Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Optimal pose selection for the calibration of an overconstrained Cable-Driven Parallel Robot

In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.

Optimal energy management of smart households with electric vehicles, energy storage and distributed generation

In this thesis, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered for each household . Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to export excessive energy to other households, respectively.