The integration of Electric Vehicles (EVs) equipped with Vehicle-to-Grid (V2G) technology has been recognized as a promising solution to enhance the flexibility of modern power distribution systems. However, to make this a reality, there is a need for flexible, reliable, and highly scalable computing architectures that can coordinate the charging and discharging process of EV fleets while identifying the best trade-offs between the vehicle charging expectations and the grid requirements. This challenging problem has been formalized in this paper as a welfare maximization problem, by defining proper utility functions to quantify the marginal cost/benefits of the charging and discharging process. Then, a self-organizing solution scheme is proposed in the task of enabling the EVs fleet to solve the welfare maximization problem by only exchanging local non-sensitive information. The insight is to define a formal connection between the charging/discharging scheduling problem and the economic dispatch of thermal generators, hence exploiting all the vast literature on the theory of the marginal cost for the decentralized solution of the scheduling problem, which is one of the main contributions of this paper. Simulation results obtained on two case studies are presented and discussed in order to assess the effectiveness of the proposed technique in solving complex scheduling problems in realistic operation scenarios.

The optimization of the efficiency of wind turbine systems is a fundamental task, from the perspective of a growing share of electricity produced from wind. Despite this, and given the complex multivariate dependence of the power of wind turbines on environmental conditions and working parameters, the literature is lacking studies specifically devoted to a careful characterization of wind farm performance. In particular, in the literature, it is overlooked that there are several types of faults which have similar manifestations and that can be defined as static errors. This kind of error manifests as a static bias occurring from a certain time onward, which can affect the anemometer, the absolute or relative pitch of the blades, or the yaw system. Static or systematic errors typically do not cause the functional failure of the wind turbine system, but they deserve attention due to the fact that they cause power production loss throughout the operation time. Based on this, the first objective of the present study is a critical review of the recent papers devoted to three types of wind turbine static errors: anemometer bias, static yaw error, and pitch misalignment. As a result, a comprehensive viewpoint, enhancing the state of the art in the literature, is developed in this study. Given that the use of data collected by Supervisory Control And Data Acquisition (SCADA) systems has, up to now, been prevailing for the diagnosis of systematic errors compared to the use of further specific sensors, particular attention in the present study is thus devoted to the discussion of the phenomena which can be observable through SCADA data analysis. Based on this, finally, a rigorous work flow is formulated for detecting static errors and discriminating among them through SCADA data analysis. Nevertheless, methods based on additional information sources (like further sensors or meteorological data) are also discussed. An important aspect of this study is that, for each considered type of systematic error, some previously unpublished results based on real-world SCADA data are reported in order to corroborate the proposed framework. Summarizing, then, the present is the first paper which considers and discusses several types of wind turbine static errors in a unified viewpoint, correctly interprets apparently controversial results collected in the literature, and finally provides guidelines for the diagnosis of this kind of error and for the quantification of the performance drop associated with their presence.

The transportation is the sector of human activities which contributes the most to greenhouse gas emissions by far

The energy storage systems of grid-connected electric buses (eBuses) equipped with Vehicle-To-Grid technology are a valuable source of flexibility that the power system operator can manage to mitigate the impacts of grid contingencies and the randomness of renewable power generators. In this context, many market schemes have been recently deployed to enable these flexibility sources to offer ancillary grid services in the electricity markets, and a large number of optimization methods have been proposed for flexibility resource allocation, and for defining strategic bidding strategies. However, after the ancillary service market settlement, the eBuses owner is compelled to adapt the charging/discharging profiles of all the grid-connected eBuses according to the system operator’s request. In this paper, this scheduling problem is formalized by a multi-temporal mixed-integer programming problem, which aims at maximizing the welfare of all the grid-connected eBuses. The insight is to define proper utility functions describing the cost/benefit of the charge/discharge power profiles in function of the eBus parameters and its state of charge and to propose proper linearization techniques enabling the prompt solution of the welfare maximization problem by conventional mixed integer linear programming solvers. Detailed simulation results obtained on realistic case studies are presented and discussed to prove the effectiveness of the proposed methodology.