Coffee & Data #4: Digital Energy

What’s a better way to start your morning than with coffee and data?

Program:
9:00 – 9:30 Georgios Methenitis (CWI)

A Multi-Scale Energy Demand Model suggests sharing Market Risks with Intelligent Energy Cooperatives

In this talk we propose a multi-scale model of energy demand that is consistent with observations at a macro scale, in our use-case standard load profiles for (residential) electric loads.
We employ the model to study incentives to assume the risk of volatile market prices for intelligent energy cooperatives at different aggregation scales of energy consumption. Next to scale, we investigate the benefits
of demand response, more precisely intelligent scheduling of time-shiftable electric processes, and virtual storage intraday and between days. Results show that the increasing electrification and introduction of flexibilities
(electric vehicles, thermal applications, storage, etc.) is going to make market participation viable for smaller groups of consumers. Retailers may thus introduce innovative tariffs for intelligent energy cooperatives to
share the risk of volatility in wholesale markets for electricity.

9:30 – 10:00 Felix Claessen (CWI)

The value of online information for demand response in Walrasian electricity markets

Smart energy systems integrate renewables and demand response. Most European electricity markets coordinate the resulting time-varying flexibility in demand and supply by organising day-ahead trade with Walrasian mechanisms, using simultaneous call auctions and sealed bids. These mechanisms give bidders no information on each other's values and flexibilities until after clearing. In this paper we simulate two alternative day-ahead market mechanisms which share information, such that bidders obtain a better position before entering the intraday market. One mechanism uses an ascending shared market price signal rather than sealed bids. The other auctions off future timeslots consecutively rather than simultaneously. We perform a case study on 400 households with electric
vehicles, either with or without volatile wind generation. Results show that a price-taking flexible consumer can obtain higher utility in the market with simultaneous ascending-price auctions, because online price
information reduces uncertainty over available energy and prices.

10:00 – 10:15 Adriatik Bedjeti, Renè Aparicio Saez, Giulia Colacicco (VU)

An Experiment on Software Energy Consumption in the VU Green Lab

coffee break

10:30 – 11:00 Maarten van Someren (UVA)

Energy Health Monitoring

Many buildings have energy installations that do not work optimally. This is due to suboptimal control settings,
faults in the system and incorrect use. We developed a simple monitoring system that uses historical energy consumption data in combination with weather data and data on the use of the building to detect anomalies: periods with unexpected high (or low) energy consumption. Engineers can use this to detect problems at an earlier stage. The approach and method can be extended to include multiple buildings and to more full-fledged health monitoring of the climate installations of a building.

11:00 - 11:30 Nanda Piersma (HvA)

CHIEF Amsterdam: The use of data analysis for the development of the Charge Infrastructure of Electric Vehicles.

Since 2013 the Urban Technology research group of the Amsterdam University of Applied Science monitors and studies the charge behavior on the public infrastructure in the City of Amsterdam on behalf of the council of Amsterdam. Since 2014 the cities of Rotterdam, Utrecht and The Hague joined tis research program and provided the data of the use of their infrastructure.

The main goal of the research project is to support the councils and the corporations in electric vehicles charging in optimizing the use and extrapolation of the charge infrastructure by the use of data studies. Since September 2015 the data is streaming, and up to .. GB is recorded per day. By monitoring the charge sessions and analyzing the data we want to optimize the effectiveness and efficiency of the public
charge infrastructures.

We discuss the descriptive statistical results for 2014 and 2015 with clustering results for customers and charge poles types in Amsterdam. Secondly we show predictive statistical results for 2015 using forecasting models for Amsterdam and The Hague.

11.30 - 12.00 Pitches and Networking