Siobhan Jocelyn Larissa Powell


Last Name

Powell

First Name

Siobhan Jocelyn Larissa

ORCID

0000-0003-2928-840X

Organisational unit

03695 - Hoffmann, Volker / Hoffmann, Volker

Filters

2020 - 202523
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Publications 1 - 10 of 23
  • Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging
    Item type: Journal Article
    Powell, Siobhan Jocelyn Larissa; Kara, Emre Can; Sevlian, Raffi; et al. (2020)
    Applied Energy
    To accelerate adoption of non-residential charging for electric vehicles, sites must maximize utilization of existing electrical infrastructure. In this study we model electric vehicle charging at a workplace using real charging data and evaluate the lifetime of the site’s transformer as the number of charging stations is incrementally increased. We implement and compare a range of control schemes for workplace charging including minimizing the peak load, capping the total load, minimizing bills under different rate structures with time-of-use energy costs and demand charges, and directly minimizing the transformer’s aging. These are compared by the number of vehicles they allow the transformer to support, the transformer’s health, and the operator’s electricity bill. We draw a connection between minimizing the peak load and improving the transformer’s health. We observe that minimizing the electricity bill is the best scheme by both criteria when the bill includes a demand charge; in our experiment it allowed the infrastructure to support over 67% more cars than under uncontrolled charging. To protect the transformer we recommend that demand charges or capacity management be applied to parking lots of charging electric vehicles with high infrastructure utilization, and operators schedule charging to minimize their electricity bills.
  • Future-proof rates for controlled electric vehicle charging: Comparing multi-year impacts of different emission factor signals
    Item type: Journal Article
    Powell, Siobhan Jocelyn Larissa; Martin, Sonia; Rajagopal, Ram; et al. (2024)
    Energy Policy
    Electricity pricing can be used to shift the timing of electricity demand, but the choice of price signals is highly constrained. Consumer rates are updated every few years and limited to simple daily profiles, yet must capture the complex dynamics of a changing electricity system. Emission factors (EFs) were developed as an evaluation tool, but are increasingly used as demand response (DR) signals. Given these constraints, can they be effective? We evaluate the emissions impact of EF-based electricity rates with and without supply-side emissions pricing. We study controlled electric vehicle (EV) charging in the Western U.S. up to 2037 by coupling an electricity system dispatch model and a data-driven EV charging model. We compare average and short-run marginal EFs with a new medium-run marginal EF that better matches the timeline of electricity rate updates. We find that a stable supply-side signal makes DR more valuable: DR reduces emissions by up to 6% with supply-side carbon pricing or just 2% without it. Medium-run marginal EFs yield the most consistent emission reductions, but constraints on charging flexibility limit their impact. We recommend policymakers base rates for DR on medium-run marginal emission factors and implement supply-side carbon pricing to facilitate greater emission reductions.
  • Flexibility provision from electromobility and buildings - Synthesis Report
    Item type: Report
    PATHFNDR Consortium; Aliana, Arnau; Bellizio, Federica; et al. (2025)
    Switzerland’s energy transition relies on electrifying transportation and heating while keeping electricity generation low in greenhouse gas emissions and ensuring grid stability. The required energy system flexibility will still be provided mainly by hydropower. However, additional valuable demand-side flexibility could be provided by electric vehicles and heat pumps by shifting their consumption to align with renewable energy generation. This report evaluates the role of electric vehicle and heat pump flexibility by synthesizing research from across the PATHFNDR project consortium. The report thus quantifies these technologies' potential flexibility and value in supporting both the transmission and distribution systems and assesses existing and required market and policy mechanisms to unlock their full benefits. New scenario-based modelling results show that flexibility provision from electric vehicles and heat pumps can reduce system costs, defer network upgrade investments, lower electricity prices and imports, and reduce curtailment of renewable energy by better aligning demand with surplus generation. Electric vehicle smart charging and vehicle-to-grid can act as energy storage, which shift or discharge electricity to support the grid. Heat pump demand can be shifted using thermal inertia and thermal energy storage to reduce peak demand and stabilize the grid. At distribution level, flexibility-aware planning can reduce or defer low- and medium-voltage grid upgrades with minimal PV energy loss. Our research also finds that enabling flexibility-readiness through supportive policy and market mechanisms are critical for effective demand-side management. Some mechanisms are already in place, such as contracts with dynamic pricing, direct load control, and subsidies for smart charging and vehicle-to-grid infrastructure. However, further policies, changes to regulation, and owner/user acceptance are needed. Surveys of the Swiss public show that support for flexible EV charging and heat pump operation is high, indicating readiness for further policy and market changes supporting flexibility and renewable energy integration. Unlocking this flexibility will improve Switzerland’s energy resilience and sustainability while empowering consumers to participate actively in grid management. Future research should focus on scalable implementation and deployment: exploring business models for flexibility provision, evaluating new policy incentives, and demonstrating the use of flexibility at scale.
  • Cascading marginal emissions signals for green charging with growing electric vehicle adoption
    Item type: Journal Article
    Martin, Sonia; Powell, Siobhan Jocelyn Larissa; Rajagopal, Ram (2025)
    Nature Communications
    Shifting electric vehicle charging to use cleaner electricity can reduce carbon dioxide emissions. Grid emissions factors can inform when to shift demand, but key assumptions behind existing emissions factor methods fail for today’s grids and electric vehicle adoption levels. We combine real charging data with a Western U.S. grid model to test these methods under increasing electric vehicle adoption. We find that following existing average and marginal emissions factor methods to manage charging can inadvertently increase grid emissions when emissions factor signals are noisy, too many electric vehicles follow the same signal, or when high-emitting generators respond. We instead propose an alternative Cascading marginal emissions factor strategy that manages charging in smaller groups. We show that the Cascading strategy reduces added emissions by 10–28% across grid scenarios for at least 2 million electric vehicles. Our research reveals how demand response methods must change to reduce emissions and support the grid transition under wider electric vehicle adoption.
  • Large-scale scenarios of electric vehicle charging with a data-driven model of control
    Item type: Journal Article
    Powell, Siobhan Jocelyn Larissa; Vianna Cezar, Gustavo; Apostolaki-Iosifidou, Elpiniki; et al. (2022)
    Energy
    Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this paper, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.
  • Using long-run marginal emissions to reflect future grid changes in pricing signals for electric vehicle charging
    Item type: Other Conference Item
    Powell, Siobhan Jocelyn Larissa (2023)
  • Policies to Improve Large-Scale Grid Integration of Electric Vehicles
    Item type: Presentation
    Powell, Siobhan Jocelyn Larissa (2024)
  • Charging toward a cleaner future: the transition to electric vehicles
    Item type: Other Conference Item
    Powell, Siobhan Jocelyn Larissa (2023)
  • Unlocking Inter-day Flexibility in Electric Vehicle Charging to Support Future Grids’ High Renewable Integration
    Item type: Other Conference Item
    Powell, Siobhan Jocelyn Larissa (2023)
  • Electromobility and Flexibility
    Item type: Presentation
    Powell, Siobhan Jocelyn Larissa (2025)
Publications 1 - 10 of 23