Person:
Valogianni, Konstantina

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Konstantina
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Valogianni
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IE University
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IE Business School
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Information Systems and Technology
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2020 - 20232
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Author: Ketter, Wolfgang × Start date: 2020 ×

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Now showing 1 - 2 of 2
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    Publication
    Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action
    (Informs, 2023-09-03) Ketter, Wolfgang; Schroer, Karsten; Valogianni, Konstantina; https://ror.org/02jjdwm75
    Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs (e.g., due to traffic congestion). However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence–powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. The resulting layered modular architecture is similar to that seen in other cyber-physical systems. Yet, it also comes with several characteristics and challenges that are unique to the domain of mobility and require entirely new solution approaches. Although other management and domain-specific research disciplines have started to embrace the new opportunities for research resulting from this deep structural change, the information systems (IS) community’s involvement in smart mobility research has been marginal. Yet, we argue that our field’s uniquely multidisciplinary, data-driven, and socio-technical research lens puts it in a strong position to address many of the large-scale societal challenges encountered in the mobility sector. Therefore, we make the case for IS research to play an active role in delivering a smart sustainable mobility ecosystem that is beneficial to users, mobility providers and the environment. We contribute a research framework to direct IS research efforts while providing a shared understanding of the smart sustainable mobility domain. We also present seven IS research opportunities along the dimensions of this framework and propose concrete angles of attack which we hope will spur an impactful and structured research agenda in the area.
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    Publication
    Sustainable Electric Vehicle Charging using Adaptive Pricing
    (Wiley, 2020-03-13) Ketter, Wolfgang; Collins, John; Zhdanov, Dmitry; Valogianni, Konstantina; https://ror.org/02jjdwm75
    A transition to electric vehicles (EVs) is widely assumed to be an important step along the road to environmental sustainability. However, large-scale adoption of EVs may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low-price periods, if large numbers of EV owners use smart charging to benefit from low prices. This effect is expected to be amplified when EV owners adopt smart decision support to assist them with optimal charging decisions. Therefore, energy policymakers are interested in advanced pricing schemes that can smooth demand or induce demand that comes as close as possible to a desired profile. We show, through simulations calibrated with real-world data, that current approaches to electricity pricing are limited in their ability to induce desired demand profiles. To address this challenge, we present adaptive pricing, a method to learn from EV owner reactions to prices and adjust announced prices accordingly. Our method draws on the Green Information Systems principles and can assist grid operators in ensuring the reliable operation of the grid. We evaluate our results in simulations, where we find that adaptive pricing outperforms current electricity pricing schemes, yielding results close to the theoretically optimal ones. We test our method in inducing both flat and extremely volatile demand profiles, and we see that in both cases it manages to induce EV charging close to the ideal scenario under perfect information.