Aivars Rubenis

Urban electrification strategies are increasingly relying on on-street charging to support residents without private driveways. However, distribution networks in dense neighbourhoods often operate near the limits established during their initial planning. This paper uses Kensington, London, as an example to focus on on-street charging in medium-density urban areas with terraced housing, and to investigate whether coordinated, phase-aware charging can increase EV uptake in multi-occupancy residential settings without triggering costly network reinforcement. Specifically, the study quantifies the impact of household demand stochasticity and vehicle-use heterogeneity on available thermal headroom and charging adequacy, as defined by standard After Diversity Maximum Demand (ADMD) planning levels. The analysis integrates a high-resolution residential demand model with behavioural EV driving archetypes to simulate overnight charging over a 365-day period, utilising the existing grid connection of six flat terraced houses without grid updates. The model employs ADMD to define aggregate feeder capacity and to simulate scenarios in which total load is managed to prevent exceeding the per-household diversified rating. The framework evaluates the sufficiency of the morning state of charge (SoC) for one, two or three vehicles by comparing an optimised phase-balancing strategy with a worst-case uncoordinated allocation. The results demonstrate that, for a six-flat terraced house, the available “soft” ADMD headroom, which is dictated by the coincidence of household base load, is the primary binding constraint rather than charger nameplate power. For initial uptake of one EV per six flats, the mean morning battery SoC remains high (70–73%). However, as the number of vehicles increases to three per shared constraint, the system reaches a capacity-saturated regime. In this state, optimisation improves grid quality and prevents localised phase overloads, but cannot offset the overall energy deficit. These findings suggest that, although phase-aware coordination can facilitate early-stage on-street electrification, saturation levels will require either physical reinforcement or advanced demand flexibility.

The Urban energy systems face increasing pressure to decarbonise while maintaining security of supply and resilience to external disruptions. Rooftop photovoltaic (PV) systems on multi-apartment residential buildings offer a promising pathway to increase locally available renewable electricity in dense urban environments. However, reliable assessment of PV contribution requires a statistically robust understanding of residential electricity demand patterns at high temporal resolution. This article presents the first stage of a multi-layer study assessing the technical viability of rooftop PV deployment in multi-apartment buildings in Riga, Latvia, with a focus on energy availability and resilience. The object of the study is residential electricity demand in apartment buildings. Using hourly smart-meter data from a representative sample of 198 apartments (3.1 million observations), the study analyses whether households with different annual electricity consumption levels exhibit systematically different diurnal or seasonal demand structures once scale effects are removed. Electricity demand profiles were normalised by annual consumption and analysed using functional analysis of variance with permutation-based inference. The results show that mean hourly electricity consumption ranges from 0.09 kWh h⁻¹ in the lowest consumption group (3000 kWh yr⁻¹), while dispersion also increases markedly. However, after normalisation, neither diurnal nor seasonal load-shape functions differ statistically across consumption groups. The tests do not reject equality of diurnal profiles (p = 0.24) or seasonal profiles (p = 0.30). These findings demonstrate that higher residential electricity consumption in multi-apartment buildings reflects scale effects rather than distinct daily routines or seasonally concentrated usage. An assumption about winter-dominated electric heating in high-consumption apartments is not supported at a statistically significant level. From a practical perspective, the results support the use of representative, scalable demand profiles in subsequent modelling of rooftop PV generation, energy flows, and local energy availability, providing a robust foundation for resilience-oriented urban energy assessments.

The decarbonisation of corporate vehicle fleets is a central challenge in achieving Europe’s climate neutrality targets under the European Green Deal. Although corporate vehicles constitute only a share of the total fleet, they account for a disproportionate fraction of total mileage and associated CO₂ emissions. Despite fiscal incentives and regulatory support, the adoption of battery electric vehicles (BEVs) within the corporate sector remains significantly below private uptake, primarily due to uncertainty about operational feasibility and charging constraints. This study presents a data-driven framework for assessing fleet electrification potential based on empirical driving data and simulation-based modelling. Using vehicle usage records from the carmonitor.eu telematics platform, the research identifies four distinct operational archetypes within corporate fleets, differentiated by travel intensity, trip fragmentation, and temporal driving structure. These archetypes are derived through a clustering methodology employing standardised behavioural indicators, principal component analysis (PCA), and k-means segmentation, validated by silhouette and Davies–Bouldin indices. Results demonstrate pronounced heterogeneity in fleet operation, with daily driving distances, trip frequency, and vehicle availability varying substantially across clusters. Scenario-based modelling reveals that electrification feasibility depends not only on total mileage but also on temporal driving regularity and charging opportunity windows. Vehicles with predictable daily cycles and long overnight parking are found to be most suitable for early electrification, while high-mileage or irregular-use vehicles require access to fast-charging infrastructure and larger battery capacities. The study concludes that segmenting corporate fleets by operational archetype provides a robust analytical foundation for transition planning, enabling tailored recommendations for vehicle selection, charging infrastructure, and total cost of ownership optimisation. By linking empirical usage data with simulation and scenario modelling, the paper contributes a replicable methodological approach for evidence-based fleet decarbonisation strategies across Europe.

One of the primary challenges in integrating electric buses into public transport systems is their cost. This financial burden is exacerbated in colder climates due to the significant energy required to heat the passenger compartment. To address this issue, the project ‘Development of a Sustainable Heating Solution for the Salon of Public Electric Transport’ was undertaken. It aimed to enhance the feasibility of electric buses during winter months. The project included an assessment of current heating standards and solicited passenger feedback on bus heating. This article presents the study's findings, which comprised three segments: an evaluation of existing solutions, a survey capturing passenger opinions, and an observational study correlating passenger behaviour with weather conditions and onboard temperatures. The survey results indicated generally favourable public attitudes towards green transportation, albeit as long as they did not have to additionally pay for it. Awareness of heating as a contributing factor to increased expenses was relatively low, with only one-fifth of respondents acknowledging it. When interviewed, most passengers rated the onboard temperature as ‘acceptable’ or ‘too warm’. On the other hand, the onboard observations showed, that when riding a heated bus on a winter day less than 20 % of the passengers removed their winter accessories or loosened their overcoats inside the heated buses, despite the interior temperature being up to 20 °C warmer than outside. One would expect that would lead to health risks due to excessive sweating resulting from overheating. In conclusion, it has been ascertained that to date, minimal research has been conducted on strategies to reduce heating costs in electric buses. Further investigation is essential to explore the array of options, both technical and regulatory, to optimize heating systems in line with the electrification of public bus fleets.

The rapid electrification of transportation necessitates the development of intelligent energy management solutions to ensure the efficient operation of electric vehicle (EV) fleets while maintaining grid stability. Traditional energy balancing methods primarily target large-scale power systems, leaving small-scale energy storage aggregators, such as distributed EV charging hubs — without effective optimization frameworks. This paper presents a data-driven approach to optimizing fleet electrification and energy balancing through an intelligent IT system that integrates distributed energy storage with real-time charging coordination. We propose a mathematical optimization model that dynamically adjusts charging strategies based on energy availability, grid demand, and fleet operational requirements. By integrating ISO 15118 communication standards with advanced energy balancing algorithms, our system enables real-time interaction between EVs and the power grid, allowing vehicles to dynamically adjust their charging rates based on grid conditions and electricity pricing. ISO 15118 facilitates seamless authentication and bidirectional energy transfer, while energy balancing algorithms optimize fleet-wide charging schedules to distribute load efficiently. This synergy reduces peak demand stress by preventing simultaneous high-power charging events and minimizes charging costs by leveraging off-peak tariffs and vehicle-to-grid (V2G) capabilities. The proposed model is validated through simulation-based case studies, demonstrating up to a 20-30% improvement in energy efficiency and a reduction in grid dependency compared to conventional unmanaged charging strategies. The results highlight the potential of IT-driven energy balancing solutions in enhancing the sustainability and cost-effectiveness of fleet electrification. This research contributes to the broader transition toward decentralized energy management, offering actionable insights for fleet operators, energy providers, and policymakers. Future work will explore real-world deployment and machine learning-driven predictive optimization to further enhance system performance.

The transition from internal combustion engine (ICE) fleets to zeroemission vehicles (ZEVs) is a critical step toward achieving corporate sustainability and regulatory compliance with European Union (EU) decarbonization targets. However, many businesses face significant challenges in fleet electrification, including high upfront costs, uncertain operational feasibility, and inadequate charging infrastructure. Traditional fleet transition approaches often rely on general estimations rather than data-driven strategies tailored to specific fleet usage patterns and business constraints. This paper explores the current state of corporate fleet electrification, highlighting key barriers and the limitations of existing transition models. It emphasizes the critical role of data analytics, predictive modelling, and optimization techniques in facilitating costeffective and operationally viable fleet transformation. By leveraging real-world fleet operation data from carmonitor.eu – such as trip duration, energy consumption, and route characteristics — combined with external factors like vehicle market availability and energy pricing, businesses can develop informed transition strategies that minimize financial risk and maximize sustainability benefits. We propose a framework for a datadriven decision-support system that integrates fleet monitoring, scenario simulations, and tailored recommendations for electrification. This system aims to provide corporate decision-makers with actionable insights that optimize vehicle replacement strategies, charging infrastructure planning, and long-term fleet efficiency. The paper concludes by outlining key research directions and the next steps toward developing a robust, scalable tool for fleet transition optimization. Through this approach, businesses can make informed, strategic decisions that accelerate their path toward zero-emission mobility while ensuring economic and operational feasibility.

The transition from internal combustion engine (ICE) fleets to zero-emission vehicles (ZEVs) is a critical step toward achieving corporate sustainability and regulatory compliance with European Union (EU) decarbonisation targets. However, many businesses face significant challenges in fleet electrification, including high upfront costs, uncertain operational feasibility, and inadequate charging infrastructure. Traditional fleet transition approaches often rely on general estimations rather than data-driven strategies tailored to specific fleet usage patterns and business constraints. In this paper we propose a framework for a data-driven decision-support system that integrates fleet monitoring based on carmonitor.eu database, scenario simulations, and tailored recommendations for fleet electrification. This system will aim to provide corporate decision-makers with actionable insights that optimise vehicle replacement strategies, charging infrastructure planning, and long-term fleet efficiency. The paper outlines key framework parameters and the next steps toward developing a robust, scalable tool for fleet transition optimisation. Through this approach, businesses can make informed, strategic decisions that accelerate their path toward zero-emission mobility while ensuring economic and operational feasibility.

This study examines the shared electric scooter usage patterns in Riga across 2021 and 2022 and assesses the potential for reducing greenhouse gas emissions in the city’s urban transport system. Analysing 3.9 million scooter trips over the two-year period, the research highlights a tripling in the use of shared scooters and a shift towards shorter rides, resulting in an average use of only 7 km per day per scooter. This trend has resulted in CO ₂ emissions to 54.16 g CO ₂ per passenger kilometre, raising the total emissions from shared scooters in 2022 to 163.3 tonnes, and in fact increasing the net transportation CO ₂ emissions in Riga by 49.2 tonnes, by largely replacing zero-emission modes of transport like walking. In response to these challenges, the research explores the implementation of photovoltaic (PV) energy-powered charging docks as a method to maintain scooter operations. While energy consumption is not the largest contributor to the scooters’ CO ₂ emissions, by also eliminating the need for scooter transportation for charging the adoption of on-site generated PV energy could yield a substantial CO ₂ emission reduction of up to 34 %. This approach could hypothetically result in a 71-tonne reduction in CO ₂ emissions from electric scooters, thus potentially establishing scooters as a net positive influence to Riga’s transport emissions, producing a net 11 tonne CO ₂ emisssion reduction.