthias, Block, Carsten, Vahidov, Rustam, Neumann, Dirk·2008·2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
DOI 10.1109/saso.2008.64
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Abstract

This paper describes a novel approach for the automated management of micro energy grids. In particular a market based resource allocation mechanism is used to control energy generators and consumers within a micro energy grid. This approach requires energy consumers (producers) to buy (sell) their energy demands (supplies) through a specialized electronic auction platform. But as manually negotiating all energy demands and supplies on such a market is a tedious task, its automation is highly desirable and thus leads to the main contribution of this paper: The automation of the demand side bidding process through electronic bidding agents, which are equipped with an intelligent buying strategy that allows them to dynamically react to market changes and adapt their bidding behavior accordingly. More precisely, the agents are able to shift energy demand within certain boundaries from (expensive) peak hours to those times of the day where energy demand and thus energy prices are lower in order to minimize their cost. Moreover, as our results show, this behavior leads to a smoothed load curve for the whole system, i.e. demand peaks are reduced while base load increases.

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21 of 21 references verified0 citation errors · 0 not checkable

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  • Techniques such as deep RL, agent-based models, online optimization, and dynamic programming can help maximize profits for decentralized storage, demand, and generation, given real-time prices.

    “Techniques such as (deep) RL [291,292,810,881], agent-based models [173,662,663,863], online optimization [99], and dynamic programming [704] can then help maximize profits for decentralized storage, demand, and generation, given real-time prices.”

    From Tackling climate change with machine learning (2023) · cites this paper as [173]

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Related

Authors
Deindl, M., Block, C., Vahidov, R., & Neumann, D.
Affiliations
  • RWTH Aachen University (DE)
  • Karlsruhe Institute of Technology (DE)
  • Concordia University (CA)
  • University of Freiburg (DE)
ORCID
Venue
2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Publisher
IEEE
Published
2008-10
Pages
487-488
DOI
10.1109/saso.2008.64
OpenAlex
W2144135680
Cited by
23
References
21
Citations by year
2023: 1 · 2022: 2 · 2019: 2 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 6 · 2013: 2 · 2012: 3
FWCI
1.90
Type
conference paper
Language
en
Open access
closed
Locations
  • IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) (publishedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
Keywords
load shifting, demand-side management, micro energy grid, online auctions, market-based resource allocation, demand response, peak shaving, load curve smoothing, multi-agent systems, energy pricing

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