A probabilistic framework for the techno-economic assessment of smart energy hubs for electric vehicle charging. (July 2022)

• Record Type: Journal Article
• Authors: George-Williams, H.; Wade, N.; Carpenter, R.N.
• Is Part Of: Renewable & sustainable energy reviews. Volume 162(2022)
• Publication Date: 2022-07
• Subjects: Electric vehicle -- Smart charging -- Hydrogen storage -- Solar microgrid -- Monte Carlo simulation -- Vehicle-to-Grid; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2022.112386
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Smart energy hubs (Smart Hubs) equipped with Vehicle-to-Grid (V2G) charging, photovoltaic (PV) energy generation, and hydrogen storage capabilities, are an emerging technology with potential to alleviate the impact of electric vehicles (EV) on the electricity grid. Their operation, however, is characterised by intermittent PV energy generation, as well as uncertainties in EV traffic and driver preference. These uncertainties, when combined with the need to maximise their financial return while guaranteeing driver satisfaction, yields a challenging decision-making problem. This paper presents a novel Monte-Carlo-based modelling and computational framework for simulating the operation of Smart Hubs — providing a means for a holistic assessment of their technical and financial viability. The framework utilises a compact and representative mathematical model, accounting for power losses, PV module degradation, variability in EV uptake, price inflation, driver preference, and diversity in charge points and EVs. It provides a comprehensive approach for dealing with uncertainties and dependencies in EV data while being built on an energy management algorithm that maximises revenue generation, ensures driver satisfaction, and preserves battery life. The energy management problem is formulated as a mixed-integer linear programming problem constituting a business case that includes an adequate V2G reward model for drivers. To demonstrate its applicability, the framework was used to assess the financial viability of a fleet management site, for various caps on vehicle stay at the site. From the assessment, controlled charging was found to be more financially rewarding in all cases, yielding between 1.7% and 3.1% more revenue than uncontrolled charging. The self-consumption of the site was found to be nearly 100%, due mainly to local load shifting and dispatchable hydrogen generation. V2G injection was, however, negligible — suggesting its unattractiveness for sites that do not participate in the demand side response market. Overall, the numerical results obtained validate the applicability of the proposed framework as a decision-support tool in the sustainable design and operation of Smart Hubs for EV charging. Highlights: A modelling framework for simulating the operation of Smart Hubs is presented. Driver preference, as well as uncertainties in electric vehicle parameters are considered. A novel energy management strategy for aggregated electric vehicle charging is proposed. A non-linear energy management problem is transformed to a linear optimisation problem. A decision-support framework for the design and operation of Smart Hubs is presented.