Assessing Potential Energy Savings in Household Travel: Methodological and Empirical Considerations of Vehicle Capability Constraints and Multi-day Activity Patterns.

The lack of multi-day data for household travel and vehicle capability requirements is an impediment to evaluations of energy savings strategies, since 1) travel requirements vary from day-to-day, and 2) energy-saving transportation options often have reduced capability. This work demonstrates a survey methodology and modeling system for evaluating the energy-savings potential of household travel, considering multi-day travel requirements and capability constraints imposed by the available transportation resources. A stochastic scheduling model is introduced – the multi-day Household Activity Schedule Estimator (mPHASE) – which generates synthetic daily schedules based on “fuzzy” descriptions of activity characteristics using a finite-element representation of activity flexibility, coordination among household members, and scheduling conflict resolution. Results of a thirty-household pilot study are presented in which responses to an interactive computer assisted personal interview were used as inputs to the mPHASE model in order to illustrate the feasibility of generating complex, realistic multi-day household schedules. Study vehicles were equipped with digital cameras and GPS data acquisition equipment to validate the model results. The synthetically generated schedules captured an average of 60 percent of household travel distance, and exhibited many of the characteristics of complex household travel, including day-to-day travel variation, and schedule coordination among household members. Future advances in the methodology may improve the model results, such as encouraging more detailed and accurate responses by providing a selection of generated schedules during the interview. Finally, the Constraints-based Transportation Resource Assignment Model (CTRAM) is introduced. Using an enumerative optimization approach, CTRAM determines the energy-minimizing vehicle-to-trip assignment decisions, considering trip schedules, occupancy, and vehicle capability. Designed to accept either actual or synthetic schedules, results of an application of the optimization model to the 2001 and 2009 National Household Travel Survey data show that U.S. households can reduce energy use by 10 percent, on average, by modifying the assignment of existing vehicles to trips. Households in 2009 show a higher tendency to assign vehicles optimally than in 2001, and multi-vehicle households with diverse fleets have greater savings potential, indicating that fleet modification strategies may be effective, particularly under higher energy price conditions.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91567/1/kevinb_1.pd

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

Technical report about sustainable urban freight solutions, part 1 of

Optimal speed trajectory and energy management control for connected and automated vehicles

Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

A chance-constrained approach for electric vehicle aggregator participation in the reserve market

As recorrentes preocupações ambientais têm levado a que se verifiquem alterações nos sistemas de energia, sendo que a aposta em sistemas distribuídos é já uma realidade, contudo a aposta em energia renovável está, normalmente, associada a intermitência no que diz respeito ao aproveitamento de energia proveniente do sol, vento, ondas, etc. Assim, neste paradigma, surgem os veículos elétricos que fazem parte do sistema e têm boas perspetivas de verem a sua penetração a aumentar exponencialmente num futuro próximo, pelo que devem ser estudadas novas formas de relação entre este recurso e os agentes de mercado. Neste momento, já é possível carregar e descarregar os veículos elétricos, o que, auxiliado pelas decisões e algoritmos corretos, pode contribuir com um apoio fundamental para a rede de energia tratar de forma simplificada congestionamentos e variações nos valores nominais de tensão e frequência. Tudo isto, está dependente da disponibilidade do proprietário do veículo para fornecer este tipo de serviço e que estados mínimos de carga sejam assegurados para as viagens diárias. Geralmente, os operadores da rede elétrica gerem elevados níveis de geração e carga, pelo que surge a necessidade do conceito de agregador de veículos elétricos, que terá a função de juntar vários veículos elétricos para assim corresponder de forma mais eficaz e com capacidade de oferta conforme as necessidades da rede.Neste trabalho, pretende-se otimizar o lucro do agregador, que poderá estar sujeito a penalidades em caso de falha no fornecimento de reserva a subir ou a descer, não colocando de parte a fiabilidade no fornecimento do serviço ao mercado de reserva. Deste modo, são utilizadas técnicas de otimização estocástica que pretendem modelar incertezas de uma pequena frota de veículos elétricos como disponibilidade para fornecer o serviço e perfis de consumo. A técnica de otimização chance-constrained, mais precisamente as relaxações Big-M e McCormick, são aplicadas a fim de analisar o risco da oferta que o agregador deve submeter em mercado.Para situações de mercado real, mais precisamente o FCR-N na Dinamarca, são explorados diversos cenários para diferentes níveis de risco submetidos em mercado, diferentes probabilidades de o veículo estar conectado à redeThe recurring environmental concerns have led to changes in energy systems, and the investment in distributed systems is in progress, however, the investment in renewable energy is usually associated with intermittent with regard to the use of energy from the sun, wind, waves, etc. Thus, within this scope, electric vehicles that are part of the system rises and stand good chances of seeing their penetration increase significantly in the near future, so new forms of relationship between this resource and the market agents should be addressed. At this point, it is already possible to charge and discharge electric vehicles, which, aided by correct decisions and algorithms, can contribute with a fundamental support for the energy network to deal in a simplified way with congestion and fluctuations in voltage and frequency. It all depends on the willingness of the vehicle owner to provide this type of service and that minimum states of charge are ensured for the daily trips. In general, electricity grid operators manage high power generation and load capacities, so there is a demand for the concept of electric vehicle aggregator, which will have the function to bring together several electric vehicles to correspond more effectively and with supply capacity according to the requirements of the grid.The aim of this work is to optimise the profit of the aggregator, which may be subject to penalties in the event of a failure in the provision of reserve upward or downward, regardless of the reliability in the provision of the service to the reserve market. Therefore, stochastic optimization techniques are used to model uncertainties of a small fleet of electric vehicles as availability to provide the service and consumption profiles. The optimization technique chance-constrained, namely Big-M and McCormick the relaxation methods, are applied in order to analyze the risk the aggregator must submit in the market. For real market cases, specifically the FCR-N in Denmark, several scenarios are analyzed for different levels of risk submitted in the market, different probabilities of the vehicle being connected to the grid

Coordinated Charging of Electric Vehicles for Congestion Prevention in the Distribution Grid

Distributed energy resources (DERs), like electric vehicles (EVs), can offer valuable services to power systems, such as enabling renewable energy to the electricity producer and providing ancillary services to the system operator. However, these new DERs may challenge the distribution grid due to insufficient capacity in peak hours. This paper aims to coordinate the valuable services and operation constraints of three actors: the EV owner, the Fleet operator (FO) and the Distribution system operator (DSO), considering the individual EV owner’s driving requirement, the charging cost of EV and thermal limits of cables and transformers in a distribution grid capacity market framework. Firstly, a theoretical market framework is described. Within this framework, FOs who represent their customer’s (EV owners) interests will centrally guarantee the EV owners’ driving requirements and procure the energy for their vehicles with lower cost. The congestion problem will be solved by a coordination between DSO and FOs through a distribution grid capacity market scheme. Then, a mathematical formulation of the market scheme is presented. Further, some case studies are shown to illustrate the effectiveness of the proposed solutions