The increasing penetration of distributed generation (DG) power plants into distribution networks (DNs) causes various issues concerning, e.g., stability, protection equipment, and voltage regulation. Thus, the necessity to develop proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards (PQR) has become a relevant issue in recent years. This paper proposes an optimized distributed control approach based on DN sensitivity analysis and on decentralized reactive/active power regulation capable of maintaining voltage levels within regulatory limits and to offer ancillary services to the DN, such as voltage regulation. At the same time, it tries to minimize DN active power losses and the reactive power exchanged with the DN by the DG units. The validation of the proposed control technique has been conducted through a several number of simulations on a real MV Italian distribution system.

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.

The expected impact of distributed generation (DG) into Smart Grid represents a great challenge of the future for power systems. In particular, the integration of DG based on renewable energy sources (RESs) in distribution networks, without compromising the integrity of the grid, requires the development of proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards. This paper proposes a coordinated local control approach that allows distribution system operator (DSO) and independent power producers (IPPs) to obtain benefits offering the voltage regulation ancillary service to DSO and maximizing allowable active power production for each RES unit belonging to the same IPP. The control is based on a cooperation of data transfer between DSO and IPPs. In order to realize such cooperation, a nonlinear constrained optimization problem is formulated and solved by sequential quadratic programming (SQP) method. The validation of the proposed control technique has been conducted through several time series simulations on a real MV Italian distribution system.

Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can offer a sensible improvement of the overall vehicle environmental impact achieving at the same time a rational energy employment. The main task of an energy flow management unit is to split the instantaneous vehicle power demand between the internal combustion engine and the electric motor ensuring that the power sources are operated at high efficiency operating points and the related vehicle emissions are minimised. This paper presents an original methodology for the tuning of the characteristic parameters. The proposed methodology identifies, using the genetic algorithm, the value of the energy flow management parameters that minimize the cost function in terms of fuel consumption and emissions. Some interesting simulation results are discussed to prove the validity of the methodology, which contributes to a substantial reduction of the pollutant emissions from hybrid electric vehicles.