Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU

Making and Breaking Impasses in International Regimes. The WTO, Seattle and Doha

WTO; international agreements; international trade; governance

Fuzzy Logic Based Negotiation in E-Commerce

The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

Re-Politicising Regulation: Politics: Regulatory Variation and Fuzzy Liberalisation in the Single European Energy Market

[From the introduction] The idea that we are living in the age of the regulatory state has dominated the study of public policy in the European Union and its member states in general, and the study of the utilities sectors in particular.1 The European Commission’s continuous drive to expand the Single Market has therefore been a free-market and rule-oriented project, driven by regulatory politics rather than policies that involve direct public expenditure. The dynamics of European integration are rooted in three central concepts: free trade, multilateral rules, and supranational cooperation. During the 1990s EU competition policy took a ‘public turn’ and set its sights on the public sector.2 EU legislation broke up national monopolies in telecommunications, electricity and gas, and set the scene for further extension of the single market into hitherto protected sectors. Both the integration theory literature (intergovernmentalist and institutionalist alike) and literature on the emergence of the EU as a ‘regulatory state’ assumed that this was primarily a matter of policy making: once agreement had been reached to liberalise the utilities markets a relatively homogeneous process would follow. The regulatory state model fit the original common market blueprint better the old industrial policy approaches. On the other hand, sector-specific studies continue to reveal a less than fully homogeneous internal market. The EU has undergone momentous changes in the last two decades, which have rendered the notion of a homogeneous single market somewhat unrealistic

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

The Messy Nuclear Landscape: Using Fuzzy Cognitive Mapping to Explore Plausible Nuclear Disarmament Scenarios

Nuclear weapons are seemingly permanent fixtures in international relations. Although nuclear abolitionists and actors within the Nonproliferation Treaty (NPT) and the International Atomic Energy Agency (IAEA) have taken significant steps towards designing a world without nuclear weapons, the longstanding realist logic that suggests nuclear disarmament is nonviable has born more fruit. On the other hand, some proponents of realism have suggested global nuclear disarmament is feasible, given that certain international instabilities are stabilized and that special care is taken during diplomatic negotiations. This presents an opportunity to test these predictions using fuzzy cognitive mapping, a computational modeling technique that identifies problems, their stakeholders, and stakeholders’ components in order to determine scenarios that solve complex disputes in ways that benefit the system as a whole. This study identifies two problems regarding nuclear disarmament. First, nuclear weapon states are resistant to giving up nuclear weapons, despite agreements to disarm. This problem follows realist logic. Second, the role that the IAEA plays in safeguarding special nuclear materials while guaranteeing states’ rights to nuclear technology is contrasted by states’ interest in maintaining levels of secrecy. These two problems constitute a “mess” that this study analyzes. Synthesis between the problems requires that solving one does not make the other worse. Therefore, this study tests various scenarios and finds that, given present-day international instabilities are stabilized, nuclear disarmament is feasible if three conditions are met: First, a global disarmament agreement must not unreasonably affect states’ sovereign rights outside of the agreement. Second, states outlying the NPT must be brought into the negotiations. Finally, present states with nuclear arsenals adopt the IAEA’s Comprehensive Safeguards Agreement with the Additional Protocol as a measure of good faith. A final factor suggests states’ rights to the technology inevitably means states should have an ability to re-proliferate in the event of future international instability that threatens global security. Once these steps are taken and technological rights guaranteed, this model suggests global nuclear disarmament is possible

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach

This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent quantified to form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we devise a transformed vehicle-cargo matching optimization model by combining the fuzzy set's membership, non-membership, and uncertainty information. Through an adaptive interactive algorithm, the matching scheme with fairness concerns is solved using CPLEX. The effectiveness of the proposed matching mechanism in securing high levels of satisfaction is established by comparison with three benchmark methods. To further investigate the impact of considering fairness in vehicle-cargo matching, a shipper-carrier-platform tripartite evolutionary game framework is developed under the waiting response time cost (WRTC) sharing mechanism. Simulation results show that with fairness concerns in vehicle-cargo matching, all stakeholders are better off: The platform achieves positive revenue growth, and shippers and carriers receive positive subsidy. This study offers both theoretical insights and practical guidance for the long-term and stable operation of the on-line freight stowage industry.Comment: 36 pages, 15 figure