Abstract
This paper presents a novel approach for demand side management (DSM) in a power distribution system by incorporating smart meter data. The approach is aimed at savings maximization by minimizing the energy consumption cost of electricity consumers. The core of the approach consists of data clustering in order to forecast demand for the benefit of DSM decisions by incorporating alternate profiles through extended kmeans algorithm, Taylor series linearization and particle swarm optimization. Two cases including integration of PV generation are simulated using the Irish data of more than 5000 smart meters. Different demand flexibility levels are considered in different Scenarios. The paper argues that inherent non-linearity of raw profiles, is likely to provide suboptimal DSM solutions against electricity consumer cost savings, however the uniformity and smoothness of reshaped alternate profiles are more likely to provide optimal DSM solutions, providing electricity consumers a true benefit for their participation in the DSM process.
Original language | English |
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Title of host publication | 2019 IEEE Milano Powertech |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 6 |
ISBN (Electronic) | 9781538647226 |
ISBN (Print) | 9781538647233 (PoD) |
DOIs | |
Publication status | Published - 26 Aug 2019 |
Event | IEEE PowerTech 2019, 13th - Bovisa Campus of Politecnico di Milano, Milano, Italy Duration: 23 Jun 2019 → 27 Jun 2019 |
Publication series
Name | Power Tech Conference |
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Conference
Conference | IEEE PowerTech 2019, 13th |
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Country/Territory | Italy |
City | Milano |
Period | 23/06/19 → 27/06/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Clustering algorithms
- Energy consumption
- Forecasting
- Indexes
- Load forecasting
- Smart grids
- Smart meters
- demand side
- load forecasting
- load profiling
- management
- smart meter data
ASJC Scopus subject areas
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Safety, Risk, Reliability and Quality