Transition to electric buses networks: a mixed-fleet approach

As the environmental concerns increase, companies are called upon to adopt environmentally friendly solutions in their operations. Since the transportation sector counted for about ¼ of the global carbon dioxide emissions in 2010, transport providers agencies have been aiming to incorporate electric vehicles in their operations. This trend is observable in bus urban networks. In order to electrify the bus fleet and make the respective fleet planning decisions, it is necessary to address the necessary infrastructure requirements considering some operational constraints and the company´s objectives. This dissertation proposes a tool based on an optimization model, the MixedBusFleet, that aims to support transport providers to achieve the electrification of their fleets by minimizing investment costs, operational costs and the external costs of emissions. The MixedBusFleet model considers: (i) location of charging station; (ii) frequency of charging; (iii) charging strategy; (iv) battery type; (v) fleet dimension and; (vi) an emissions factor. The literature analyzed throughout this study identified that there is no previous work that incorporates all these planning objectives in one approach. Therefore, the proposed model aims to fill this gap. To illustrate the potential of the model, it was applied to part of the network of a public transport company operating in Lisbon. The case study comprised of 17 bus routes with predefined demand. The results showed that 133 buses are required to serve all the demand requiring a total investment of €24 950 000 and 5 fast charging facilities were installed in final stops.À medida que as preocupações ambientais aumentam, as empresas são incentivadas a adotar soluções ecológicas nas suas operações. O setor de transporte foi responsável por cerca de ¼ das emissões globais de dióxido de carbono em 2010, então, as empresas de transportes têm procurado incorporar veículos elétricos nas suas operações. Para eletrificar uma frota de autocarros e tomar as respetivas decisões de planeamento, é necessário considerar as necessidades de infraestruturas, algumas restrições operacionais e os objetivos da empresa. Esta dissertação propõe uma ferramenta baseada num modelo de otimização, o MixedBusFleet, que visa apoiar as empresas na eletrificação das suas frotas, minimizando os custos de investimento, custos operacionais e os custos externos de emissões. O modelo MixedBusFleet considera: (i) localização da estação de carregamento; (ii) frequência de carregamento; (iii) estratégia de carregamento; (iv) tipo de bateria; (v) dimensão da frota e; (vi) um fator de emissão. A literatura analisada ao longo deste projeto não identificou estudos que incorporassem todos os objetivos de planeamento indicados numa abordagem. O modelo proposto visa então, preencher essa lacuna. Para ilustrar a o potencial do modelo, o mesmo foi aplicado a parte de uma rede de transporte público em Lisboa. O estudo de caso é composto por 17 rotas de autocarros com procura pré-definida. Os resultados revelaram que são necessários 133 autocarros para satisfazer a totalidade da procura, o que requer um investimento total de €24 950 000 e que é necessária a instalação de 5 estratégias de carregamento rápido em paragens

Secure and cost-effective operation of low carbon power systems under multiple uncertainties

Power system decarbonisation is driving the rapid deployment of renewable energy sources (RES) like wind and solar at the transmission and distribution level. Their differences from the synchronous thermal plants they are displacing make secure and efficient grid operation challenging. Frequency stability is of particular concern due to the current lack of provision of frequency ancillary services like inertia or response from RES generators. Furthermore, the weather dependency of RES generation coupled with the proliferation of distributed energy resources (DER) like small-scale solar or electric vehicles permeates future low-carbon systems with uncertainty under which legacy scheduling methods are inadequate. Overly cautious approaches to this uncertainty can lead to inefficient and expensive systems, whilst naive methods jeopardise system security. This thesis significantly advances the frequency-constrained scheduling literature by developing frameworks that explicitly account for multiple new uncertainties. This is in addition to RES forecast uncertainty which is the exclusive focus of most previous works. The frameworks take the form of convex constraints that are useful in many market and scheduling problems. The constraints equip system operators with tools to explicitly guarantee their preferred level of system security whilst unlocking substantial value from emerging and abundant DERs. A major contribution is to address the exclusion of DERs from the provision of ancillary services due to their intrinsic uncertainty from aggregation. This is done by incorporating the uncertainty into the system frequency dynamics, from which deterministic convex constraints are derived. In addition to managing uncertainty to facilitate emerging DERs to provide legacy frequency services, a novel frequency containment service is designed. The framework allows a small amount of load shedding to assist with frequency containment during high RES low inertia periods. The expected cost of this service is probabilistic as it is proportional to the probability of a contingency occurring. The framework optimally balances the potentially higher expected costs of an outage against the operational cost benefits of lower ancillary service requirements day-to-day. The developed frameworks are applied extensively to several case studies. These validate their security and demonstrate their significant economic and emission-saving benefits.Open Acces

Reinforcement learning for EV charging optimization : A holistic perspective for commercial vehicle fleets

Recent years have seen an unprecedented uptake in electric vehicles, driven by the global push to reduce carbon emissions. At the same time, intermittent renewables are being deployed increasingly. These developments are putting flexibility measures such as dynamic load management in the spotlight of the energy transition. Flexibility measures must consider EV charging, as it has the ability to introduce grid constraints: In Germany, the cumulative power of all EV onboard chargers amounts to ca. 120 GW, while the German peak load only amounts to 80 GW. Commercial operations have strong incentives to optimize charging and flatten peak loads in real-time, given that the highest quarter-hour can determine the power-related energy bill, and that a blown fuse due to overloading can halt operations. Increasing research efforts have therefore gone into real-time-capable optimization methods. Reinforcement Learning (RL) has particularly gained attention due to its versatility, performance and real- time capabilities. This thesis implements such an approach and introduces FleetRL as a realistic RL environment for EV charging, with a focus on commercial vehicle fleets. Through its implementation, it was found that RL saved up to 83% compared to static benchmarks, and that grid overloading was entirely avoided in some scenarios by sacrificing small portions of SOC, or by delaying the charging process. Linear optimization with one year of perfect knowledge outperformed RL, but reached its practical limits in one use-case, where a feasible solution could not be found by the solver. Overall, this thesis makes a strong case for RL-based EV charging. It further provides a foundation which can be built upon: a modular, open-source software framework that integrates an MDP model, schedule generation, and non-linear battery degradationElektrifieringen av transportsektorn är en nödvändig men utmanande uppgift. I kombination med ökande solcellsproduktion och förnybara energikällor skapar det ett dilemma för elnätet som kräver omfattande flexibilitetsåtgärder. Dessa åtgärder måste inkludera laddning av elbilar, ett fenomen som har lett till aldrig tidigare skådade belastningstoppar. Ur ett kommersiellt perspektiv är incitamentet att optimera laddningsprocessen och säkerställa drifttid. Forskningen har fokuserat på realtidsoptimeringsmetoder som Deep Reinforcement Learning (DRL). Denna avhandling introducerar FleetRL som en ny RL-miljö för EV-laddning av kommersiella flottor. Genom att tillämpa ramverket visade det sig att RL sparade upp till 83% jämfört med statiska riktmärken, och att överbelastning av nätet helt kunde undvikas i de flesta scenarier. Linjär optimering överträffade RL men nådde sina gränser i snävt begränsade användningsfall. Efter att ha funnit ett positivt business case för varje kommersiellt användningsområde, ger denna avhandling ett starkt argument för RL-baserad laddning och en grund för framtida arbete via praktiska insikter och ett modulärt mjukvaruramverk med öppen källko

Optimization methods for developing electric vehicle charging strategies

Electric vehicles (EVs) are considered to be a crucial and proactive player in the future for transport electrification, energy transition, and emission reduction, as promoted by policy-makers, relevant industries, and the academia. EV charging would account for a non-negligible share in the future electricity demand. The integration of EV brings both challenges and opportunities to the electricity system, mainly from their charging profiles. When EV charging behaviors are uncontrolled, their potentially high charging rate and synchronous charging patterns may result in the bottleneck of the grid capacity and the shortage of generation ramping capacity. However, the promising load shifting potential of EVs can alleviate these problems and even bring additional flexibilities to the demand side for further applications, such as peak shaving and the integration of renewable energy. To grasp these opportunities, novel controlled charging strategies should be developed to help integrate electric vehicles into energy systems. However, corresponding methods in current literature often have customized assumptions or settings so that they might not be practically or widely applied. Furthermore, the attention of literature is more paid to explaining the results of the methods or making consequent policy recommendations, but not sufficiently paid to demonstrating the methods themselves. The lack of the latter might undermine the credibility of the work and hinder readers’ understanding. Therefore, this thesis serves as a methodological framework in response to the fundamental and universal challenges in developing charging strategies for integrating EV into energy systems. The discussions aim to raise readers’ awareness of the essential but often unnoticed concerns in model development and hopefully would enlighten future researchers into this topic. Specifically, this cumulative thesis comprises four papers and analyzes the research topic from two perspectives. With Paper A and Paper B, the micro perspective of the thesis is more applied and focuses on the successful implementation of charging scheduling solutions for each EV individually. Paper A proposes a two-stage scenario-based stochastic linear programming model to schedule EV charging behaviors and considers the uncertainties from future EVs. The model is calculated in a rolling window fashion with updated parameters. Scenario generation for future EVs is simulated by inhomogeneous Markov chains, and scenario reduction is achieved by a fast forward selection method to reduce the computational burden. The objective function is formulated as variance minimization so that the model can be flexibly implemented for various applications. Paper B applies the model proposed in Paper A to investigate how the generation of a wind turbine could be correlated with the EV controlled charging demand. An empirical controlled charging strategy is designed for comparison where EVs would charge as much as possible when wind generation is sufficient or would postpone charging otherwise. Although these two controlled charging strategies perform similarly in terms of wind energy utilization, the solutions from the proposed model could additionally alleviate the volatility of wind energy generation by matching the EV charging curve to the electricity generation profile. With Paper C and Paper D, the macro perspective of the thesis is more explorative and investigates how EVs as a whole would contribute to energy transition or emission reduction. Paper C investigates the greenhouse gas emissions of EVs under different charging strategies in Europe in 2050. Methodologically, the paper proposes an EV module that enables different EV controlled charging strategies to be endogenously determined by energy system models. The paper concludes that EVs would contribute to a 36% emission reduction on the European level even under an uncontrolled charging strategy. Unidirectional and bidirectional controlled charging strategies could further reduce emissions by 4% and 11%, respectively, compared with the original level. As a follow-up study of Paper C, Paper D develops, demonstrates, improves, and compares three different types of EV aggregation methods for integrating an EV module into energy system models. The analysis and demonstration of these methods are achieved by having a simplified energy system model as a testbed and the results from the individual EV modeling method as the benchmark. As different EV aggregation methods share the same data set as for the individual EV modeling method, the disturbance from parameters is minimized, and the influence from mathematical formulations is highlighted. These EV aggregation methods are compared from multiple aspects

Improving smart charging for electric vehicle fleets by integrating battery and prediction models

With increasing electrification of vehicle fleets there is a rising demand for the effective use of charging infrastructure. Existing charging infrastructures are limited by undersized connection lines and a lack of charging stations. Upgrades require significant financial investment, time and effort. Smart charging represents an approach to making the most of existing charging infrastructure while satisfying charging needs. Smart charging involves scheduling for electric vehicles (EVs). In other words, smart charging approaches decide which EV may charge at which charging station and at which current during which time periods. Planning flexibility is determined by the length of stay and the available electrical supply. First, we present an approach for smart charging combining day-ahead planning with real-time planning. For day-ahead planning, we use a mixed integer programming model to compute optimal schedules while making use of information available ahead of time. We then describe a schedule guided heuristic which adapts precomputed schedules in real-time. Second, we address uncertainty in smart charging. For example, EV departure times are an important component in prioritization but are uncertain ahead of time. We use a regression model trained on historical data to predict EV departure times. We integrate predictions directly in the smart charging heuristic used in the first approach. Experimental results show a more accurate EV departure time leads to a more accurate EV prioritization and a higher amount of delivered energy. Third, we present two approaches which allow the smart charging heuristic to take EV charging behavior into account. In practice, EVs charge using nonlinear charge profiles where power declines towards the end of each charging process. There is thus a gap between the scheduled power and the actual charging power if nonlinear charge profiles are not taken into account. The first approach uses a traditional equivalent circuit model (ECM) to model EV charging behavior but in practice is limited by the availability of battery parameters. The second approach relies on a regression model trained on historical data to directly predict EV charging profiles. In each of the two approaches, the model of the EV's charging profile is directly integrated into the smart charging heuristic which allows the heuristic to produce more accurate charge plans. Experimental results show EVs charge significantly more energy because the charging infrastructure is used more effectively. Finally, we present an open source package containing the smart charging heuristic and describe results from applying the heuristic in a one-year field test. Experimental results from the field test show EVs at six charging stations can be scheduled for charging when the grid connection only allows two EVs to charge concurrently. Runtime measurements demonstrate the heuristic is applicable in real time and scales to large fleet sizes