Antonio Piccolo

Evaluating the Impact of Network Investment Deferral on Distributed Generation Expansion

Distributed generation (DG) can offer an alternative planning approach to utilities to satisfy demand growth and distribution network security, planning and management issues. However, an appropriate framework is required to foster the integration of DG within grid network planning, thus avoiding potential inefficiencies in electricity supply infrastructure. In this work, in order to capture the effects of network investment deferral on DG expansion, different regulations for distribution network operators (DNOs) ownership of DG and how they influence the optimal connection of new generation within existing networks are examined. Using a multiyear multiperiod optimal power flow, DNOs preference for the siting and sizing of DG installation are analyzed.

Smart Operation of Wind Turbines and Diesel Generators According to Economic Criteria

This paper proposes an innovative system for Smart Grid (SG) management aiming at minimizing the total costs supported for carrying out the delivery of energy to consumers. These costs include the production costs of distributed generators, the cost of the power provided by the primary substation, and the cost associated with grid power losses. After a brief overview on the main SG aspects, this paper describes the proposed approach that makes use of an optimal power flow algorithm and the active management schemes. The efficiency of the method is verified on a distribution system comprising wind turbines and diesel generators, considering the time-varying characteristics of the load demand and wind power generation.

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

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.

Failure Identification in Smart Grids Based on Petri Net Modeling

This paper presents a method to identify and localize failures in smart grids. The method is based on a carefully designed Petri net (PN) that captures the modeling details of the protection system of the distribution network and allows the detection/identification of failures in data transmission and faults in the distribution network by means of simple matrix operations. The design of the PN model is carried out by carefully composing multiple PN models for single protection systems: Such an approach allows the identification of the faults despite possible strong penetration of distributed generation. In order to verify the method, two case studies are discussed. The results highlight that the proposed method can remove a lot of the complexity of the associated data analysis despite the possible presence of malfunctioning protection systems and misinformation due to communication and other errors.

Optimal Decentralized Voltage Control for Distribution Systems With Inverter-Based Distributed Generators

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.

Distributed Generation Capacity Evaluation Using Combined Genetic Algorithm and OPF

A range of techniques has been proposed to define the optimal locations and capacities of distributed generation (DG) as a means of ensuring that the maximum amount of DG can be connected to existing and future networks. However, there are limitations inherent in these methods, not least in finding the best combination of sites for connecting a predefined number of DGs. Here, a method combining optimal power flow and genetic algorithms aims to meet this requirement. Its use would be in enabling Distribution Network Operators to search a network for the best sites and capacities available to strategically connect a defined number of DGs among a large number of potential combinations. Some applications of the proposed methodology confirmed its effectiveness in sitting and sizing an assigned number of DG units.

An overview on the smart grid concept

Smart Grid is a concept for transforming the electric power grid by using advanced automatic control and communications techniques and other forms of information technology. It integrates innovative tools and technologies from generation, transmission and distribution all the way to consumer appliances and equipment. This concept integrates energy infrastructure, processes, devices, information and markets into a coordinated and collaborative process that allows energy to be generated, distributed and consumed more effectively and efficiently. This paper reviews some researches and studies on Smart Grids (SGs) technology.

Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms

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.

Maximizing DG penetration in distribution networks by means of GA based reconfiguration

The distributed generation (DG) capacity will increase considerably in the future distribution networks, involving more and more complex control systems able to integrate DG supervision and control into network control schemes. This paper proposes a reconfiguration methodology based on a genetic algorithm (GA), that aims at achieving the maximum DG penetration, while observing thermal and voltage constraints. The proposed methodology has been tested on a 33-bus system with DG units. Simulation results demonstrated its effectiveness in increasing both individual and overall DG penetration allowing to exploit network capability. The methodology can assist distribution system operators (DSOs) in planning and managing DG connections and in maximizing the total GD penetration and renewable sources exploitation

Designing and testing decision support and energy management systems for smart homes

Most advantages that the smart grid will bring derive from its capability of improving reliability performance and customers’ responsiveness and encouraging greater efficiency decisions by the costumers. Demand side management is, therefore, considered as an integral part of the smart grid and one of the most important methods of energy saving. Accordingly, an innovative decision support and energy management system (DSEMS) for residential applications is proposed in this paper. The DSEMS is represented as a finite state machine and consists of a series of scenarios that may be selected according to the user preferences. The designing and testing methods are described and some simulations results are presented in order to verify its effectiveness both in terms of continuity of electricity supply and energy savings and economics.