On an Information and Control Architecture for Future Electric Energy Systems

This paper presents considerations towards an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification. This paper describes the new requirements and challenges of an extended information and control architecture that need to be addressed for continued reliable delivery of electricity. It identifies several new actionable information and control loops, along with their spatial and temporal scales of operation, which can together meet the needs of future grids and enable deep decarbonization of the electricity sector. The present architecture of electric power grids designed in a different era is thereby extensible to allow the incorporation of increased renewables and other emerging electric loads.Comment: This paper is accepted, to appear in the Proceedings of the IEE

Frontiers In Operations Research For Overcoming Barriers To Vehicle Electrification

Electric vehicles (EVs) hold many promises including diversification of the transportation energy feedstock and reduction of greenhouse gas and other emissions. However, achieving large-scale adoption of EVs presents a number of challenges resulting from a current lack of supporting infrastructure and difficulties in overcoming technological barriers. This dissertation addresses some of these challenges by contributing to the advancement of theories in the areas of network optimization and mechanism design. To increase the electric driving range of plug-in hybrid electric vehicles (PHEVs), we propose a powertrain energy management control system that exploits energy efficiency dif- ferences of the electric machine and the internal combustion engine during route planning. We introduce the Energy-Efficient Routing problem (EERP) for PHEVs, and formulate this problem as a new class of the shortest path problem. We prove that the EERP is NP-complete. We then propose two exact algorithms that find optimal solutions by exploiting the transitive structure inherent in the network. To tackle the intractability of the problem, we proposed a Fully Polynomial Time Approximation Scheme (FPTAS). From a theoretic perspective, the proposed two-phase approaches improve the state-of-the-art to optimally solving shortest path problems on general constrained multi-graph networks. These novel approaches are scalable and offer broad potential in many network optimization problems. In the context of vehicle routing, this is the first study to take into account energy efficiency difference of different operating modes of PHEVs during route planning, which is a high level powertrain energy management procedure. Another challenge for EV adoption is the inefficiency of current charging systems. In addition, high electricity consumption rates of EVs during charging make the load manage- ment of micro grids a challenge. We proposed an offline optimal mechanism for scheduling and pricing of electric vehicle charging considering incentives of both EV owners and utility companies. In the offline setting, information about future supply and demand is known to the scheduler. By considering uncertainty about future demand, we then designed a family of online mechanisms for real-time scheduling of EV charging. A fundamental problem with significant economic implications is how to price the charging units at different times under dynamic demand. We propose novel bidding based mechanisms for online scheduling and pricing of electric vehicle charging. The proposed preemption-aware charging mechanisms consider incentives of both EV drivers and grid operators. We also prove incentive-compatibility of the mechanisms, that is, truthful reporting is a dominant strategy for self-interested EV drivers. The proposed mechanisms demonstrate the benefits of electric grid load management, revenue maximization, and quick response, key attributes when providing online charging services

Top-down sustainability transitions in action: How do incumbent actors drive electric mobility diffusion in China, Japan, and California?

In explaining how socio-technical transitions occur, prevailing theories focus on bottom-up processes driven by new entrants, diverse actors and open-ended exploration in small, protected niches. Incumbent firms are frequently portrayed as hampering change, while managerial strategies using traditional public policy instruments remain understudied. Addressing this bias, we examine strategies used by networks of incumbent state and industry actors in China, Japan and California to accelerate the production and diffusion of battery-electric or hydrogen-powered vehicles. We build a comprehensive framework that systematically marries mechanisms of industrial transformation described in developmental-state literature with theories of socio-technical change from transitions scholarship. We then use a vast dataset of secondary documents and interviews to examine the principal strategies employed in each country, identifying variations over two phases of technological diffusion. Findings reveal that the incumbent actor networks in each country have collectively employed multiple but similar strategies. Yet closer inspection of specific policy instruments, such as regulations and performance-based incentives, along with ambitions to phase out vehicles with internal combustion engines, reveals differences across cases. We explain these by considering different motivations for each country’s transition and influencing socio-political conditions. Our study contributes to the enrichment of future transitions research in at least two ways. Theoretically, by integrating literature on transitions and developmental states, we deepen understanding of how incumbent state and market actors can attempt to drive socio-technical change. Empirically, our analysis provides important evidence for understanding the strategies driving top-down transitions outside northern Europe, and the conditions affecting instrument choice

Pricing and Electric Vehicle Charging Equilibria

We study equilibria in an Electric Vehicle (EV) charging game, a cost minimization game inherent to decentralized charging control strategy for EV power demand management. In our model, each user optimizes its total cost which is sum of direct power cost and the indirect dissatisfaction cost. We show that, taking player specific price independent dissatisfaction cost in to account, contrary to popular belief, herding only happens at lower EV uptake. Moreover, this is true for both linear and logistic dissatisfaction functions. We study the question of existence of price profiles to induce a desired equilibrium. We define two types of equilibria, distributed and non-distributed equilibria, and show that under logistic dissatisfaction, only non-distributed equilibria are possible by feasibly setting prices. In linear case, both type of equilibria are possible but price discrimination is necessary to induce distributed equilibria. Finally, we show that in the case of symmetric EV users, mediation cannot improve upon Nash equilibria

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

Big Data and Electric Mobility

Nowadays Electric Vehicles are getting more and more important to address modern issues like pollution, economical transportation needs and more efficient and flexible ways of moving. In this thesis we focus on the assessment of an electrification rate of the major urban areas of Tuscany, by simulating the consumption of a real EV on millions of real users trajectories. We propose different usage scenarios, all regarding a different level of sophistication, this to make more reliable evaluations in different environmental conditions.Then, we generate the algorithms used for the simulations, and address the challenges met on the path, such as GPS data sampling and elevation extraction issues. Gli odierni veicoli elettrici stanno assumendo sempre più importanza come risposta a problemi quali l’inquinamento dell’aria, il bisogno di un mezzo di trasporto più economico e modalità di spostamento più efficienti e flessibili. In questa tesi focalizziamo l’attenzione all’individuazione di un tasso di elettrificabilità delle maggiori aree cittadine della Toscana, attraverso una simulazione del consumo di un reale veicolo elettrico su milioni di traiettorie di utenti reali. Proponiamo quindi differenti scenari di uso, tutti riguardanti differenti livelli di sofisticazione, in modo da generare valutazioni più precise al variare di specifiche condizioni.In seguito generiamo gli algoritmi utilizzati per la simulazione, risolvendo tutte le sfide incontrate sul cammino, come il sampling dei dati GPS e i problemi relativi all’estrazione dell’elevazione

Notes on Cloud computing principles

This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that Cloud Computing first and foremost is a new business model, a new model to sell computational resources, the understanding of these concepts is facilitated by treating them in unison. Here, we review some of the most important concepts and how they relate to each other