Details
| Original language | English |
|---|---|
| Title of host publication | IEEE PES General Meeting |
| Pages | 1-6 |
| Number of pages | 6 |
| Publication status | Published - 1 Jul 2010 |
Abstract
Keywords
- distributed power generation, energy management systems, load flow, power grids, distributed power flow calculation, multiagent calculation, distributed generation, DG sources, local utility, distribution grids, adaptive agents, Load flow, Energy management, Adaptive systems, Face, Artificial intelligence, Diakoptics, Adaptive Systems, Distribution grids, Energy management system, Local utilities, Measurement, Multi-Agent-System, Power system automation, power flow, System services
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IEEE PES General Meeting. 2010. p. 1-6.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Multi-agent based distributed power flow calculation
AU - Wolter, M.
AU - Guercke, H.
AU - Isermann, T.
AU - Hofmann, L.
PY - 2010/7/1
Y1 - 2010/7/1
N2 - Against the background of the increasing amount of distributed generation and the intention of local utilities to offer system services at the distribution level, a high degree of automation gets more and more necessary. Right now DG sources already are able to offer system services although these potentials often remain unused. Local utilities face these new challenges by applying energy management systems which essentially need information on the system state. Unfortunately, measurement is sparsely spread in distribution grids so the required data normally cannot be provided. The authors propose a new approach on managing distribution systems based on adaptive agents which are placed at different locations on the grid. They are able to locally control sources, loads and switches to keep the system state within tolerable bounds. In doing so, knowledge of the entire grid state becomes obsolete. For this purpose a decentralized power flow method is introduced. Depending on available local information each agent is able to calculate a system state. By communicating their results other agents can now react adequately. Furthermore, it is possible to infer on the entire system state as well.
AB - Against the background of the increasing amount of distributed generation and the intention of local utilities to offer system services at the distribution level, a high degree of automation gets more and more necessary. Right now DG sources already are able to offer system services although these potentials often remain unused. Local utilities face these new challenges by applying energy management systems which essentially need information on the system state. Unfortunately, measurement is sparsely spread in distribution grids so the required data normally cannot be provided. The authors propose a new approach on managing distribution systems based on adaptive agents which are placed at different locations on the grid. They are able to locally control sources, loads and switches to keep the system state within tolerable bounds. In doing so, knowledge of the entire grid state becomes obsolete. For this purpose a decentralized power flow method is introduced. Depending on available local information each agent is able to calculate a system state. By communicating their results other agents can now react adequately. Furthermore, it is possible to infer on the entire system state as well.
KW - distributed power generation
KW - energy management systems
KW - load flow
KW - power grids
KW - distributed power flow calculation
KW - multiagent calculation
KW - distributed generation
KW - DG sources
KW - local utility
KW - distribution grids
KW - adaptive agents
KW - Load flow
KW - Energy management
KW - Adaptive systems
KW - Face
KW - Artificial intelligence
KW - Diakoptics
KW - Adaptive Systems
KW - Distribution grids
KW - Energy management system
KW - Local utilities
KW - Measurement
KW - Multi-Agent-System
KW - Power system automation
KW - power flow
KW - System services
U2 - 10.1109/PES.2010.5589910
DO - 10.1109/PES.2010.5589910
M3 - Conference contribution
SP - 1
EP - 6
BT - IEEE PES General Meeting
ER -