Symposium Co-Chairs

• Chris Develder, Ghent University – imec, Belgium
• Wenpeng Luan, China Electric Power Research Institute, China
• Marco Levorato, UC Irvine, CA, USA
   

Scope and Motivation

Smart meters have been deployed worldwide to different degrees, with varying monitoring and communication technologies. Furthermore, several projects have shown the potential of demand response (DR) and dynamic pricing. DR, or the ability to dynamically adjust current or future electricity load, in response to price signals, therefore covers the entire chain from the energy market dynamics (on-line price signals) to the inside intelligence of the household appliances. Key words in this story are integration and flexibility.
This symposium will focus on research and innovation results closing the triangle: smart meter – demand response – dynamic pricing. DR strategies will be effective if they succeed in optimally integrating the smart metering infrastructure and if they incorporate the flexibility to control a large variety of end-use appliances. An optimal data management and data exchange balance needs to be achieved between central, local (customer) and appliance level. The in home intelligence system will require an interaction between smart meter owners (e.g., grid operators), home gateways (e.g., IT providers) and end-use appliances (e.g., white good manufacturers). And finally the role of the end-user is essential.
Papers can summarize recent theoretical developments and practical experience in the topic area, as further summarized below.

Topics of Particular Interest

The symposium aims to focus on theoretical, experimental and proof-of-concept results of research and innovation activities where smart meters, demand response strategies and dynamic pricing are applied in integrated context. Topics of interest include, but are not limited to:

•   Integration of smart meters in smart grids:
- Functionalities of smart meters in function of smart grids
- Integrated and flexible in home intelligence systems (meters, gateways, control boxes, appliances, visualization)
- Integration of gas and electricity consumption data

•   Intelligence from grid to in-home appliances:
- Automated DR algorithms to match supply and demand
- Electric home appliances/(renewable) generators with embedded intelligence
- DR with multiple energy sources’ utilization optimization
- DR experiences and experimental outcomes

•   Market models with multi-stakeholder involvement/active end-users:
- Dynamic Pricing Models
- Incentive-based vs. punitive pricing, energy profiles and demand response
- Predicting and impacting end-user load profiles
- Impact studies of DR strategies on consumer energy behavior
- DR capabilities in consumer and industrial applications

•   Data analytics of smart (sub)meter data:
- Modeling and analysis of consumer’s consumption/production patterns
- Nontechnical loss and anomaly detection
- Integration of smart metering and distribution data in supporting system operation, asset management and customer service
- Non-intrusive load monitoring (NILM)
- Load disaggregation

•   Real-world experiences and lessons learned in any of the above areas