Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.Comment: 14 pages, 19 figures, IEEE Transactions on Vehicular Technology ( Volume: 68 , Issue: 5 , May 2019

Improvements to transmission expansion planning and implementation :treating uncertainty in commercial operation dates and increasing aunction efficiency

Three proposals contributing to the electricity transmission expansion planning and implementation process are presented in this thesis. The first proposal refers to the use of combinatorial and simultaneous descending auctions to treat the exposure problem and increase the efficiency of multi-item transmission auctions. A simulation framework to quantify potential benefits of using these auctions protocols, for transmission companies and grid users, is proposed. The second proposal refers to an expansion planning methodology that explicitly accounts for uncertainties in facility implementation times while determining the capacity additions and their optimal implementation schedule. In the third proposal, principal-agent theoretic concepts are applied to develop a methodology for the optimal design of winner-selection and risksharing mechanisms, with the goal of managing uncertainties in implementation times of transmission facilities, when competitive processes are used to select the agents to which concessions to implement and operate these facilities are awarded. Classical optimization approaches, notably mixed-integer linear programming, are used in the mathematical formulations that underlie the simulation and analyses carried out for all three proposals; and qualitative conclusions aiming at aiding planners and regulators are drawn from the quantitative results of case studies.Esta tese apresenta três contribuições ao planejamento e implantação da expansão da transmissão. Primeiro, propõe-se usar leilões combinatórios e leilões descendentes simultâneos para tratar o problema da exposição em leilões multi-itens de concessões de transmissão, aumentando a eficiência destes leilões, e apresenta-se um arcabouço de simulação para quantificar os benefícios potencias do uso de tais protocolos. Segundo, propõe-se uma metodologia de planejamento da expansão que considera explicitamente incertezas em tempos de implantação de instalações da transmissão ao determinar as adições de capacidade e as datas de início de implantação de ativos. Terceiro, aplica-se conceitos da teoria do agente-principal para propor uma abordagem para otimizar o desenho de mecanismos de seleção do vencedor e de partilha de riscos, de modo a gerir incertezas em tempos de implantação de ativos, no contexto em que mecanismos competitivos são utilizados para selecionar os agentes a que contratos de transmissão implantação são concedidos. Para todas as três propostas, utiliza-se abordagens de otimização clássica, notadamente programação inteira linear mista, para a formulação matemática que subsidia simulações e análises; e retira-se dos resultados numéricos de estudos de casos conclusões qualitativas que subsidiem planejadores e reguladores