The smart grid concept enables demand-side management, including electric vehicles (EVs). Thus way, some ancillary services can be provided in order to improve the power system stability, reliability, and security. The high penetration level of renewable energy resources causes some problems to independent system operators, such as lack of primary reserve and active power balance problems. Nowadays, many countries are encouraging the use of EVs which provide a good chance to utilize them as a virtual power plant (VPP) in order to contribute to frequency event. This paper proposes a new control method to use EV as VPP for providing primary reserve in smart grids. The primary frequency reserve helps the power system operator to intercept the frequency decline and to improve the frequency response of the whole system. The proposed method calculates the electric vehicles’ primary reserve based on EVs’ information, such as the state of charge (SOC), the arriving time and the vehicle’s departure time. The effectiveness of the proposed scheme is verified by several simulation scenarios on a real-world modern power system with different generating units, such as conventional power plants, renewable energy resources, and electric vehicles.

In this technical note, cone-copositive piecewise quadratic Lyapunov functions (PWQ-LFs) for the stability analysis of conewise linear systems are proposed. The existence of a PWQ-LF is formulated as the feasibility of a cone-copositive programming problem which is represented in terms of linear matrix inequalities. A constructive procedure for its solution is provided. Examples show the effectiveness of the approach, also in the case of uncertain conewise linear systems.

In this paper the use of a wearable device is considered in order to evaluate the performance of an athlete during her/his sport activities. The preliminary step consists of recording the motion variables at a sufficiently high sampling rate throughout the experimental campaign. The collected data are then elaborated by a PC-based application to identify the system dynamics and derive some synthetic performance indicators, by taking into account also the experience of the sport professionals. The extraction of the indicators is based on basic signal processing that can be implemented in algorithms run directly on the microcontroller unit (MCU) of the device. The key indicators values can be sent to other electronic devices by using one of the available wireless network connections at a reduced transmission rate. Some experimental data are also reported to illustrate the effectiveness of the approach.

Heterogeneous bounded confidence opinion dynamics with an adaptation policy of the agents' confidence thresholds is considered. The strategy implemented by each agent consists of increasing his confidence thresholds when he has no active neighbor, thus strengthening his heterophilous propensity. Moreover, his adaptation algorithm is stopped if there is a minimal number of agents within his similarity interval. The heterogeneity of the model could lead to interesting scenarios, e.g. steady-state oscillatory behaviors and practical clustering. Sufficient conditions for reaching in finite time a practical consensus are proposed. An upper bound on the maximum number of steady-state practical clusters which depends on the parameters of the confidence thresholds adaptation algorithm is obtained. The steady-state behaviors in the presence of one-sided confidence thresholds and stubbornness are discussed. Numerical simulations verify the theoretical results.