One of the most recent topics in variable-structure systems theory is represented by the second-order sliding-mode control (2-SMC) methodology. This approach guarantees the same robustness and dynamic performance of traditional first-order SMC algorithms, and, at the same time, attenuates the chattering phenomenon, which is the main drawback in the actual implementation of this technique. In the present paper, a recently-proposed 2-SMC algorithm is used to synthesize a robust dc-drive control system which does not require current feedback and demands only rough information about the actual motor parameters. Stability and performance are analyzed, and an experimental comparison with a proportional-integral-based control scheme is reported.

An optimal energy storage system (ESS) management procedure devoted to full renewable energy sources (RESs) exploitation is presented in this paper. It consists of an appropriate scheduling procedure and a real-time control strategy, which both aim to increase the RES penetration level as much as possible. In particular, the one-day-ahead scheduling procedure synthesizes the combined RES-ESS energy production profile with the aim of minimizing the RES energy production curtailments by means of ESS energy buffering. The real-time control strategy is developed in order to track the scheduled profile as well as possible by mitigating forecasting errors, thus improving RES reliability. The worth and effectiveness of the proposed management procedure is verified through a wide simulation study, which is carried out by means of the Matlab software package.

Renewable energy sources will play an increasingly central role in the power network of the future. The will to reduce the dependence on fossil sources as well as growing environmental awareness, both individually and governmentally, is forcing governments throughout the world to invest an enormous amount of capital in renewable energy sources. The European Union, through its Energy and Climate Policy, has fixed an ambitious target: to provide, 20% of electricity from renewable sources by 2020. The additional volatility introduced by renewable energy source to the grid can be compensated by energy storage systems as well as by demand control measurements and by the reinforcement of the power grid. These solutions can improve the system reliability of the electric grid and the efficiency of the energy supply and give more flexibility to the system. This study analyzes the possibilities to use electric vehicles connected to the grid as energy storage system within a Virtual Power Plant structure. Through the formulation of an optimization problem the optimal number of electric vehicles has been evaluated.

A model predictive control algorithm (MPC) suitable for Surface-Mounted Permanent Magnet Synchronous Machines (SPMs) is presented in this paper. It is based upon an accurate discrete-time model of the drive, accounting for both voltage saturation and current limitation constraints. It aims to properly manage these constraints in order to guarantee a good exploitation of both transient and steady state SPM performances, especially over flux-weakening operation. The effectiveness of the proposed MPC is first validated by a simulation study, in which the sensitivity of the proposed MPC against un-compensated inverter un-idealities and parameter uncertainties is properly highlighted and discussed, as well as the effects of sudden load torque variations. Then, an experimental study is performed on a radial-flux SPM driven by a Field Programmable Gate Arrays (FPGA) control board. The performances achievable by the proposed MPC are compared with those obtained by a conventional PI-based control system in order to highlight the improvements, especially regarding transient and flux-weakening operation.

A novel optimal power and energy management (OPEM) for centralized hybrid energy storage systems (HESS) in microgrids is presented in this paper. The proposed OPEM aims at providing multiple grid services by suitably exploiting the different power/energy features of electrochemical batteries (B) and supercapacitors (S). The first part of the paper focuses on the design and analysis of the proposed OPEM, by highlighting the advantages of employing hand-designed solutions based on Pontryagin’s minimum principle rather than resorting to pre-defined optimization tools. Particularly, the B power profile is synthesized optimally over a given time horizon in order to provide both peak shaving and reduced grid energy buffering, while S is employed in order to compensate for short-term forecasting errors and to prevent B from handling sudden and high-frequency power fluctuations. Both the B and S power profiles are computed in real-time in order to benefit from more accurate forecasting, as well as to support each other. Then, the effectiveness of the proposed OPEM is tested through numerical simulations, which have been carried out based on real data from the German island of Borkum. Particularly, an extensive and detailed performance analysis is performed by comparing OPEM with a frequency-based management strategy (FBM) in order to highlight the superior performance achievable by the proposed OPEM in terms of both power and energy management and HESS exploitation.

The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources. This is fundamentally changing the configuration of energy management and is introducing new problems that are only partly understood. In particular, renewable energies introduce fluctuations which cause an increased request for conventional energy sources to balance energy requests at short notice. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and for the forecast of short time fluctuations related to renewable sources in order to estimate their effects on the electricity market. To account for the inter-dependencies in the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations in the power system and an agent based approach for the prediction of the market players’ behavior. Our model is data-driven; it builds on one-day-ahead real market transactions in order to train agents’ behaviour and allows us to deduce the market share of different energy sources. We benchmarked our approach on the Italian market, finding a good accordance with real data.