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Research Focus: Smart Cities

Current Research Projects


Data-driven grid planning
  • Design of a decision support system (DSS) for low and medium voltage grid planning
  • Improvement of operational and strategic grid planning
  • Stochastic modelling of decentralized generation units focusing on individual buildings
  • Probabilistic load flow calculations, complete iteration over all networks in the low / medium voltage layer
  • Simulation of cost optimal grid expansion measures using dynamic stochastic programming
  • Determination of long-term grid investments for strategic grid planning
  • Evaluation of smart-grid technologies for reduction of grid investments
  • DSS to streamline processes in operative grid planning




Identifying spatial key performance indicators for the use of carsharing services
  • Determining the optimal business area for free-float carsharing providers
  • Data basis: 1 million trips and 180,000 POIs
  • Statistical model that utilizes relationships between trips within a specific region and POIs in the vicinity
  • Visualization of expected demand in current business area and in prospective expansion areas
  • Decision criteria for expansion strategies
  • Experiences of the carsharing partner confirm model predictions
  erwartete_nachfrage_poi.jpg tatsaechliche_nachfrage.jpg Expected demand according to points of interest (left), compared to actual demand (right)


Spatial relationships from social media
  • Explanation of spatial preferences of different population groups according to social network data
  • Data basis: 650,000 Tweets and 60,000 POIs
  • Estimation of the appeal of different areas within the city through spatial analysis of Twitter activity in the vicinity
  • Robust relationships between Twitter acitivity and establishments in the vicinity could be uncovered
  • Insights are particularly valuable for market research and the use of location-based services, such as recommendation systems
  geo_temporale_beobachtungen.jpg auftrittswahrscheinlichkeiten.jpg Spatio-temporal observations (left) can be used to calculate the probability of certain events (right)


  • Bendler J, Wagner S, Brandt T, Neumann D: Taming Uncertainty in Big Data - Evidence from Social Media in Urban Areas BISE/WIRTSCHAFTSINFORMATIK Special Issue "Big Data", 2014.
  • Bendler J, Brandt T, Wagner S, Neumann D: Investigating Crime-to-Twitter Relationships in Urban Environments – Facilitating a Virtual Neighborhood Watch 2014 (22nd European Conference on Information Systems (ECIS 2014), Tel Aviv, Israel, June 9-11, 2014).



Completed Research Projects


Demand Response
  • Design of Demand Response systems in order to optimize load shifting
  • Estimation of cost-benefit-ratio using an aggregated, local view
  • Data basis: electricity prices, power demand, Demand Response potential
  • Financial optimization model to provide decision support for load shifting 
  • Sensitivity analyze to assess the influence of different parameters
  • High communication costs exceeding direct financial savings
  • Demand Response potential to low to be offered at electricity exchanges → policy adjustments necessary


  • Direct load shifting more profitable than other usage scenarios such as control reserve


Information Systems for Vehicle-to-Household Integration
  • Design of an Information System to improve electricity usage of households
  • Identifying "Green Synergies" between Vehicle-to-Grid (V2G) technology and photovoltaic power
  • Data basis: real history of electricity prices, photovoltaic feed-ins, mobility patterns
  • Developing several load strategies to optimally charge the battery of electric vehicles in order to prevent electricity purchase
  • Sensitivity analyze to assess the influence of different parameters
  • Profits from photovoltaic feed-ins exceed those for household consumption and mobility
  • Utilizing a V2G hybrid car allows to store electricity from solar sources

Benchmark: no load strategy                 V2G load strategy

  • Finding a resulting profit that is equivalent to a 7.500$ subvention for buying electric vehicles with V2G technology


Optimal placement of electric vehicle charge points
  • Identification of optimal locations for charge points in Amsterdam
  • Maximization of the expected utilization of the stations
  • Data basis: 1.1 million charging events and 61,000 points of interest, such as schools, bars, or museums
  • Dynamic model that relates the utilization of a station to POIs in its vicinity
  • Enables the estimation of one station's effect of the utilization of surrounding stations
  • Visualization of expected utilization (green &rar; high; red &rar; low) within Amsterdam
  • Expected utilization adapts when new stations are placed
  • Automated placement of charge points according to expected utilization
  •  Wagner S, Brandt T, Neumann D: Smart City Planning – Developing an Urban Charging Infrastructure for Electric Vehicles 2014 (ECIS 2014, 22nd European Conference on Information Systems, Tel Aviv, Israel, June 9-11, 2014).
  • Wagner S, Brandt T, Neumann D: Business Intelligence in Infrastructure Planning – Maximizing the Utilization of Charging Stations in Urban Setting 2014 (Winter Conference on Business Intelligence (WCBI 2014), Snowbird, Utah, 27 February – 1 March 2014).