• Energy Research

Electric vehicles (EVs) offer significant opportunities to improve sustainability of the road transport sector. But simultaneously, widespread adoption of EVs would create new challenges. For example, spatiotemporal concentration of charging events in high-density residential or commercial areas would place extreme demands on the power network, causing bottlenecks and grid instability. A novel approach to the typical decentralized control methods for EV charging service providers (CSPs) is presented. First, static price signals based on anticipated demand define a set of charging offers, targeted to segments of EV users. Prices are differentiated either only by time or both by time and place and allow comparison and evaluation of both scenarios. A choice-based revenue management method is employed to optimize allocation of generated charging offers, with respect to revenue outcome for the CSP. The charging coordination techniques are demonstrated through simulation. Data come from the London Travel Demand Survey and particularly trips around Westfield, one of Europe's largest urban shopping malls, representing out-of-home charging behavior for short intervals in a high-demand area. Findings suggest that in a first-come, first-served system, locational pricing might create opportunities both for increased revenue and for relocation of charging events to less-congested facilities. In the revenue management system, locational pricing significantly favors total revenue outcome but without discharging vulnerable areas. However, because agents with conflicting interests participate in the process (infrastructure owners, power system operators, EV drivers), opportunity exists for the CSP to adapt constraints according to the priority of its objectives.

Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context.

Abstract Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified – technology design, building design, urban climate, systems design, and policy assessment – each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date. Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

Abstract The sophistication in the demand management approaches in both transport and energy sectors and their interaction call for modelling approaches that consider both sectors jointly. For agent-based microsimulation models of travel demand and energy consumption, this implies the necessity to ensure consistent representation of user behaviour with respect to mobility and energy consumption behaviours across the model components. Therefore this paper proposes a microeconomic framework, termed the HOT model (Home, Out-of-home, Travel) grounded in the goods-leisure paradigm, but extended to incorporate emerging activity-travel behaviour patterns and their energy consumption implications. We discuss how the model can be operationalised and embedded within agent-based frameworks with a case study using time use and energy consumption data from the UK.

Transport-energy transitions pose complex challenges that have been extensively studied in high-income countries in response to national mandates for climate action. Low- and middle-income countries, however, have low but rapidly growing motorisation rates and face very different challenges in adopting new technologies to foster economic development and ensure equitable access to clean transportation. Here, we present a set of narrative scenarios for the future of the Kenyan transport-energy system co-developed through engagement with 41 local experts and decision-makers. Through the co-development of a Kenyan transport-energy system model, we present a decision-support tool, populated with those scenarios, to assist policymakers at regional, national and international levels in building policy and investment pipelines to support low-carbon economic growth. We find that Kenya’s transport-energy system can meet both development and climate goals, but this demands strong policy support for efficient public transport and targeted support for road vehicle electrification. Increased support for non-motorised transport is essential to provide equitable access to services and economic opportunities. Favourable pathways result in significant e-mobility uptake, which is anticipated to increase electricity demand by 5%–56% from 2023 to 2040, relative to the IEA Kenya Energy Outlook’s Stated Policies scenario, representing a 2.7–3.9x increase in Kenya’s total electricity demand over the same period. From a macro-fiscal perspective, results show that e-mobility has two important consequences for Kenya. Firstly, under high e-mobility scenarios, there is a negative fiscal impact that taxation revenues from the sale of transport fuels reduce by up to 41% relative to the low e-mobility scenario (though, notably, they still increase marginally from the 2023 level because of increasing transport demand). Secondly, high e-mobility scenarios have a positive impact on balance of payments by reducing the fuel import bill by up to 69% relative to the low e-mobility baseline. This corresponds to a reduction in foreign exchange requirement of up to $4.2bn annually by 2050.

The ability to accurately model and predict timing and duration of activities for different individuals is essential for successful and widespread Demand Side Response (DSR) policies, especially in the residential sector. Understanding what people do during the day and what factors influence their activity participation decisions is important for planning an effective DSR strategy to harness the end-user flexibility. The recent Covid-19 pandemic has shown how much activities can be shifted to a virtual mode in the presence of mobility restrictions. Further, participation in activities via digital devices (virtual activity participation) has spread across society. Such virtual activities, including teleworking, online shopping, and virtual social interactions, are observed to explicitly impact travel behaviour and activity scheduling. And yet, activity-based models of mobility and energy demand do not accommodate the trade-offs between activity types, location and virtual activity participation. This paper presents a model of activity participation that captures the relationship between the three dimensions of: activity type (such as work, study, shopping), activity location (in-home, out-of-home), and activity modality (in-person, virtual). A Multiple-Discrete Continuous Extreme Value (MDCEV) model structure is applied, and the empirical analysis is undertaken using the 2015 United Kingdom Time Use Survey (UKTUS). The model results provide insights for better understanding of the trade-offs made by individuals as they participate in and allocate time across a set of activity type-location-modality alternatives, and the heterogeneity in these trade-offs. Further, holdout sample validation and policy scenario analysis exercises are presented to demonstrate the reliability and suitability of the model for policy implications. The empirical results presented in our paper suggest that this framework embedded in an activity and agent-based simulator of energy demand will enable us to test a variety of scenarios related to end-user flexibility and activity behaviour change, including consideration of virtual activity participation trends.

AbstractIn the literature electric vehicle use is modelled using of a variety of approaches in power systems, energy and environmental analyses as well as in travel demand analysis. This paper provides a systematic review of these diverse approaches using a twofold classification of electric vehicle use representation, based on the time scale and on substantive differences in the modelling techniques. For time of day analysis of demand we identify activity-based modelling (ABM) as the most attractive because it provides a framework amenable for integrated cross-sector analyses, required for the emerging integration of the transport and electricity network. However, we find that the current examples of implementation of AMB simulation tools for EV-grid interaction analyses have substantial limitations. Amongst the most critical there is the lack of realism how charging behaviour is represented.